{"reviewed":"2026-10-21","works":[{"id":"fr-vla","name":"FR-VLA","title":"FR-VLA: Implicit Force Reasoning VLA for Contact-Rich Manipulation","date":"2026-10-19","year":2026,"collections":["policy"],"categories":["tactile-il","generalist-foundation"],"resourceType":"policy","summary":"A vision-language force-admission signal gates separately encoded wrench information before action generation in contact-rich tasks.","institution":"Nanyang Technological University","venue":"CoRL 2026","modalities":["RGB","Force/Torque","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.25981"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The inspected primary sources do not establish a public implementation, model checkpoint or dataset download. Paper figures retain their original rights."},"evidence":[{"url":"https://arxiv.org/html/2610.25981","label":"Original full paper, methods and evaluation","fields":["title","date","institution","summary","modalities","policy","model","benchmark","benchmark.dataCollection","caveats"],"note":"Publication date is arXiv v1; implementation and deployment observations are distinguished from training supervision, generated geometry and base-model identity."}],"caveats":["Built-in UR six-axis F/T is not cutaneous tactile; camera hardware does not establish depth input.","Motion-phase labels are generated offline from demonstrations and are not an extra oracle input during deployment."],"verified":"2026-10-21","policy":{"family":"Vision-language-action","baseModel":"VLA-Adapter-style VLM/action backbone with force-query reasoning and frozen VQ-VAE wrench tokenizer.","modelSize":"Not reported","inputs":"Scene/wrist RGB, language, robot state and recent measured 6D wrench; vision-language features determine admission.","outputs":"Robot action chunks conditioned on force admission and external continuous/tokenized wrench.","trainingMethod":"Train force relevance/motion-phase reasoning plus gated action conditioning; tokenize wrench histories before VLA training.","trainingData":"70–90 demonstrations/task, collected with 3D mouse, five tasks across UR5e/UR3e.","trainingTime":"Not reported","trainingHardware":"Training 2 RTX A6000; inference 1 RTX 3090.","controlRate":"Images collected10 Hz, force/state/actions50 Hz; streams aligned to images for training, not a measured VLM refresh rate.","evaluation":"Gear assembly, cable connection, surface cleaning, screw tightening and seat-belt insertion; five tasks/six evaluated stages with controlled baselines."}},{"id":"handson","name":"HandsOn","title":"HandsOn: A Large-Scale Simulation Benchmark for Generalizable Dexterous Manipulation","date":"2026-10-19","year":2026,"collections":["benchmark"],"categories":["dexterous-benchmarks"],"resourceType":"benchmark","summary":"A dexterous simulation platform pairs 50 demonstrated tasks with 16 demonstration-free transfer tasks and execution diagnostics.","institution":"Tsinghua University","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Bimanual","Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2610.26765","project":"https://llltttwww.github.io/HandsOn-website/","code":"https://github.com/TsinghuaC3I/HandsOn","data":"https://huggingface.co/datasets/Cyan666/HandsOn-Datasets"},"release":{"code":"Available —66-task PhysX implementation","weights":"Not applicable — benchmark; policy-specific checkpoints retain separate scope","data":"Available —PhysX demonstrations; Newton data not released","license":"MIT original benchmark code and published dataset card; bundled robot/scene/policy assets retain component-specific licenses.","notes":"Actual native simulation/evaluation code,66 configs andHFdata/parquet/video inventories are public. OptionalNewton evaluation currentlycovers2 tasks, not a complete alternate 66-task release."},"evidence":[{"url":"https://arxiv.org/html/2610.26765","label":"Original full paper, methods and evaluation","fields":["title","date","institution","summary","modalities","policy","model","benchmark","benchmark.dataCollection","caveats"],"note":"Publication date is arXiv v1; implementation and deployment observations are distinguished from training supervision, generated geometry and base-model identity."},{"url":"https://github.com/TsinghuaC3I/HandsOn","label":"Official artifact: actual source files, inventory and license","fields":["links","release"],"note":"7755real files,66 actual config entries and native runner; not merely a website. HF72058 entries include actual trajectory/video data."},{"url":"https://huggingface.co/datasets/Cyan666/HandsOn-Datasets","label":"Official artifact inventory / model or dataset card","fields":["links","release"],"note":"Actual file inventory and artifact-specific terms checked independently; upstream checkpoints and equivalent dataset layouts are not double-counted."}],"caveats":["Core-50 andTransfer-16 have different demonstration availability. Simulator platform portability across 5 arms / 6 hands is not evidence that every policy was evaluated on every combination.","Physical rollouts are qualitative transfer illustrations; benchmark scores are simulation results.","GPT-6 move_eef uses palm/finger targets and kinematic conversion, not a learned grasp primitive or the exact gripper-only RoboProbe interface.","The source also implements metric-depth capture to float32 NPZ with separate normalized 8-bit video previews; this optional capture feature does not imply that each baseline policy uses measured depth."],"verified":"2026-10-21","benchmark":{"simulator":"NVIDIA Isaac Sim / Isaac Lab with PhysX. Optional Newton evaluator currently covers two tasks.","simulatorVersion":"Default Isaac Sim 5.1.0.0 / Isaac Lab v2.3.2; Newton uses a separate Python 3.12 / Isaac Sim 6 environment.","taskCount":"66: Core-50 + Transfer-16","tasks":["Align blocks in midair","Connect charger and cable","Cover box","Cover pitcher","Fill cup","Fit device cover","Flip sponge","Hammer nail","Insert peg","Lift carton","Line up blocks","Load and start microwave","Load cans","Move and click mouse","Open drawers","Open laptop","Open latched door","Open micropipette box","Orient cup upright","Pass and orient pen","Pass block","Pass screwdriver","Photograph logo","Place ball in bowl","Place cup on plate","Place fruit in bucket","Place largest fitting ball","Place tableware","Place wine glass","Pour seasonings","Prepare coffee","Press soap pump","Reposition marker grasp","Roll ball through gate","Rotate propeller","Scoop marbles","Scrub bottle","Set dual knobs","Set timer","Slide knife blade","Sort nested cups","Sort objects","Squeeze scissors","Stack blocks","Stir pot","Store charger","Trigger drill on block","Unfold fan","Unfold ruler","Water plant","Cover pitcher and box","Open laptop and click mouse","Pass block and close box","Place fruit and photograph logo","Scrub bottle then fill cup","Season and stir","Cover lunchbox","Open trash bin","Orient bottle upright","Pass glue stick","Set mixer knobs","Spray shoe","Stack boxes","Compress sponge into mug","Fold towel twice","Pack pillow"],"taskListScope":"Complete 66 released configurations: 50 demonstrated tasks; 16 transfer tasks (6 compositional, 7 generalization, 3 deformable).","capabilities":["Dexterous tool use","Bimanual coordination","Numerical/spatial grounding","Transfer","Deformable manipulation"],"metrics":"Task success, ordered progress, contact/action/goal diagnostics and uncertainty; separateCore-50/Transfer-16.","robots":"Paper ROKAE–Wuji bimanual platform. Other arm/hand descriptions establish portability support, not all-combination evaluation.","sensors":"Three RGB views and measured robot joint/end-effector state; operator/setup camera not a policy input.","dataCollection":"Curated human teleoperation with dexterous hand retargeting, success/feasibility verification and high-fidelity re-rendering.","data":"18,000 curated PhysX demonstrations / 6,053,555 timesteps at 30 Hz. Newton demonstration data remain planned.","protocol":"Native EEF52 executor at 30 Hz; task-specific 3×P75 physical-time budgets with three deformable exceptions. GPT-6: ten trials per task, 660 total, simulation paused during API inference.","baselines":"Diffusion, VLA, WAM and GPT-6 move_eef. Best trained multi-task OpenWAM: 29.1% Core-50. GPT-6: 5.2% Core-50 / 1.9% Transfer-16."}},{"id":"ropedia-w0","name":"Ropedia W0","title":"Ropedia W0: Scaling Loco-Manipulation World Models across Embodiments with Human Experience","date":"2026-10-19","year":2026,"collections":["policy"],"categories":["geometry-models","generalist-foundation"],"resourceType":"model","summary":"A cross-embodiment egocentric world model predicts visual consequences from shared camera-pose and projected-skeleton conditions.","institution":"Nanyang Technological University","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Human interaction recordings","Real-world recordings"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2610.26855","project":"https://ropedia.github.io/ropedia-w0/","code":"https://github.com/Ropedia/ropedia-w0","weights":"https://huggingface.co/ropedia-ai/Ropedia-W0","data":"https://huggingface.co/datasets/ropedia-ai/Xp-Teleop"},"release":{"code":"Partial —5B inference","weights":"Partial —5B pretrained and midtrained safetensors","data":"Partial —Xp-Teleop; not the full 13K-hour mixed corpus","license":"Apache-2.0 code; CC BY-NC 4.0 model weights; CC BY 4.0 Xp-Teleop. Upstream model/data retain separate terms.","notes":"Actual 5B inference modules, two safetensor files and Xp-Teleop data are public. A14B, benchmark-finetuned checkpoints, post-training/evaluation code and benchmark data remain planned."},"evidence":[{"url":"https://arxiv.org/html/2610.26855","label":"Original full paper, methods and evaluation","fields":["title","date","institution","summary","modalities","policy","model","benchmark","benchmark.dataCollection","caveats"],"note":"Publication date is arXiv v1; implementation and deployment observations are distinguished from training supervision, generated geometry and base-model identity."},{"url":"https://github.com/Ropedia/ropedia-w0","label":"Official artifact: actual source files, inventory and license","fields":["links","release"],"note":"144 real files and Apache LICENSE; two HF .safetensors, model-specific CC BY-NC license and 4,354 Xp-Teleop entries inspected."},{"url":"https://huggingface.co/ropedia-ai/Ropedia-W0","label":"Official artifact inventory / model or dataset card","fields":["links","release"],"note":"Actual file inventory and artifact-specific terms checked independently; upstream checkpoints and equivalent dataset layouts are not double-counted."},{"url":"https://huggingface.co/datasets/ropedia-ai/Xp-Teleop","label":"Official artifact inventory / model or dataset card","fields":["links","release"],"note":"Actual file inventory and artifact-specific terms checked independently; upstream checkpoints and equivalent dataset layouts are not double-counted."}],"caveats":["The released world model generates video conditioned on kinematics, not direct motor actions. Paper downstream planning/WAM applications do not establish release of those policies.","Camera poses, skeleton projections and metricized annotations are not a measured depth image input.","10K human +3K robot hours are a mixed training corpus; the released Xp-Teleop dataset is a distinct smaller contribution."],"verified":"2026-10-21","model":{"role":"Kinematics-conditioned egocentric video world model, separate from downstream action-policy demonstrations.","family":"Video world model","baseModel":"Wan2.2-TI2V-5B and A14B configurations; shared camera and skeleton conditioning.","modelSize":"5B and A14B; current public checkpoints 5B only.","inputs":"Initial RGB, text, camera intrinsics/extrinsics and projected human/robot skeletons; optional wide-FOV/wrist conditions.","outputs":"Predicted egocentric video under the supplied motion conditions.","trainingMethod":"Human pretraining, robot/human midtraining and separate benchmark post-training; causal distillation for interactive streaming.","trainingData":"Xp-Data:≈10K human +3K robot hours; Xp-Teleop dexterous humanoid teleoperation held-out benchmark.","trainingTime":"Not reported","trainingHardware":"Release 5B inference verified by authors on H200; ≥40GBGPU,≈31GBpeak for 640×480/81 frames.","inference":"Released diffusion clip generation and separate distilled-streaming application; video 30 fps is a recording/output rate.","evaluation":"Five robot video benchmarks, held-out human clips and separately scoped planning/policy-evaluation applications."}},{"id":"tactalent","name":"TacTalent","title":"GPT-6 Astra Is a Native Visuo-Tactile Policy for Contact-Rich Manipulation","date":"2026-10-19","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","tactile-il"],"resourceType":"policy","summary":"A frozen GPT-6 Astra directly interprets scene and tactile images through RoboICL, using demonstration context and closed-loop contact feedback without task-specific weight training.","institution":"Tsinghua University","venue":"Preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.26846","project":"https://tactalent.github.io/","code":"https://github.com/ccyydd/TacTalent","rollouts":"https://tactalent.github.io/records.html?lang=en"},"release":{"code":"Available","weights":"Not applicable","data":"Partial — public rollout records/videos; benchmark training corpora are separate","license":"Apache-2.0 original adapters/evaluation; vendored RoboICL MIT and benchmark assets retain their own terms.","notes":"Actual src/tactalent benchmark adapters, RoboICL integration, evaluation and demonstration-preparation code are public. This is not the older SimpleTouch implementation linked by one stale README badge. Hosted-model access is external."},"evidence":[{"url":"https://arxiv.org/html/2610.26846","label":"Original full paper, methods and evaluation","fields":["title","date","institution","summary","modalities","policy","model","benchmark","benchmark.dataCollection","caveats"],"note":"Publication date is arXiv v1; implementation and deployment observations are distinguished from training supervision, generated geometry and base-model identity."},{"url":"https://github.com/ccyydd/TacTalent","label":"Official artifact: actual source files, inventory and license","fields":["links","release"],"note":"135 actual files; src/tactalent/roboicl, benchmark adapters and evaluation scripts; root Apache-2.0 LICENSE and vendored runtime reviewed."}],"caveats":["All reported TacTalent trials are simulated; OpenViTac’s paired sim/real benchmark does not turn these into physical TacTalent experiments.","Astra uses a 10% seed-42 stratified sample: 80 UniVTAC and 130 OpenViTac episodes per input mode. Baselines use their separately published benchmark protocols.","V+T and V mean task success is 93.8% / 76.3% on UniVTAC and 81.8% / 80.6% on OpenViTac. These are task averages, not pooled success or full benchmark sweeps.","A report-page date of October 17 is separate from the first arXiv submission on October 19."],"verified":"2026-10-21","policy":{"family":"Robot agent","baseModel":"Frozen hosted GPT-6 Astra with pinned RoboICL runtime","modelSize":"Not reported","inputs":"Scene RGB, optional tactile RGB images, task text, robot state, few-shot demonstration blocks and recent observation/action receipts. UniVTAC camera selection is task-specific; OpenViTac uses fixed and wrist views.","outputs":"Relative end-effector translation/rotation and gripper commands, executed by benchmark-native interfaces.","trainingMethod":"Frozen model weights; tactile images use the existing visual interface, without a learned tactile tokenizer or target-task fine-tuning.","trainingData":"Benchmark training demonstrations form context; evaluation is held out by the reported seed-selection protocol.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","evaluation":"8 UniVTAC and 11 OpenViTac tasks, 210 sampled episodes per input mode. Fragile-chip scoring adaptation and exclusions retain the documented protocol."},"relatedIds":["roboicl","simpletouch","univtac","openvitac"]},{"id":"tactile-hand-morphology","name":"Tactile rotation — hand morphology","title":"Tactile In-Hand Object Rotation: A Comparative Study of Hand Morphology in Simulation and Hardware","date":"2026-10-19","year":2026,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"A shared teacher–student RL protocol compares continuous rotation and contact-load distribution on anthropomorphic and symmetric TacTip-equipped LEAP hands.","institution":"University of Bristol","venue":"Preprint","modalities":["Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2610.26759"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The inspected primary sources do not establish a public implementation, model checkpoint or dataset download. Paper figures retain their original rights."},"evidence":[{"url":"https://arxiv.org/html/2610.26759","label":"Original full paper, methods and evaluation","fields":["title","date","institution","summary","modalities","policy","model","benchmark","benchmark.dataCollection","caveats"],"note":"Publication date is arXiv v1; implementation and deployment observations are distinguished from training supervision, generated geometry and base-model identity."}],"caveats":["Privileged simulator object/hand information is teacher-only; student estimates its latent from a 30-step deployable history.","Hardware tactile channels are contact-pose and force estimates from TacTip deformation images, not external scene vision.","Full/binary/proprioception comparison is simulated; hardware evaluates full-tactile policies on ten objects."],"verified":"2026-10-21","policy":{"family":"Reinforcement learning","baseModel":"AnyRotate-style PPO teacher, history-TCN latent student and TacTip contact estimators.","modelSize":"Not reported","inputs":"Joint positions, previous commands, rotation axis and optional fingertip contact/pose/force features.","outputs":"16 joint-target increments per LEAP hand, filtered and tracked with PD control.","trainingMethod":"PPO privileged teacher with curricula/domain randomization; student latent/action distillation on its own rollouts.","trainingData":"Cuboid/ellipsoid simulation objects, system-identification joint traces and TacTip contact calibration.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Policy20 Hz; simulation timestep 1/60 s.","evaluation":"12 unseen simulation objects across 3 observation conditions;10 hardware objects with varied orientations and contact/load analysis."}},{"id":"bidex100","name":"BiDex-100","title":"BiDex-100: A Comprehensive Benchmark for Bimanual Dexterous Manipulation Across 100+ Simulation Tasks","date":"2026-10-18","year":2026,"collections":["benchmark"],"categories":["dexterous-benchmarks","robustness-evaluation"],"resourceType":"benchmark","summary":"A 106-task bimanual dexterous suite uses matched in-/out-of-domain variants to evaluate five capability axes across two embodiments.","institution":"Zhejiang University","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Bimanual","Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2610.25459","project":"https://BiDex-100.github.io/BiDex-100/","code":"https://github.com/robbyant-research/BiDex-100","data":"https://huggingface.co/datasets/Robbyant-Research/BiDex-100"},"release":{"code":"Available","weights":"Not applicable — benchmark; baseline weight inventory separate","data":"Available —8,080 trajectories and assets","license":"MIT original benchmark code; CC BY-NC 4.0 trajectory/metadata and original assets. Third-party assets retain their own terms.","notes":"106non-OODYAMLtasks and matched variants are public. HF LeRobot v2.1/v3.0 directories are two representations of the same demonstrations, not two datasets."},"evidence":[{"url":"https://arxiv.org/html/2610.25459","label":"Original full paper, methods and evaluation","fields":["title","date","institution","summary","modalities","policy","model","benchmark","benchmark.dataCollection","caveats"],"note":"Publication date is arXiv v1; implementation and deployment observations are distinguished from training supervision, generated geometry and base-model identity."},{"url":"https://github.com/robbyant-research/BiDex-100","label":"Official artifact: actual source files, inventory and license","fields":["links","release"],"note":"503 files,106canonicalYAMLstems andactualbenchmarkrunner;HF58421 entries include MP4 / parquet / NPY / assets andseparateCCBYNClicense."},{"url":"https://huggingface.co/datasets/Robbyant-Research/BiDex-100","label":"Official artifact inventory / model or dataset card","fields":["links","release"],"note":"Actual file inventory and artifact-specific terms checked independently; upstream checkpoints and equivalent dataset layouts are not double-counted."}],"caveats":["RGB-D is a benchmark observation/data interface; each evaluated VLA must retain its actual RGB/depth input choice.","Rigid-body simulation does not establish real tactile sensing, fluids/deformables or physical policy transfer.","Datasetcard genericother tag is resolved by actualdatasets/lerobotv30/LICENSE:CC BY-NC 4.0."],"verified":"2026-10-21","benchmark":{"simulator":"MuJoCo","simulatorVersion":"Not reported","taskCount":"106 canonical specifications across five capability axes","tasks":["Cook egg in frypan","Drop two spoons from tray into two cups","Grill steak and corn","Hang mugs on tree","Heat sandwich bread in microwave","Insert spatula and rolling pin into glass cup","Insert two cubes into slot","Load three canned food onto dish rack","Make cereal in bowl","Make espresso","Measure two paprika shakers on digital scale","Nest medium box in large box","Organize items in desk caddy","Pick up peach","Pick up pear","Pick up spoon from tray into cup","Pick up tangerine","Place cake tongs and mug on tiered shelf","Place coffee mug and spoon on tray","Place condiments on tray","Prepare salad in bowl","Prepare tea with cup","Put apple and orange in bowl","Put apple in white bowl","Put hotdog bun on tray","Put marshmallow and cookie dough ball in bowl","Put measuring cup and ice into glass","Put mustard and honey bottle on tray","Put onion on cutting board","Put peach in fruit bowl","Put peeler and pizza cutter in drainer","Put sweet potato and onion in bowl","Put three apples into fruit bowl","Rethread bagel and donut onto middle rod","Stack three canned food","Stack yellow book on black book","Stand two wine bottles upright","Store condiments in short cabinet","Store snacks in slide cabinet","Take milk from microwave","Hammer one nail each hand","Hammer two nails each hand","Open bottle and drop one sugar cube in bottle","Open bottle and drop two sugar cubes in bottle","Pack four mangoes into baskets","Pack two mangoes into two baskets","Put jam and lemon from tray onto shelf","Put lemon on shelf then take jam into tray","Put orange on shelf then take back into tray","Take jam and lemon from shelf into tray","Take jam into tray and put milk on shelf","Take jam into tray then put back on shelf","Take milk and lemon from shelf into tray","Take milk and orange from shelf into tray","Take teapot into tray and put orange on shelf","Take teapot into tray then put back on shelf","Thread bagel and donut onto middle and side rods","Thread bagel and donut onto side rods","Water one plant each hand","Water two plants each hand","Arrange bar on tray and cake on plate","Arrange three bagels in oven tray","Assemble arch bridge","Drain three tomatoes in strainer","Flip open bottle cap","Insert items into shelf","Open bread maker lid","Open flatbed scanner lid","Open microwave oven door","Open rice cooker lid","Open wheelie bin lid","Pack three avocados into tiered basket","Pick up fork from tray into cup","Pick up whisk","Place two oranges on shelf tiers","Pull down slot machine lever","Pull open cabinet drawer","Pull open cash register drawer","Put apple and orange into basket","Put three eggs into tupperware","Raise laptop stand platform","Rotate two cubes around plates","Serve cheese on two cutting boards","Sort pepper and onion into two bowls","Store frozen food in freezer","Store items in white cabinet","Fill jar with snacks","Fill pot and bottle with sugar cubes and veg","Grill drumsticks fish and squash","Insert three plates into dish rack","Nest four bowls","Place strainer with tomatoes and spoon on dish rack","Recycle five cans into bin","Restock fridge with condiments and bottles","Sort mixed food into boxes","Stack sugar cubes","Drop fruits in basket then remove in order","Drop items into picnic basket then remove in order","Pour ice between two cups","Press button then grasp mug","Press red button then blue button","Swap candles between bowls","Swap cup colors with cube","Swap fruits between bowls","Uncover cubes in order","Uncover red cube"],"taskListScope":"Complete 106 released non-OOD YAML stems; matched out-of-domain variants are not counted as additional tasks.","capabilities":["Generalization","Instruction following","Limited demonstrations","Long horizon","Memory"],"metrics":"Tasksuccess andprogress under matchedID/OOD conditions; capability-specific aggregates.","robots":"Dual-Franka and Astribot bimanual configurations; 44 hand and 14 arm degrees of freedom.","sensors":"Head and bilateral-wrist RGB-D at 480×360 / 25 Hz; joint positions/velocities and end-effector poses. No skin tactile stream.","dataCollection":"Success-validated human teleoperation with exoskeleton gloves and Apple Vision Pro wrist/hand tracking.","data":"8,080 demonstrations / 6.25 million frames. LeRobot v2.1 and v3.0 are alternative representations of the same episodes.","protocol":"MuJoCo at 500 Hz, control at 25 Hz. Matched visual/spatial OOD changes and explicit model-specific training/evaluation budgets.","baselines":"Seven VLA families: π0.5, GR00T-N1.7, InternVLA-A1.5, Galaxea G0.5, DM0.5, LingBot-VLA-v2 and Xiaomi XR1."}},{"id":"trograsp2","name":"T(R,O) Grasp++","title":"T(R,O) Grasp++: Cross-Embodiment Dexterous Grasping in Cluttered Environments at Scale","date":"2026-10-18","year":2026,"collections":["policy"],"categories":["geometry-models","rgbd-3d"],"resourceType":"model","summary":"Graph diffusion synthesizes cross-hand cluttered-scene grasps from object, environment and robot geometry, paired with a large annotated grasp resource.","institution":"National University of Singapore","venue":"CoRL 2026","modalities":["Point cloud","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2610.25410","project":"https://nus-lins-lab.github.io/TROGrasp2Web/","code":"https://github.com/Barrybarry-Smith/TROGraspV2","data":"https://huggingface.co/datasets/Barrybarry-Smith/TROV2Dataset","weights":"https://drive.google.com/file/d/1mLTmTMilaS5hLyWo5OskxEn9P96CAVko/view?usp=sharing"},"release":{"code":"Available","weights":"Not verified — author-linked Drive checkpoint; no bundled method checkpoint in the inspected repository","data":"Available — actual train shards, validation and object assets","license":"Repository-wide source/checkpoint/data license not verified; third-party and original figure rights remain separate.","notes":"Training, inference and validation source is public. Actual default-branch tree has no ckpt/ directory despite README claims of bundled Wuji/test files; feature .pt files under robot assets are not method checkpoints. The separate author Drive checkpoint requires independent content verification. HF contains actual training/validation tar shards; no root LICENSE was found."},"evidence":[{"url":"https://arxiv.org/html/2610.25410","label":"Original full paper, methods and evaluation","fields":["title","date","institution","summary","modalities","policy","model","benchmark","benchmark.dataCollection","caveats"],"note":"Publication date is arXiv v1; implementation and deployment observations are distinguished from training supervision, generated geometry and base-model identity."},{"url":"https://github.com/Barrybarry-Smith/TROGraspV2","label":"Official artifact: actual source files, inventory and license","fields":["links","release"],"note":"Actual train.py, test.py and validation implementation plus HF tar inventories inspected. The current tree has no ckpt/ method files despite the README text; robot-feature tensors are not model checkpoints. A root source/data license is not established."},{"url":"https://huggingface.co/datasets/Barrybarry-Smith/TROV2Dataset","label":"Official artifact inventory / model or dataset card","fields":["links","release"],"note":"Actual file inventory and artifact-specific terms checked independently; upstream checkpoints and equivalent dataset layouts are not double-counted."}],"caveats":["Static grasp generation and lift/collision evaluation are not a long-horizon closed-loop action policy.","Approximately 0.5B annotations/50K objects/eight paper hands describe dataset scale, not independent executed robot trials.","The extra Wuji checkpoint is a release extension; preserve the paper’s eight-hand protocol."],"verified":"2026-10-21","model":{"role":"Cross-embodiment grasp synthesizer and geometric representation.","family":"Grasp synthesis","baseModel":"Separate PointNet++ VQ-VAE object/environment encoders and robot-object-environment graph diffusion.","modelSize":"Not reported","inputs":"Target-object and surrounding-environment point clouds, hand geometry/link features and robot configuration.","outputs":"Hand poses/joint configurations for task-agnostic cluttered-scene grasping.","trainingMethod":"Geometric encoder pretraining followed by graph diffusion and grasp validation.","trainingData":"Around 0.5B annotated grasps across 8 hands/~50K objects; released training shards and validation/object packages.","trainingTime":"Not reported","trainingHardware":"Not reported","inference":"Reported≈0.3s grasp synthesis latency; not robot servo frequency.","evaluation":"Paper 88% average simulated grasp success across 8 hands; real XHand/LEAP demonstrations."}},{"id":"contactbench","name":"ContactBench 1.0","title":"ContactBench 1.0: A Benchmark for Everyday Contact-Rich Robotic Manipulation","date":"2026-10-17","year":2026,"collections":["benchmark"],"categories":["tactile-benchmarks","precision-assembly"],"resourceType":"benchmark","summary":"A printable 36-task suite instantiates eight everyday contact skills with tunable physical/simulation mechanisms and evaluates learned policies on six physical tasks.","institution":"University of Illinois Urbana-Champaign","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.24512","project":"https://jingyi-xiang.github.io/contactbench"},"release":{"code":"Not released — project simulation-code button says coming soon","weights":"Not applicable","data":"Not released — asset-model download says coming soon","license":"Implementation/dataset/asset license not verified; project figures retain original rights.","notes":"The paper describes an open benchmark, but inspected project Code and Asset models links are placeholder#buttons explicitly labeled coming soon."},"evidence":[{"url":"https://arxiv.org/html/2610.24512","label":"Original full paper, methods and evaluation","fields":["title","date","institution","summary","modalities","policy","model","benchmark","benchmark.dataCollection","caveats"],"note":"Publication date is arXiv v1; implementation and deployment observations are distinguished from training supervision, generated geometry and base-model identity."}],"caveats":["36reference tasks/>40assets are suitecoverage; policycomparisonis sixphysicaltasks.","Poor evaluatedVTpolicyperformance on thisprotocol doesnot establishthat tactileinformation is universallyunhelpful.","Simulation fingertipimages/pressure/shear/netforces andphysicalGelSight channels are distinctsensorconfigurations."],"verified":"2026-10-21","benchmark":{"simulator":"PyBullet-linked rigid/constraint mechanism suite with custom tactile rendering","simulatorVersion":"Not reported","taskCount":"36 reference tasks; six physical policy-evaluation tasks","tasks":["Thread nut onto screw","Tighten screw with Allen key","Plug in charger","Turn doorknob to open door","Retrieve single utensil from bin","Pick up object from platform"],"taskListScope":"Selected named reference/evaluation examples from the paper; not the complete 36-task catalogue.","capabilities":["Small-object grasping","Multi-object grasping","Clutter grasping","Constrained placement","In-hand repositioning","Extrinsic contact control","Articulated objects","Insertion/alignment"],"metrics":"Task-specific success and contact-failure analysis under reproducible initial-pose randomization.","robots":"Single physical arm with GelSight Mini parallel gripper; fixtures stabilize printable task objects.","sensors":"Physical wrist RGB and GelSight Mini images/marker displacements. Simulated pressure, indentation, shear and net force are separate outputs.","dataCollection":"Physical demonstrations: custom handheld UMI gripper. Simulation: keyboard, gamepad and SpaceMouse interfaces with force feedback.","data":"Human demonstration training for six single-task policy comparisons. A public complete 36-task corpus is not established.","protocol":"Per-task π0.5 / FTP-1 fine-tuning and reactive diffusion comparisons. Randomized object poses in a 0.15 m × 0.15 m area, Velcro fixtures and rendered reset guidance.","baselines":"Vision π0.5 / diffusion and visual-tactile reactive diffusion / FTP-1 specialists; language capability is not the target comparison."}},{"id":"contactpolicy","name":"ContactPolicy","title":"Touch Where It Matters: Learning Local Tactile Policies for Fine-Grained Gripper Control","date":"2026-10-17","year":2026,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"A tactile–force local controller adjusts gripper width during contact while a visual base policy keeps moving the arm; an Intent policy controls handover.","institution":"University of Sydney","venue":"Preprint","modalities":["RGB","Tactile","Force/Torque","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2610.24485"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The inspected primary sources do not establish a public implementation, model checkpoint or dataset download. Paper figures retain their original rights."},"evidence":[{"url":"https://arxiv.org/html/2610.24485","label":"Original full paper, methods and evaluation","fields":["title","date","institution","summary","modalities","policy","model","benchmark","benchmark.dataCollection","caveats"],"note":"Publication date is arXiv v1; implementation and deployment observations are distinguished from training supervision, generated geometry and base-model identity."}],"caveats":["Local gripper control and visual whole-arm control have different input and update contracts.","Predicted tactile/force futures regularize training; deployment uses the contact encoder and width/modulation heads."],"verified":"2026-10-21","policy":{"family":"Hybrid","baseModel":"ACT visual arm checkpoint; ResNet-18 tactile encoder, force MLP, temporal GRU and action/modulation heads; ResNet-transformer Intent gate.","modelSize":"Not reported","inputs":"Contact controller: recent tactile images, fingertip force, arm/gripper state; Intent/base policy: scene RGB and robot state.","outputs":"Gripper width increments and retain/update decisions; separate base arm trajectory.","trainingMethod":"Imitation on contact-stage windows; auxiliary future tactile and force predictions; stage-wise learned ownership switches.","trainingData":"50 teleoperated demonstrations/task; same complete-episode partitions across methods.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Visual base and Intent30 Hz; local ContactPolicy60 Hz with 50 ms timestamp tolerance.","evaluation":"Physical container opening, object handling and tool-operation tasks; per-task success and arm-state/contact-input ablations."}},{"id":"gen2dex","name":"Gen2Dex","title":"Gen2Dex: Learning Dexterous Manipulation from Simulation-Initialized Generated Video Demonstrations","date":"2026-10-17","year":2026,"collections":["policy"],"categories":["rgbd-3d","visual-precision-rl"],"resourceType":"policy","summary":"Known simulated scene geometry anchors generated-video hand/object recovery, followed by residual RL that repairs retargeted dexterous trajectories.","institution":"University of Massachusetts Amherst","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2610.24392"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The inspected primary sources do not establish a public implementation, model checkpoint or dataset download. Paper figures retain their original rights."},"evidence":[{"url":"https://arxiv.org/html/2610.24392","label":"Original full paper, methods and evaluation","fields":["title","date","institution","summary","modalities","policy","model","benchmark","benchmark.dataCollection","caveats"],"note":"Publication date is arXiv v1; implementation and deployment observations are distinguished from training supervision, generated geometry and base-model identity."}],"caveats":["Known scene geometry, articulation, camera parameters and generated/estimated depth support reference extraction; this is not measured RGB-D deployment.","Physical Wuji+xArm demonstrations execute optimized trajectories open loop. Simulation residual control does not establish a feedback-driven real robot policy.","Generated video is not automatically a physically valid demonstration."],"verified":"2026-10-21","policy":{"family":"Reinforcement learning","baseModel":"Foundation-feature object tracker, hand-object alignment/retargeting and residual PPO with reset-state cache.","modelSize":"Not reported","inputs":"Generated/reference video and known scene for recovery; reference motion and robot/simulator state for residual control.","outputs":"Physically grounded dexterous robot demonstrations/optimized hand-arm trajectories.","trainingMethod":"Backward-curriculum residual PPO repairs retargeted references while preserving the video strategy; cache physically valid reset states.","trainingData":"Simulation-initialized generated videos and clean ARCTIC references.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","evaluation":"Genesis with Shadow/Wuji/Sharpa in separate settings; rigid/articulated tasks and open-loop Wuji+xArm physical transfer."}},{"id":"nise","name":"NISE","title":"Noise-Invariant Spatial Encoding for 3D Manipulation Policies under Depth Corruption","date":"2026-10-17","year":2026,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Cross-view geometric supervision and uncertainty-aware 3D attention make spatial action features more robust to missing or misleading depth measurements.","institution":"Singapore Institute of Manufacturing Technology (SIMTech), A*STAR","venue":"Preprint","modalities":["RGB","Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2610.24674","project":"https://nise-robotics.github.io/"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The inspected primary sources do not establish a public implementation, model checkpoint or dataset download. Paper figures retain their original rights."},"evidence":[{"url":"https://arxiv.org/html/2610.24674","label":"Original full paper, methods and evaluation","fields":["title","date","institution","summary","modalities","policy","model","benchmark","benchmark.dataCollection","caveats"],"note":"Publication date is arXiv v1; implementation and deployment observations are distinguished from training supervision, generated geometry and base-model identity."}],"caveats":["Calibrated multiview depth supervision is training-only; deployed attention retains uncertain measured geometry.","Backbone-matched controls distinguish NISE mechanisms from gains due to VGGT-Ω pretraining.","A depth-repair frontend is a separate component; model throughput is not low-level hardware control frequency."],"verified":"2026-10-21","policy":{"family":"Diffusion policy","baseModel":"VGGT-Ω visual geometry backbone, DPT spatial head and DDPM action expert; native 3DDA/RVT-2 ablations retain their own decoders.","modelSize":"Not reported","inputs":"Current RGB-D views, noisy lifted geometry/coordinate uncertainty and proprioception.","outputs":"Continuous action chunks through geometry-conditioned attention.","trainingMethod":"Train Geometric Cross-View Self-Supervision and Gaussian Marginalized 3D Attention with policy objective; source-depth validity/visibility masks prevent oracle geometry.","trainingData":"RLBench/MetaWorld demonstrations under clean or calibrated sensor-realistic corruption; physical transparent/reflective contact tasks.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","evaluation":"18-task RLBench clean/noisy protocols, MetaWorld and physical UR5e/dual UR3e; separate calibrated depth-repair comparisons."}},{"id":"worldscape-r2si","name":"WorldScape R²SI","title":"WorldScape R²SI (Recursive Robot Self-Improvement)","date":"2026-10-17","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A launch-reported two-level robot agent that revises reusable skills and can update a reactive WorldScape Policy component. Its identity, contributor and simulation results are confirmed by the official RoboDojo leaderboard; the method description has not been corroborated by a public technical report.","institution":"Manifold AI","venue":"Launch coverage / official leaderboard entry","modalities":[],"environments":["Simulation"],"embodiments":[],"links":{"leaderboard":"https://robodojo-benchmark.com/leaderboard","report":"https://mp.weixin.qq.com/s/GqI66EPiB9xz7Bz_LxwdmQ"},"policy":{"family":"Robot agent","baseModel":"Launch coverage describes inner and outer VLM agents plus an optional reactive WorldScape Policy (WSP) skill. VLM names, reactive checkpoint version and exact base models are not independently verified.","modelSize":"Not reported for this R²SI system; WorldScape Policy 2.0 component sizes are not inherited.","inputs":"Launch coverage reports multi-view RGB, robot state, task descriptions and episode memory. These inputs are not independently verified against an R²SI technical report or release.","outputs":"Launch coverage reports skill/tool execution and optional reactive WSP action chunks that return control to the inner agent. Native action dimensions and interfaces are not independently verified.","trainingData":"Launch coverage reports reuse of rollout records for skill revision and, for WSP-enabled tasks, reactive-model training. Dataset scale and the leaderboard checkpoint’s training corpus are not reported.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Launch coverage describes an inner Robot Agent executing task skills and an outer Recursion Agent revising skills, testing versions and optionally training WSP. The complete improvement loop and any meta-level self-modification are not independently verified.","evaluation":"Official RoboDojo-Sim snapshot 2026-10-17: average Score 46.58 and success rate 39.35%; Precision Score 23.51 and success rate 15.50%. The simulation entry is separate from the real-world leaderboard; this listing does not independently establish the launch article’s recursive-improvement claims."},"release":{"code":"Not verified — no R²SI-specific public implementation was located.","weights":"Not verified — no R²SI-specific checkpoint release was located.","data":"Not verified — no R²SI-specific public training or rollout dataset was located.","license":"Not verified for R²SI; WorldScape Policy 2.0 code and weight licenses do not establish this system’s release terms.","notes":"The official WorldScape Policy repository and Hugging Face model release are labeled 2.0. They are related earlier artifacts and are not linked as R²SI releases."},"evidence":[{"url":"https://robodojo-benchmark.com/data/leaderboard.generated.json","label":"Official RoboDojo-Sim leaderboard — 2026-10-17 snapshot","fields":["name","institution","environments","policy.evaluation"],"note":"The new row names WorldScape-R²SI, contributor Manifold AI, and per-dimension simulation metrics. The row supplies no paper, project, code, data, weights or implementation details. The snapshot date is not treated as a paper submission date."},{"url":"https://github.com/manifoldai-research/WorldScape-Policy","label":"Author release-scope audit — existing WorldScape Policy 2.0","fields":["release","caveats"],"note":"Current main README and published webpage branch identify WorldScape Policy 2.0. This is a related-release scope check, not R²SI code or proof that no private implementation exists."},{"url":"https://huggingface.co/manifoldai-research/WorldScape-Policy-2","label":"Author weight-release scope — WorldScape Policy 2.0","fields":["release.weights","release.license","caveats"],"note":"The public model card/checkpoints are labeled WorldScape Policy 2.0. No R²SI-specific or WorldScape Policy 2.5 checkpoint was established from this reviewed release."},{"url":"https://mp.weixin.qq.com/s/GqI66EPiB9xz7Bz_LxwdmQ","label":"Launch coverage — 机器之心, 2026-10-17 (secondary source)","fields":["title","date","summary","venue","policy.family","policy.baseModel","policy.inputs","policy.outputs","policy.trainingData","policy.trainingMethod","caveats"],"note":"Article read through a normal Chrome browser. This supports only explicitly attributed launch descriptions and the report-publication event. It is not primary technical evidence; no R²SI paper or author technical release was located.","sourceType":"secondary"}],"caveats":["The displayed date is the explicit news-publication event (2026-10-17), not a paper submission date; a public R²SI technical report has not been established.","Technical descriptions are explicitly attributed to launch coverage. The official leaderboard independently confirms the entry, contributor and reported metrics, not the internal architecture or improvement mechanism.","The launch article also mentions WorldScape Policy 2.5. No separate primary technical report or version-specific release was located; it is not treated as a renamed WorldScape Policy 2.0 checkpoint or a separately verified model here.","No measured depth or tactile input is established for R²SI. The source illustration labels RGB-only deployment, but primary implementation evidence remains unavailable.","R²SI is distinct from R²-WAM (arXiv:2610.04913), a separate repair-and-reject post-training method."],"relatedIds":["worldscape-policy2"],"verified":"2026-10-17"},{"id":"acts","name":"ACTS / React","title":"ACTS: Learning Visuo-Tactile World Models from Human Physical Interaction","date":"2026-10-16","year":2026,"collections":["policy"],"categories":["tactile-representation","geometry-models"],"resourceType":"model","summary":"An action-conditioned visuo-tactile world model learns human handheld-sensor contact dynamics and evaluates prediction transfer to robot teleoperation.","institution":"Massachusetts Institute of Technology","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Human interaction recordings","Real-world recordings"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2610.24285","project":"https://acts-anon.github.io/"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The inspected primary sources do not establish a public implementation, model checkpoint or dataset download. Paper figures retain their original rights."},"evidence":[{"url":"https://arxiv.org/html/2610.24285","label":"Original full paper, methods and evaluation","fields":["title","date","institution","summary","modalities","policy","model","benchmark","benchmark.dataCollection","caveats"],"note":"Publication date is arXiv v1; implementation and deployment observations are distinguished from training supervision, generated geometry and base-model identity."}],"caveats":["ACTS predicts observations given actions; it does not itself choose an executable robot action.","Normal force is estimated from tactile sensing and incorporated in sensor-action representation, not an independently measured wrist F/T input.","Robot transfer is inspected teleoperation prediction; no autonomous closed-loop task-success claim is established."],"verified":"2026-10-21","model":{"role":"Action-conditioned multimodal predictor, not an autonomous manipulation policy.","family":"Predictive contact model","baseModel":"Cosmos3-initialized generator with frozen Wan2.2 scene/wrist VAE and adapted tactile VAE.","modelSize":"Not reported","inputs":"Scene/wrist RGB, two GelSight image streams,6DoF sensor motion and tactile-derived normal-force changes.","outputs":"Future scene, wrist and tactile observations.","trainingMethod":"Blockwise teacher forcing and shared multimodal flow generation; train per object on human recordings and evaluate robot teleoperation without target fine-tuning.","trainingData":"React4.91 h across 3 objects, tracked handheld GelSight interactions; tactile encoder adaptation corpus separate.","trainingTime":"20 epochs per object.","trainingHardware":"4 NVIDIA H100 GPUs.","inference":"Short-horizon 8-frame prediction (0.53s) and long-rollout analyses;30 Hz recording alignment is not model latency.","evaluation":"Held-out human prediction and zero-shot robot teleoperation prediction; visual transfer remains mixed acrossPushT/Rope/Motherboard."}},{"id":"blind-precise-assembly","name":"Blind Precise Assembly","title":"Blind Precise Assembly: Proprioceptive Sim-to-Real Peg-in-Hole for Diverse Parts","date":"2026-10-16","year":2026,"collections":["policy"],"categories":["visual-precision-rl"],"resourceType":"policy","summary":"Uses joint history alone for contact-driven assembly; transfers a simulation-trained insertion policy to physical parts.","institution":"MIT","venue":"arXiv preprint","modalities":["Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.23849","project":"https://younghyopark.me/blind-proprioceptive-assembly/","code":"https://github.com/younghyopark/mjfit"},"release":{"code":"Partial — mjfit geometry generator","weights":"Not verified","data":"Not verified","license":"MIT for mjfit; policy artifacts not verified","notes":"Official mjfit repository contains procedural assembly generators, examples and benchmarks. This is a geometry/tool release, not evidence that policy training or checkpoints are released."},"evidence":[{"url":"https://arxiv.org/abs/2610.23849","label":"Canonical author manuscript and arXiv submission history","fields":["title","date","institution","venue"],"note":"Date retains the initial v1 submission, independently of revisions or current artifact releases."},{"url":"https://arxiv.org/html/2610.23849v1","label":"Full author paper: method, observations and evaluation scope","fields":["summary","policy","modalities","environments","embodiments","caveats"],"note":"Physical sensors, predicted signals, training labels and policy inputs are recorded separately; undisclosed quantities remain unknown."},{"url":"https://younghyopark.me/blind-proprioceptive-assembly/","label":"Official paper-linked project page; release review on 19 October 2026","fields":["links","release"],"note":"Current artifacts are checked separately from paper experiments. A project-only link is not proof of an implementation release."},{"url":"https://github.com/younghyopark/mjfit","label":"Official linked implementation and current file inventory","fields":["release","links"],"note":"Official mjfit repository contains procedural assembly generators, examples and benchmarks. This is a geometry/tool release, not evidence that policy training or checkpoints are released."}],"caveats":["Physical transfer is demonstrated for peg insertion; gear meshing and nut threading remain simulation evaluations.","The linked mjfit repository releases procedural collision geometry, not a verified trained assembly-policy checkpoint."],"verified":"2026-10-19","policy":{"family":"RL","baseModel":"Temporal-convolution encoder and MLP actor; asymmetric privileged critic during PPO training.","modelSize":"Not reported","inputs":"0.6-second joint-position/velocity, kinematic gripper pose/twist and previous-action history; no visual, wrench or tactile observations.","outputs":"Seven joint-position increments tracked by joint impedance control.","trainingData":"Procedurally randomized peg/hole, gear and thread assemblies in MuJoCo Warp; 12,288 parallel training worlds.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"50 Hz policy; 1 kHz simulated physics.","trainingMethod":"PPO with domain randomization, training-only privileged critic and contact supervision.","evaluation":"Peg insertion, gear meshing and nut threading in simulation; zero-shot physical peg insertion on Flexiv Rizon 4S/Robotiq 2F-85."}},{"id":"claro","name":"CLARO","title":"CLARO: Clutter Aware Robot Orchestrator for Open-Vocabulary Grasping using VLM Agents","date":"2026-10-16","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"A closed-loop VLM orchestrates scene survey, object localization, six-degree-of-freedom grasping and pushing to retrieve language-specified targets from clutter.","institution":"New York University","venue":"WACV 2027","modalities":["RGB","Depth","Point cloud","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.24016"},"release":{"code":"Not released","weights":"Not applicable","data":"Not released","license":"Not verified","notes":"The paper says CLARO code and simulation benchmark will be released; no official implementation/download was established from the inspected primary manuscript."},"evidence":[{"url":"https://arxiv.org/html/2610.24016","label":"Original full paper, methods and evaluation","fields":["title","date","institution","summary","modalities","policy","model","benchmark","benchmark.dataCollection","caveats"],"note":"Publication date is arXiv v1; implementation and deployment observations are distinguished from training supervision, generated geometry and base-model identity."}],"caveats":["The planner reasons over visual observations and tool feedback; depth lifts masks/pixels to metric object point clouds for manipulation skills. This is measured/simulated depth input, not monocular depth prediction.","Static referring-grasp image benchmarks, dynamic retrieval success and real-robot trials are separate settings."],"verified":"2026-10-21","policy":{"family":"Robot agent","baseModel":"Gemini 3.1 Pro planner plus perception/segmentation and grasp-generation tools; alternate planner ablations are separate.","modelSize":"Not reported","inputs":"Task, settled RGB-D observations, image-grounded scene graph, tool receipts and accumulated action/belief memory.","outputs":"Named skill calls yielding 6-DoF grasps and metric push waypoints; controller checks feasibility/collisions.","trainingMethod":"Training-free target-task orchestration of pretrained skills; execution feedback updates long-term memory and replans.","trainingData":"No task-specific training demonstrations.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","evaluation":"PyBullet/Blender UR5 cluttered retrieval, static RGS datasets and Franka with a fixed ZED2 RGB-D camera. Paper reports 89.1% dynamic simulation average and 88.8% real success."}},{"id":"defflow","name":"DefFlow","title":"DefFlow: Goal-Conditioned Deformable Object Manipulation using 3D Point Flows","date":"2026-10-16","year":2026,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Jointly predicts robot and deformable-object point flows for goal-conditioned manipulation.","institution":"Brown University","venue":"arXiv preprint","modalities":["Depth","Point cloud","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.22827","project":"https://def-flow.github.io/"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Paper and public presentation verified; no implementation, weight or data release is inferred from a publication or demonstration."},"evidence":[{"url":"https://arxiv.org/abs/2610.22827","label":"Canonical author manuscript and arXiv submission history","fields":["title","date","institution","venue"],"note":"Date retains the initial v1 submission, independently of revisions or current artifact releases."},{"url":"https://arxiv.org/html/2610.22827v1","label":"Full author paper: method, observations and evaluation scope","fields":["summary","policy","modalities","environments","embodiments","caveats"],"note":"Physical sensors, predicted signals, training labels and policy inputs are recorded separately; undisclosed quantities remain unknown."},{"url":"https://def-flow.github.io/","label":"Official paper-linked project page; release review on 19 October 2026","fields":["links","release"],"note":"Current artifacts are checked separately from paper experiments. A project-only link is not proof of an implementation release."}],"caveats":["Real deployment uses observed 3D point clouds; no tactile observation is claimed."],"verified":"2026-10-19","policy":{"family":"Transformer","baseModel":"Point Transformer V3 point-cloud UNet with learned type embeddings and an MLP flow head; inverse-kinematics action extraction.","modelSize":"Not reported","inputs":"Robot/scene point-cloud observation history and a target 3D deformable configuration; real observations from RealSense D455.","outputs":"Joint robot/object point flows converted into end-effector and gripper actions.","trainingData":"Unstructured play demonstrations relabeled with goal configurations for rope, granular media and cloth; training amounts differ by environment.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Imitation learning with joint robot/scene-flow supervision.","evaluation":"Simulated rope, granular-media and cloth manipulation; physical Franka Panda deployment on unseen goal configurations."}},{"id":"ego2contact","name":"Ego2Contact","title":"Ego2Contact: Predicting Touch from Egocentric Human Video for Contact-Rich Robot Manipulation","date":"2026-10-16","year":2026,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Transfers human visual-touch priors to robot tactile layouts for downstream control.","institution":"Carnegie Mellon University","venue":"arXiv preprint","modalities":["RGB"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2610.22932"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Paper and public presentation verified; no implementation, weight or data release is inferred from a publication or demonstration."},"evidence":[{"url":"https://arxiv.org/abs/2610.22932","label":"Canonical author manuscript and arXiv submission history","fields":["title","date","institution","venue"],"note":"Date retains the initial v1 submission, independently of revisions or current artifact releases."},{"url":"https://arxiv.org/html/2610.22932v1","label":"Full author paper: method, observations and evaluation scope","fields":["summary","model","modalities","environments","embodiments","caveats"],"note":"Physical sensors, predicted signals, training labels and policy inputs are recorded separately; undisclosed quantities remain unknown."}],"caveats":["Predicted pressure is inferred from RGB, not a physical tactile sensor stream.","In the real-robot study (§IV-E), predicted tactile annotations support policy post-training, but live rollouts receive measured glove tactile input. Hardware results do not establish sensor-free online predicted-touch control."],"verified":"2026-10-19","model":{"role":"Visual tactile-field predictor; supplies derived touch to separately trained robot policies.","family":"Visual-to-tactile prediction","baseModel":"Temporal scene context and hand-local visual features; anatomical mapping to a canonical tactile grid.","modelSize":"Not reported","inputs":"Egocentric RGB sequences and local hand-image features; paired tactile measurements supervise adaptation.","outputs":"Bimanual pressure/contact fields mapped to robot tactile layouts.","trainingMethod":"Human visual-tactile pretraining, robot-layout adaptation and downstream PPO / N0-VTLA post-training.","trainingData":"Paired human video/tactile data and robot video/tactile fine-tuning examples; policy comparisons use matched measured, predicted and zero-touch variants.","trainingTime":"Not reported","trainingHardware":"Not reported","inference":"Predicted touch is supplied online to simulation PPO and used offline to annotate real demonstration videos. Real N0-VTLA rollouts consume measured signals from an 880-taxel glove, not online predicted touch.","evaluation":"Four simulated contact-rich tasks and physical bottle-cap removal / test-tube transfer."}},{"id":"hexashell","name":"HexaShell / Preview","title":"HexaShell: A Thin Harness for Exploring the Limits of VLM Agents as Robot Coding Agents","date":"2026-10-16","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A coding agent drives a robot from calibrated RGB images and preserves failure-driven repairs as reusable Python tools; a separate repair agent improves tools while model weights stay frozen.","institution":"HexaFuture","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.23479","project":"https://github.com/HexaFuture/HexaShell","code":"https://github.com/HexaFuture/HexaShell","leaderboard":"https://robodojo-benchmark.com/leaderboard"},"release":{"code":"Partial — frozen tools, shared Open setup and interface contracts","weights":"Not applicable","data":"Partial — per-task result JSON/Markdown and recipes","license":"Apache-2.0 root code and separately attributed RoboProbe recipes. Hosted-model and simulator terms are external.","notes":"196 frozen tools for 34 non-Open tasks, shared Open tools and server import contracts are public. The executable server, agent container, executor and evolution loop are not included; hexashell/server/*.py are constants/helpers, not the complete harness."},"evidence":[{"url":"https://arxiv.org/html/2610.23479","label":"Original full paper, methods and evaluation","fields":["title","date","institution","summary","modalities","policy","model","benchmark","benchmark.dataCollection","caveats"],"note":"Publication date is arXiv v1; implementation and deployment observations are distinguished from training supervision, generated geometry and base-model identity."},{"url":"https://github.com/HexaFuture/HexaShell","label":"Official artifact: actual source files, inventory and license","fields":["links","release"],"note":"597 real files; frozen task tools, results and import contracts inspected; root LICENSE is Apache-2.0."}],"caveats":["No measured depth input: geometry comes from calibrated RGB ray triangulation, ray–plane intersections and active wrist views. A depth-network variant is an ablation.","Official held-out-seed evaluation and author new-layout/development retests have different protocols and Open tool configurations.","The official board’s precise 53.18 Score / 46.85% success differs from the README’s rounded 53.2 / 46.9.","No real-robot transfer is established; tools and API calls are not learned policy weights."],"verified":"2026-10-21","policy":{"family":"Robot agent","baseModel":"Codex with GPT-6-Astra for both execution and repair agents","modelSize":"Not reported","inputs":"Task instruction/recipe/notes, three calibrated RGB views, robot state and execution logs.","outputs":"robo CLI calls and generated Python perception/motion tools drive dual-arm ARX X5.","trainingMethod":"Same-layout failure review repairs code/tools; freeze all artifacts before evaluation. Eight Open tasks share one tool set without per-task notes.","trainingData":"Tools developed on ten seed-0 layouts per task; frozen official evaluation uses an independent hidden seed.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","evaluation":"Official RoboDojo: 42 tasks × 50 episodes; Generalization is 25 standard +25 cluttered. October 20 board: Score 53.18, success 46.85%. Author new-layout retest: Score 48.0, success 39.7%; not the same test."},"relatedIds":["roboprobe"]},{"id":"law","name":"LAW","title":"LAW: Unifying Language, Action, and World Modeling for Agentic Physical Intelligence","date":"2026-10-16","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","generalist-foundation"],"resourceType":"policy","summary":"A hybrid-expert VLM plans subtasks, assesses progress and values candidate WAM actions while dual-stream memory maintains the execution history.","institution":"Peking University","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.23334","project":"https://hey-cjj.github.io/LAW-project/","code":"https://github.com/hey-cjj/LAW","weights":"https://huggingface.co/KAITANG2003/LAW-OpenWAM","weights-fastwam":"https://huggingface.co/KAITANG2003/LAW-FastWAM","weights-vlm":"https://huggingface.co/KAITANG2003/LAW-VLM-OpenWAM","weights-vlm-fastwam":"https://huggingface.co/KAITANG2003/LAW-VLM-FastWAM","data":"https://huggingface.co/datasets/KAITANG2003/LAW-RoboTwin-WAM-Training","data-vlm":"https://huggingface.co/datasets/KAITANG2003/LAW-RoboTwin-VLM-Training"},"release":{"code":"Available — RoboTwin FastWAM/OpenWAM pipelines","weights":"Available — four WAM/VLM checkpoint inventories","data":"Partial — RoboTwin WAM data and Q1/Q2/Q3 annotation packages","license":"MIT original/root code; Apache-2.0 LAW-authored weights/adapters; CC BY 4.0 author annotations. Source videos, images, meshes and upstream assets retain separate rights.","notes":"Actual training/inference modules and four HF checkpoint repositories are public. HF annotation license tags do not relicense source videos or assets. Released RoboTwin pipelines do not establish all physical-robot and RoboDojo reproduction artifacts."},"evidence":[{"url":"https://arxiv.org/html/2610.23334","label":"Original full paper, methods and evaluation","fields":["title","date","institution","summary","modalities","policy","model","benchmark","benchmark.dataCollection","caveats"],"note":"Publication date is arXiv v1; implementation and deployment observations are distinguished from training supervision, generated geometry and base-model identity."},{"url":"https://github.com/hey-cjj/LAW","label":"Official artifact: actual source files, inventory and license","fields":["links","release"],"note":"1231 actual files and MIT root LICENSE; four HF model siblings and two dataset shard inventories inspected. Website’s old CogWAM code URL redirects to this work, distinct from HorizonRobotics/CogWAM."},{"url":"https://huggingface.co/KAITANG2003/LAW-OpenWAM","label":"Official artifact inventory / model or dataset card","fields":["links","release"],"note":"Actual file inventory and artifact-specific terms checked independently; upstream checkpoints and equivalent dataset layouts are not double-counted."},{"url":"https://huggingface.co/KAITANG2003/LAW-FastWAM","label":"Official artifact inventory / model or dataset card","fields":["links","release"],"note":"Actual file inventory and artifact-specific terms checked independently; upstream checkpoints and equivalent dataset layouts are not double-counted."},{"url":"https://huggingface.co/KAITANG2003/LAW-VLM-OpenWAM","label":"Official artifact inventory / model or dataset card","fields":["links","release"],"note":"Actual file inventory and artifact-specific terms checked independently; upstream checkpoints and equivalent dataset layouts are not double-counted."},{"url":"https://huggingface.co/KAITANG2003/LAW-VLM-FastWAM","label":"Official artifact inventory / model or dataset card","fields":["links","release"],"note":"Actual file inventory and artifact-specific terms checked independently; upstream checkpoints and equivalent dataset layouts are not double-counted."},{"url":"https://huggingface.co/datasets/KAITANG2003/LAW-RoboTwin-WAM-Training","label":"Official artifact inventory / model or dataset card","fields":["links","release"],"note":"Actual file inventory and artifact-specific terms checked independently; upstream checkpoints and equivalent dataset layouts are not double-counted."},{"url":"https://huggingface.co/datasets/KAITANG2003/LAW-RoboTwin-VLM-Training","label":"Official artifact inventory / model or dataset card","fields":["links","release"],"note":"Actual file inventory and artifact-specific terms checked independently; upstream checkpoints and equivalent dataset layouts are not double-counted."}],"caveats":["The 2B label is the VLM backbone, not the full combined WAM agent parameter total.","This agent trains planner/state/value LoRA experts and a WAM; it is not a wholly frozen zero-training harness.","Three RGB views are the physical observation contract; no measured depth or tactile deployment channel is claimed."],"verified":"2026-10-21","policy":{"family":"Hybrid","baseModel":"Qwen3-VL-2B-Instruct frozen base with planner/state/value LoRA; FastWAM or OpenWAM-α action backbone.","modelSize":"2B VLM backbone plus distinct WAM and adapters; combined total not reported.","inputs":"Task, three RGB views, robot state, short-term visual history and persistent long-term memory.","outputs":"Subtask plans, asynchronous state checks and value-ranked/fused continuous WAM action chunks.","trainingMethod":"Train subtask planner/state/value experts, MoTE video-action WAM and subtask-local values; freeze backbone parameters where specified. At inference, feedback updates memory and subtask plans rather than performing online weight updates.","trainingData":"RoboTwin 50 clean/randomized demonstrations; separate RoboDojo and physical data protocols. Q1/Q2/Q3 annotation packages are released for RoboTwin.","trainingTime":"Planner 3k, state 50k, WAM 30k, value 50k steps; wall-clock not reported.","trainingHardware":"16 GPUs for VLM experts; 32 for WAM in paper Table 1.","controlRate":"Not reported","evaluation":"RoboTwin 50 clean/randomized average 94.21% with FastWAM, 95.56% with OpenWAM; three real long-horizon tasks, 65% mean. Paper RoboDojo comparison is not official-board membership."}},{"id":"blind-baoding","name":"No Eyes, No Problem: Blind Baoding Manipulation","title":"No Eyes, No Problem: What Does Touch Contribute to Blind Baoding Ball Manipulation?","date":"2026-10-16","year":2026,"collections":["policy"],"categories":["tactile-il","visual-precision-rl"],"resourceType":"policy","summary":"Studies which binary contact signals enable sustained blind Baoding rotation and zero-shot transfer.","institution":"University of Edinburgh","venue":"arXiv preprint","modalities":["Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2610.23438"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Paper and public presentation verified; no implementation, weight or data release is inferred from a publication or demonstration."},"evidence":[{"url":"https://arxiv.org/abs/2610.23438","label":"Canonical author manuscript and arXiv submission history","fields":["title","date","institution","venue"],"note":"Date retains the initial v1 submission, independently of revisions or current artifact releases."},{"url":"https://arxiv.org/html/2610.23438v1","label":"Full author paper: method, observations and evaluation scope","fields":["summary","policy","modalities","environments","embodiments","caveats"],"note":"Physical sensors, predicted signals, training labels and policy inputs are recorded separately; undisclosed quantities remain unknown."}],"caveats":["The reported 225 consecutive half-turns are one continuous hardware trial, not an aggregate success rate.","Tactile channels combine twelve FSRs and four BioTac sensors, thresholded to binary contacts. No images or object-state estimates enter deployment."],"verified":"2026-10-19","policy":{"family":"RL","baseModel":"PPO in Isaac Lab with a sensor-aligned, coupling-aware Shadow Hand Lite model.","modelSize":"Not reported","inputs":"Four frames of 52 proprioceptive values and 16 binary tactile contacts per frame; 272 values total.","outputs":"13 joint-position commands.","trainingData":"Simulation rollouts with physical-parameter, slew-rate and stuck-taxel randomization.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"60 Hz hardware commands.","trainingMethod":"PPO; tactile calibration and episodic SlewDR/STAT randomization for zero-shot transfer.","evaluation":"Shadow Dexterous Hand Lite Baoding rotation; tactile-off and regional-sensor ablations."}},{"id":"ord-wm","name":"ORD-WM","title":"ORD-WM: Learning Object-Centric Rigid Dynamics for 3D World Modeling in Robotic Manipulation","date":"2026-10-16","year":2026,"collections":["policy"],"categories":["geometry-models"],"resourceType":"model","summary":"Predicts object-wise rigid motion in geometry-preserving vector latents for 3D rollouts and MPC.","institution":"Harbin Institute of Technology, Shenzhen","venue":"arXiv preprint","modalities":["RGB","Point cloud","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.23156","project":"https://hitsz-wyy.github.io/ORD-WM/"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Paper and public presentation verified; no implementation, weight or data release is inferred from a publication or demonstration."},"evidence":[{"url":"https://arxiv.org/abs/2610.23156","label":"Canonical author manuscript and arXiv submission history","fields":["title","date","institution","venue"],"note":"Date retains the initial v1 submission, independently of revisions or current artifact releases."},{"url":"https://arxiv.org/html/2610.23156v1","label":"Full author paper: method, observations and evaluation scope","fields":["summary","model","modalities","environments","embodiments","caveats"],"note":"Physical sensors, predicted signals, training labels and policy inputs are recorded separately; undisclosed quantities remain unknown."},{"url":"https://hitsz-wyy.github.io/ORD-WM/","label":"Official paper-linked project page; release review on 19 October 2026","fields":["links","release"],"note":"Current artifacts are checked separately from paper experiments. A project-only link is not proof of an implementation release."}],"caveats":["Real-world depth is estimated with FoundationStereo, not evidence that measured depth is the deployed input.","MPC is a downstream controller; the predictive model is not counted as a learned action policy."],"verified":"2026-10-19","model":{"role":"Object-centric 3D dynamics predictor; consumed by a downstream model-predictive controller.","family":"3D world model","baseModel":"SDF-supervised vector-neuron VAE and action-conditioned SE(3) motion predictor.","modelSize":"Not reported","inputs":"Object point clouds, robot state and candidate actions. Real observations use three D435 camera views with FoundationStereo-estimated depth.","outputs":"Object-wise rigid-motion increments and geometry-preserving future latent/point-cloud states.","trainingMethod":"Geometry reconstruction followed by action-conditioned rigid dynamics learning.","trainingData":"Manipulation trajectories with object-centric point clouds; paper retains train/evaluation trajectory splits.","trainingTime":"Not reported","trainingHardware":"Not reported","inference":"MPC planning measured on one RTX 3090; budgets and candidate horizons vary.","evaluation":"Multi-step geometry preservation and closed-loop MPC in simulation and with Franka Research 3."}},{"id":"sensenova-roborsi","name":"SenseNova-RoboRSI / 27B Preview","title":"SenseNova-RoboRSI: Recursive Self-Improvement for Embodied Agents","date":"2026-10-16","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A shared robot-agent runtime improves harnesses from execution traces; a distinct 27B Preview branch converts validated interaction experience into model-training supervision.","institution":"Not reported","venue":"Official project report","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"project":"https://github.com/OpenSenseNova/SenseNova-RoboRSI","code":"https://github.com/OpenSenseNova/SenseNova-RoboRSI","report":"https://www.sensenova.ai/has-gpt-6-astra-achieved-embodied-intelligence"},"policy":{"family":"Hybrid","baseModel":"Frozen frontier-model harness configurations, including GPT-6 Astra; the learned SenseNova-RoboRSI-27B-Preview is based on Qwen3.8-27B.","modelSize":"27B base model for the Preview branch; hosted frontier-model counts are not disclosed.","inputs":"Task, robot/simulator observations, skill/MCP interfaces, context/memory and traces. The released BEHAVIOR-1K adapter supplies RGB-D and the native 21D interface; other adapters retain benchmark-specific observations.","outputs":"VLA skill calls, scripted robot actions or end-effector tool requests; model training additionally learns goal understanding, decisions and feedback-based correction.","trainingData":"Validated multimodal interaction traces support Preview model training. Exact data scale and training budget are not disclosed in the current repository.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Two branches: frozen-model harness revision with retesting, and joint model–harness RSI that updates the Qwen3.8-27B-derived Preview model.","evaluation":"Author-reported RoboDojo, LIBERO-Pro and RoboCasa365 comparisons remain protocol-specific. Released runtime integrations now include LIBERO-family, BEHAVIOR-1K, Bench2Dex and DexJoCo; integration availability is not a new scored leaderboard entry."},"release":{"code":"Partial","weights":"Not verified — announced Preview model endpoint is not publicly inspectable","data":"Not verified","license":"Apache-2.0 for the public runtime; external models, simulators and datasets retain separate terms.","notes":"October 20 source release adds BEHAVIOR-1K RGB-D/native-21D service plus Bench2Dex and DexJoCo adapters/build guides. The 27B Preview release was announced for October 20, but its HF API returned401 on October 21; no publicly downloadable weight inventory is established. RoboDojo runtime remains marked coming soon."},"evidence":[{"url":"https://github.com/OpenSenseNova/SenseNova-RoboRSI","label":"Official project / author report","fields":["title","date","summary","policy","release","caveats"],"note":"Project-only evidence; any reported date is an explicit public report or code-release event, not a manuscript submission."},{"url":"https://github.com/OpenSenseNova/SenseNova-RoboRSI/blob/main/robots/behavior_1k/roborsi_adapter.py","label":"Official current source / artifact inventory — 21 October 2026","fields":["release","links","policy"],"note":"Actual RGB-D adapter and native action interface inspected alongside new Bench2Dex/DexJoCo source; the Preview model/API and reported benchmark scores are separate."}],"caveats":["The date is the explicit initial code-release announcement, not a claimed paper submission date.","RoboDojo results were still being submitted to the official leaderboard; final official results may differ.","The README distinguishes LIBERO-Pro Task / Swap aggregates and RoboCasa Target50 from other published leaderboard protocols.","The distinct learned Preview branch is included once; the generic GPT-6 base product is not a robot-policy record.","Measured depth is an adapter-specific BEHAVIOR-1K observation, not a universal observation claim for every agent/model branch."],"verified":"2026-10-21","relatedIds":["astra-embodied-policies"]},{"id":"dexterity-supervised-play","name":"DSP: Dexterity from Supervised Play","title":"Dexterity from Supervised Play","date":"2026-10-15","year":2026,"collections":["policy"],"categories":["visual-precision-rl"],"resourceType":"policy","summary":"Learns a play policy from human interaction, then collects autonomous data for world-model planning.","institution":"Amazon FAR","venue":"arXiv preprint","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2610.22340","project":"https://play-policy.github.io/","code":"https://github.com/FAR-hands/play_policy"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Not reported — no repository license found","notes":"Official linked repository includes play_policy, scripts, Isaac/Manus interfaces and quickstart material. Weights and data remain separate unverified artifacts."},"evidence":[{"url":"https://arxiv.org/abs/2610.22340","label":"Canonical author manuscript and arXiv submission history","fields":["title","date","institution","venue"],"note":"Date retains the initial v1 submission, independently of revisions or current artifact releases."},{"url":"https://arxiv.org/html/2610.22340v1","label":"Full author paper: method, observations and evaluation scope","fields":["summary","policy","modalities","environments","embodiments","caveats"],"note":"Physical sensors, predicted signals, training labels and policy inputs are recorded separately; undisclosed quantities remain unknown."},{"url":"https://play-policy.github.io/","label":"Official paper-linked project page; release review on 19 October 2026","fields":["links","release"],"note":"Current artifacts are checked separately from paper experiments. A project-only link is not proof of an implementation release."},{"url":"https://github.com/FAR-hands/play_policy","label":"Official linked implementation and current file inventory","fields":["release","links"],"note":"Official linked repository includes play_policy, scripts, Isaac/Manus interfaces and quickstart material. Weights and data remain separate unverified artifacts."}],"caveats":["Real and simulated tasks are separate settings; tag-based object pose is not a tactile signal.","A public implementation repository is available, but a source-code license was not found in its root inventory."],"verified":"2026-10-19","policy":{"family":"Model-based planning","baseModel":"Behavior-cloned play policy, action-conditioned world model and play-proposal MPC; exploratory goal-policy distillation.","modelSize":"Not reported","inputs":"Robot state, tracked object pose and desired goal; visual-reorientation variant uses images. Real cube pose is tracked from camera-visible tags.","outputs":"Hand action sequences proposed by the play policy and selected/refined with MPC.","trainingData":"Minutes of human play seed autonomous interaction; real-world world-model scaling study spans 0.5–5 hours.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Real closed-loop system 15–18 Hz; reported replan latency 134 ms on one RTX 5090.","trainingMethod":"Behavior cloning, autonomous play, dynamics prediction and MPC; self-play distillation is a separate acceleration study.","evaluation":"Push-T and in-hand reorientation; physical 22-commanded-joint Sharpa hand with ZED 2i cube tracking."}},{"id":"evo-dexbench","name":"Evo-DexBench","title":"Evo-DexBench: Benchmarking Generalization across Related Tasks for Dexterous Manipulation","date":"2026-10-15","year":2026,"collections":["benchmark"],"categories":["dexterous-benchmarks","tactile-benchmarks","robustness-evaluation"],"resourceType":"benchmark","summary":"A Core–Gen protocol groups30 dexterous tasks by functional interaction, with limited target demonstrations, variation-controlled rollouts and tactile ablations.","institution":"Shanghai Jiao Tong University / Nanjing University / Central South University / BUPT / Beijing Normal University","venue":"arXiv preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2610.21180","code":"https://github.com/MINT-SJTU/Evo-DexBench","data":"https://huggingface.co/datasets/MINT-SJTU/Evo-DexBench-dataset"},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"Apache-2.0 for code/documentation; third-party assets retain separate licenses and access terms","notes":"Actual implementation, setup scripts and LICENSE inspected. Public HF files verified. Paper-linked project page is marked coming soon in README and returned404; omitted as a usable Project artifact. Benchmark-trained policy weights not independently identified."},"evidence":[{"url":"https://arxiv.org/html/2610.21180","label":"Original full manuscript and appendices","fields":["title","date","summary","benchmark","policy","model","modalities","environments","embodiments","caveats"],"note":"Method, evaluation and appendix read; observations, training-only labels, rates and task scopes remain distinct."},{"url":"https://github.com/MINT-SJTU/Evo-DexBench","label":"Official implementation, README and actual Apache license","fields":["links","release","benchmark"],"note":"Executable benchmark and KineDex adapter code inspected; adapter availability is separate from evaluation of all paper baselines."},{"url":"https://github.com/MINT-SJTU/Evo-DexBench/blob/main/pyproject.toml","label":"Current installation constraints","fields":["benchmark.simulatorVersion"],"note":"Release requirements do not establish the paper exact patch."},{"url":"https://huggingface.co/api/datasets/MINT-SJTU/Evo-DexBench-dataset","label":"Public dataset inventory","fields":["release.data","benchmark.data"],"note":"Public ungated inventory contains LeRobot data files; asset access conditions remain separate."},{"url":"https://arxiv.org/abs/2610.21180","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Section3.2 collection pipeline; optional keyboard interfaces in the release are not substituted for the reported VR corpus."}],"caveats":["30-task suite,15-task standard comparison,10-task tactile ablation and separately scoped transfer experiments retain distinct task coverage.","Eight Gen demonstrations are training data; the protocol measures few-shot adaptation, not zero-shot transfer.","123-taxel simulated normal-force observations do not reproduce the original KineDex120 tri-axis taxels/finger.","RGB camera interfaces do not imply observed depth. Scene layout, task family and policy version counts are separate.","Evo No-Touch removes tactile inputs, force supervision and deployment force feedback together; it does not isolate tactile sensing alone. Original KineDex input-only ablation retains a different controller pathway."],"verified":"2026-10-17","benchmark":{"simulator":"ManiSkill 3 / SAPIEN / PhysX","simulatorVersion":"Paper names ManiSkill3 without exact patch. Current pyproject requires mani_skill>=3.0.1,<3.1 and sapien>=3.0; these are release constraints, not a paper lockfile.","taskCount":"30 tasks in6 families:24 Core+6 Gen. Standard paper comparison evaluates15 tasks in3 families; six scene layouts are not additional task types.","tasks":["LampButton","WallSwitch","PhoneHomeButton","ToasterSlider","DisplayPowerButton","FaucetHandle","MouseWheel","CoffeeKnob","KettleLidTab","DishwasherKnob","CupFromCabinet","MicrowaveBowl","TennisBallTransfer","BananaFromDrawer","CandleToCabinet","TablewarePlacement","HammerBlockAssembly","SandwichAssembly","RingTowerAssembly","BoxPacking","PenCapping","CupSleeveFit","BottleFunnelInsertion","PlugInsertion","ScissorsSheathing","LaptopClosing","StaplerPaperMark","ScissorsOpening","BoxLidOpening","GlassesFolding"],"taskListScope":"Complete30 task names from Appendix A, Tables6–11; each family lists4 Core then1 Gen. Main results cover Button-driven, Rotational and Articulated configuration families only.","capabilities":["Functional Core-to-Gen transfer","Eight-shot target adaptation","Single-hand and bimanual dexterity","Camera/light/physical/spatial variations","Tactile ablation"],"metrics":"Episode SR and normalized StageScore, macro-averaged across fixed tasks. SR uses Wilson/rollout intervals; training-seed variability and missing per-condition StageScores are not fabricated.","robots":"Panda arm with Shadow Hand; single-arm and bimanual configurations; Quest3/WebXR demonstration collection.","sensors":"Context, interaction and wrist RGB;31-D or62-D state;123 scalar normal-force taxels per hand. PhysX impulses at120Hz are integrated over30Hz observation periods, preserving total impulse. KineDex adaptation uses100 finger taxels with its own padded format, not original KineDex sensor specifications.","dataCollection":"VR teleoperation with Quest 3/WebXR hand tracking; wrist motion drives Panda arm IK and hand keypoints are retargeted to Shadow Hand joint targets.","data":"1,248 successful trajectories:24 Core×50+6 Gen×8; three layouts per task. Canonical HDF5 and LeRobot-v3 formats. Public HF dataset and asset bundle verified; PartNet assets require separate provider access.","protocol":"100 frozen-policy episodes/task:20 nominal and20 each camera,lighting,physical,spatial; layouts34/33/33. Identical reset schedules across models; no test-time weight or normalization updates. Separate single/bimanual checkpoints; train/transfer mixtures and target budgets remain scoped.","baselines":"π0.5, SmolVLA, Diffusion Policy convolutional/transformer variants, and KineDex; matched KineDex No-Touch ablation. Adapters retain different horizons; training settings and transfer protocols appear in Appendix D."}},{"id":"fast-vla","name":"Fast-VLA","title":"Fast-VLA: You Only Think Once for Robot Manipulation","date":"2026-10-15","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Encodes language and initial views into fixed VLM KV memory once per episode, then uses a compact live-feedback actor for repeated action decisions.","institution":"Huawei Technologies / Huawei Central Research Institute","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2610.21389","project":"https://garfield-kh.github.io/FastVLA","code":"https://github.com/Garfield-kh/FastVLA","weights":"https://huggingface.co/kehong/FastVLA-weights"},"policy":{"family":"VLA","baseModel":"Frozen4B task-memory VLM (DM0.5 default); DINOv2-initialized online vision encoders and learned action decoder/head","modelSize":"4B offline-prefill backbone; complete online-actor parameter total not reported","inputs":"Initial RGB/task language for prefill; live RGB, optional history and proprioception for online actor","outputs":"RoboTwin50 absolute bimanual-joint action commands; benchmark-specific chunk/execution settings","trainingData":"RoboTwin2 all 50 clean + randomized tasks; LIBERO all 40 tasks; frozen VLM memory caches precomputed","trainingTime":"RoboTwin200k steps ≈ 150 accelerator-hours; LIBERO30k≈ 5 accelerator-hours","trainingHardware":"Accelerator model not disclosed in inspected manuscript","controlRate":"RoboTwin subsequent chunks44.87 ms; first call452.47 ms including 407.60 ms prefill; hardware-specific servo rate not stated","trainingMethod":"Train shared online actor with fixed episode KV task memory, current-feedback query fusion and action supervision","evaluation":"RoboTwin2, LIBERO and LIBERO-Plus; memory-swap and efficiency ablations"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2610.21389","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://garfield-kh.github.io/FastVLA","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."},{"url":"https://github.com/Garfield-kh/FastVLA","label":"Official executable release linked from project","fields":["release.code","release.weights","links"],"note":"Repository provides policy training, evaluation and model links."},{"url":"https://github.com/Garfield-kh/FastVLA/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["Initial VLM prefill must be included when comparing amortized latency; subsequent-chunk timing is not first-call timing.","This method uses fixed task memory; it does not establish context adaptation from demonstrated actions."],"verified":"2026-10-19"},{"id":"maia","name":"MAIA / VAF-WM","title":"MAIA: Anticipating Interaction Anomalies in Contact-Rich Manipulation via Multimodal World Models","date":"2026-10-15","year":2026,"collections":["policy"],"categories":["tactile-foundation","geometry-models","visual-precision-rl"],"resourceType":"model","summary":"A visual–action–force world model predicts contact anomalies; its scores can rerank action chunks from a separate frozen π0.5 policy.","institution":"Shanghai Jiao Tong University","venue":"arXiv preprint","modalities":["RGB","Force/Torque","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.21686"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified; source figures retain original rights","notes":"The manuscript points to https://joringell.github.io/MAIA/ but it returned404 at review; omitted as a usable Project artifact. Code, CRIA downloads, weights and license are not independently verified."},"evidence":[{"url":"https://arxiv.org/html/2610.21686","label":"Original full manuscript and appendices","fields":["title","date","summary","benchmark","policy","model","modalities","environments","embodiments","caveats"],"note":"Full method, all18 task definitions, per-task corpus statistics, implementation appendix and online reranking evaluation inspected. Predictor and downstream action sampler are separate."}],"caveats":["Wrist force/torque is not cutaneous tactile; three RealSense D435i cameras supply RGB, not depth in the reported model interface.","CRIA has2,852 collected trajectories; experimental100/20/30 per-task split budgets are distinct from all collected corpus totals.","No explicit end-to-end inference latency is reported. The30Hz control-aligned data timeline and50-step prediction horizon do not establish model refresh frequency.","The world-model video transformer is trained in stage1, then frozen for anomaly-head training and novel-task adaptation."],"verified":"2026-10-17","model":{"role":"Predictive interaction/anomaly model. A downstream wrapper selects among8 proposed π0.5 action chunks; MAIA is not itself a direct action generator.","family":"Predictive contact model","baseModel":"GE-Base video backbone, gated action–force cross-attention adapters, TCN wrench-history encoder, interaction readout and temporal anomaly transformer.","modelSize":"Total parameters not reported; anomaly fusion width128, two temporal transformer layers/four heads.","inputs":"Recent multi-view RGB and wrist six-axis wrench history, language task instruction and planned future action sequence supplied by another policy. Depth camera hardware is not a measured-depth input.","outputs":"Future visual latents and50-step wrench rollout; window anomaly probability, prevalence and auxiliary step scores.","trainingMethod":"Nominal-dynamics flow matching plus force prediction; freeze world model for supervised anomaly-head learning; freeze video backbone during limited-data joint pathway/head adaptation.","trainingData":"CRIA2,852 trajectories over18 tasks, with2,328 anomaly events. At least150 trajectories/task;13 seen tasks for main training,5 novel tasks held out;10 adaptation trajectories/novel task. Nominal, anomalous human demonstrations and π0.5 rollouts are distinct sources.","trainingTime":"30k world-model steps;5k anomaly-head steps;2k adaptation steps. Wall-clock time not reported.","trainingHardware":"4 NVIDIA A100 GPUs for all reported training stages.","inference":"30Hz alignment;128-step sample/50-step future, one world-model inference step for head features. Downstream wrapper scores8 chunks, executes20 actions and computes asynchronously; this is not a claimed30Hz model inference rate.","evaluation":"Offline anomaly AUPRC,bACC,anticipation rate/lead time on13 seen+5 novel tasks. Real frozen-policy reranking:18 tasks×30 trials/method; mean success52.6%→61.5%. Novel-task results use adaptation, not zero-shot operation."}},{"id":"spark-0","name":"Spark-0","title":"Spark-0: Scaling Spatially Aligned Tactile Pretraining for Robot Dexterous Manipulation","date":"2026-10-15","year":2026,"collections":["benchmark","policy"],"categories":["tactile-benchmarks","dexterous-benchmarks","tactile-foundation","generalist-foundation"],"resourceType":"benchmark","summary":"Spatially aligned VTLA pretraining uses human touch as visual supervision and as an optional action-expert input; Spark Arena separates vision-only simulation from real tactile control.","institution":"Xspark AI / Tsinghua University / MMLab, HKU / HKUST (Guangzhou) / Peking University","venue":"Preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2610.22342","project":"https://xsparkai.com/Spark-0/","code":"https://github.com/XsparkAI/Spark-0","weights":"https://huggingface.co/TianxingChen/Spark-0"},"policy":{"family":"VLA","baseModel":"StarVLA / Qwen 3-VL-4 B-Instruct; 36-layer flow-matching action DiT; frozen Depth Anything 3 geometry teacher only during training.","modelSize":"4 B VLM backbone; total policy parameters Not reported.","inputs":"Head/wrist RGB, language and three robot-state frames; optional bilateral regional-force history and 3D pressure-taxel tokens. No measured depth input.","outputs":"50-step chunks of 58-dimensional bimanual state/action targets.","trainingData":"Human Touch: 1,000 h, >100 tasks, 32 operators; 460 physical taxels/hand with 140 flex ion points masked, leaving 320 used. Table 5 mixture totals 1,944 source-hours including EgoDex 662 h and separate robot/simulation corpora; sampling weights differ from hours.","trainingTime":"400 k pretraining + 80 k downstream steps; wall-clock time Not reported.","trainingHardware":"12 nodes × 8 GPUs = 96 GPUs; GPU model Not reported (Table 6).","controlRate":"Training-free real-time chunking: 50-action horizon, request after 24 executed steps, nominal eight-step inference delay; physical Hz Not reported.","trainingMethod":"400 k pretraining steps with geometry/contact alignment and tactile fusion; 80 k downstream fine-tuning steps; frozen geometry teacher and alignment heads are removed at inference. The current XPolicyLab adapter recipe instead lists 500k pretraining steps; this is a separate released recipe, not the paper setting.","evaluation":"Spark Arena seven simulation task settings without tactile input and five real contact-rich tasks with tactile; additional Ego Humanoid vision-only transfer. Different tactile deployment conditions must be compared within their tracks."},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Official Spark-0 source MIT, verified from LICENSE; XPolicyLab integration is Apache-2.0 and vendored StarVLA retains MIT notices. Checkpoint and dataset terms are separate.","notes":"Official standalone Spark-0 source is public. Author-linked XPolicyLab contains vendored Spark-0 implementation,training/evaluation recipes and a download script pointing to the verified pretrain_spark_0 checkpoint. SparkArena-Sim is separately marked Coming soon."},"evidence":[{"url":"https://arxiv.org/abs/2610.22342","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats","benchmark"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://xsparkai.com/Spark-0/","label":"Official author project","fields":["links","release"],"note":"Live artifact status reviewed separately from paper claims."},{"url":"https://github.com/XPolicyLab/XPolicyLab","label":"Official released implementation and LICENSE","fields":["links","release"],"note":"Repository metadata,README and actualLICENSE inspected; availability scoped to the listed component."},{"url":"https://github.com/XPolicyLab/XPolicyLab/blob/main/policy/Spark_0/scripts/download_spark0_pretrained_ckpt.sh","label":"Official adapter checkpoint download script","fields":["links","release"],"note":"Explicit author checkpoint identity TianxingChen/Spark-0; public pretrain_spark_0 tensor-file inventory verified."},{"url":"https://huggingface.co/TianxingChen/Spark-0","label":"Public Spark-0 pretraining checkpoint","fields":["release.weights"],"note":"Model repository contains pretraining tensors; this does not establish task-finetuned checkpoints or benchmark assets."},{"url":"https://arxiv.org/abs/2610.22342","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."},{"url":"https://github.com/XsparkAI/Spark-0","label":"Official standalone Spark-0 source and LICENSE","fields":["links.code","release.code","release.license"],"note":"Current standalone repository is public and its actual LICENSE is MIT. The canonical paper’s 400k pretraining steps and the current adapter recipe’s 500k are distinct settings."},{"url":"https://github.com/XPolicyLab/XPolicyLab/tree/94dbf9505f2f87cf59c3ee8ba5912cd236781771/policy/Spark_0","label":"XPolicyLab adapter identity and current recipe","fields":["policy.trainingMethod"],"note":"Same title/arXiv and XsparkAI upstream as canonical Spark-0. Current adapter recipe lists 500k pretraining steps; retain paper-specific 400k rather than silently overwriting it."}],"caveats":["Depth Anything 3 and contact-map heads supervise visual tokens only during training; inferred teacher geometry is not runtime RGB-D sensing.","Spark Arena-Sim and Ego Humanoid deploy without touch; Spark Arena-Real deploys with tactile gloves.","The paper claims released Spark Arena assets, but the live project labels Spark Arena-Sim code Coming soon. Artifact availability is verified separately from paper prose."],"verified":"2026-10-17","benchmark":{"simulator":"Not reported in the inspected paper; Spark Arena-Sim reproduces the Gen1Prototype kinematics/cameras.","simulatorVersion":"Not reported","taskCount":"12 track-specific task settings: 7 simulation + 5 real; Stack Bowls occurs in both tracks.","tasks":["Sim: Click Mouse","Sim: Collect Objects","Sim: Dual Bottles Pick","Sim: Hammer Beat","Sim: Put Food in Microwave","Sim: Retrieve Gap","Sim: Stack Bowls","Real: Insert Test Tubes","Real: Pack Shoes into Box","Real: Place the Phone","Real: Stack Bowls","Real: Stand Up Bottle"],"taskListScope":"Complete Spark Arena Table 7 registry, with track prefixes; not 12 unique semantic task families.","capabilities":["Vision-only dexterity after tactile pretraining","Contact-conditioned precision control","Bimanual grasping","Insertion","Compliant packing","Object reorientation"],"metrics":["Success rate: all task goals reached / score 100","Completion score: highest attained subgoal stage; binary on tasks without intermediate stages"],"robots":["Gen1Prototype: two Marvin M6 S CCS-696 arms and two WUJI Hand 2 dexterous hands"],"sensors":["Orbbec 336 head RGB camera","Two Orbbec 305 wrist RGB cameras","Moxian tactile gloves on real hands; 320 retained taxels/hand; no tactile input in simulation"],"dataCollection":"Benchmark task-demonstration collector Not reported. Human Touch pretraining uses egocentric human motion/tactile capture and is separate from SparkArena task demonstrations.","data":"Human Touch 1,000 h is pretraining data, not the Spark Arena evaluation episode count. Benchmark-specific downstream demonstrations are separate; their exact count Not reported.","protocol":"Held-out evaluated tasks absent from pretraining. Per policy/task: 50 simulated rollouts, 10 real rollouts; real methods share paired initial conditions.","baselines":["π0.5","GR00T-N1.7","StarVLA","RDT-1 B","X-VLA","EgoVLA","H-RDT","VITRA","UniT (human-action policy)","FastWAM","ACT","FTP-1","Spark-0 Scratch / Visual PT / 100 H / 1000 H"]},"benchmarkName":"SparkArena","policyName":"Spark-0","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Not verified","license":"Official Spark-0 source MIT, verified from LICENSE; XPolicyLab integration is Apache-2.0 and vendored StarVLA retains MIT notices. Checkpoint and dataset terms are separate.","notes":"Official standalone Spark-0 source is public. Author-linked XPolicyLab contains vendored Spark-0 implementation,training/evaluation recipes and a download script pointing to the verified pretrain_spark_0 checkpoint. SparkArena-Sim is separately marked Coming soon."},"benchmark":{"code":"Not released","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"SparkArena-Sim code button says Coming soon; paper release claims are not substituted for retrievable assets."}}},{"id":"adarsi","name":"AdaRSI","title":"AdaRSI: On-Demand Recursive Self-Improvement for Embodied Agents","date":"2026-10-14","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Improves a frozen executor through harness revision first, then fine-tunes only the substeps whose failures remain after harness repair.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.20474"},"policy":{"family":"Hybrid","baseModel":"GPT-6.1-sol coding agent; GR00T N1.5 executor initialized from official atomic / composite checkpoints.","modelSize":"Not reported","inputs":"Task, robot observations and execution feedback for the agent; saved failure states and collected demonstrations for selective executor training.","outputs":"Harness programs call a library of existing or selectively fine-tuned manipulation policies for individual substeps.","trainingData":"Self-collected demonstrations target attributed executor deficits; no human demonstrations are used in the 50-task experiment.","trainingTime":"Selective fine-tuning uses 28% of the GPU training time and 22% of the demonstrations of the paper's always-train comparison; absolute duration is not reported here.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Both code and policy weights can change: harness refinement, failure attribution, targeted demonstration collection, selective fine-tuning and deployment-time policy recommendation.","evaluation":"50 RoboCasa365 tasks, each evaluated on 50 scenes: 18 Atomic-Seen, 16 Composite-Seen and 16 Composite-Unseen."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2610.20474","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["Fine-tuned policies execute about 15% of deployed manipulation steps in the reported study; this is not a claim that the entire system is training-free.","Development can access privileged simulator state to generate and label demonstrations; the deployed harness receives regular observations rather than that development privilege."],"verified":"2026-10-17"},{"id":"forcebench","name":"ForceBench / force-integration policies","title":"ForceBench: Benchmarking Force Integration for Robotic Manipulation","date":"2026-10-14","year":2026,"collections":["benchmark","policy"],"categories":["precision-assembly","robustness-evaluation","tactile-foundation","generalist-foundation"],"resourceType":"benchmark","summary":"A common π0.5 backbone compares wrist-wrench conditioning, predictive supervision and temporal force interfaces across controlled physical interactions.","institution":"Peking University / UC Berkeley / National University of Singapore / ePyBot Intelligence","venue":"arXiv preprint","modalities":["RGB","Force/Torque","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.19604","project":"https://forcebench-platform.github.io/"},"release":{"code":"Not released","weights":"Not released","data":"Not released","license":"Not verified; source figures retain original rights","notes":"Official Resources section explicitly provides no code, demonstrations or checkpoint downloads. Its stale no-paper-download notice is superseded by the public arXiv manuscript; result tables are available separately."},"evidence":[{"url":"https://arxiv.org/html/2610.19604","label":"Original full manuscript and appendices","fields":["title","date","summary","benchmark","policy","model","modalities","environments","embodiments","caveats"],"note":"Method, evaluation and appendix read; observations, training-only labels, rates and task scopes remain distinct."},{"url":"https://forcebench-platform.github.io/","label":"Official benchmark, task/protocol pages and release notice","fields":["links","release","benchmark"],"note":"Live project inspected; implementation/data/weights are independently distinguished from paper and published result tables."},{"url":"https://arxiv.org/abs/2610.19604","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Wrist F/T is not skin tactile; force varies through physical properties that may also affect visible motion and proprioception.","Single simulated embodiment and one assembly task limit scope; 20 real trials/task do not establish a general sim-to-real ranking correlation.","The best two simulation means differ by three successful episodes out of1600; no significance claim is inferred."],"verified":"2026-10-17","benchmarkName":"ForceBench","policyName":"ForceBench force-integration policies","benchmarkSummary":"Eight simulation tasks and five separate physical tasks compare wrist force integration under a common demonstration, controller and evaluation protocol.","benchmark":{"simulator":"NVIDIA Isaac Lab / Isaac Sim","simulatorVersion":"Not reported in the inspected paper and appendix","taskCount":"8 simulation tasks; 5 separate real-world tasks. These tracks are not 13 independent task families.","tasks":["Simulation — Weight Sort","Simulation — CoM Sort","Simulation — Faucet Turn","Simulation — Faucet Direction","Simulation — Stove Turn","Simulation — Stove Press","Simulation — Drawer Pull","Simulation — Nut Thread","Real — Weight Sort","Real — Plate Retrieve","Real — Stamp Press","Real — Plug Insert","Real — Block Insert"],"taskListScope":"Complete simulation task list (§III-B) and separate hardware study (Appendix C-B). Weight Sort occurs in both tracks.","capabilities":["Physical-property inference","Resistance-dependent interaction","Contact-rich assembly","Force-interface comparison"],"metrics":"Complete episode success; task/category macro means; destination and mechanism-progress diagnostics do not substitute for success. Nut Thread also checks release and withdrawal.","robots":"Simulated Franka with parallel gripper; real Franka Research 3, Robotiq gripper and KWR75B six-axis F/T sensor.","sensors":"External/wrist RGB, task language, 7-D TCP/gripper state and six-axis wrist wrench. Sim uses parent-on-child joint reaction without gravity compensation; real examples retain sensor-frame readings. No cutaneous tactile or measured depth input.","dataCollection":"Simulation: task-specific scripted experts with privileged state and a shared controller. The physical demonstration collection interface is Not reported.","data":"1,600 successful scripted simulation demos: 200/task, seeds0–199, synchronized at15Hz. Real study:200 demos/task. Public demonstrations not released at review snapshot.","protocol":"Ten systems ×8 tasks ×200 shared evaluation seeds300–499 =16,000 simulation episodes. Physics60Hz/control15Hz; budget1500 controls/100s. Seven π0.5 variants, π0 and ForceVLA execute50-action chunks; ForceFlow predicts64/executes32. Real:8 systems ×5 tasks ×20 shared starts =800 trials.","baselines":"π0.5 without force; Token Fusion; Prompt Fusion; Cross-Attention; Token Fusion+Pred; Temporal Prompt; Dual-Path Fusion; external π0, ForceVLA and ForceFlow. External architectures/budgets/horizons differ."},"policy":{"family":"VLA","baseModel":"π0.5 with alternative numerical-prompt, continuous-token or output cross-attention force interfaces; temporal variants add16-sample summaries.","modelSize":"Not reported in the inspected paper and appendix","inputs":"External/wrist RGB, language,7-D robot state; current6-D wrench or16-sample history depending on variant. No-force baseline excludes wrench.","outputs":"50-step chunks of absolute TCP poses and gripper targets.","trainingMethod":"Task-specific flow-matching fine-tuning. Token Fusion+Pred adds a ten-step future-wrench objective during training only; it does not run at deployment.","trainingData":"Same200 successful scripted demos per simulation task for compared methods; real policies use200 demos/task.","trainingTime":"10,000 steps for π0.5 variants and π0;100,000 for ForceFlow. Wall-clock time not reported.","trainingHardware":"Not reported in the inspected paper and appendix","controlRate":"Simulation15Hz execution/recording and60Hz physics. Executing all50 steps gives a3.33s observation-query interval, not15Hz closed-loop policy queries. Physical end-to-end policy Hz not reported.","evaluation":"Eight simulation tasks with200 episodes/task; hardware comparison on five tasks with20 trials/task. Overall point estimates do not establish paired significance or training-seed variance."}},{"id":"mocap","name":"MoCaP","title":"MoCaP: Mobile Code-as-Policies for Zero-Shot Mobile Manipulation from Passive Human Videos","date":"2026-10-14","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Passive human video supplies reusable spatial memory and approach priors to a robot-grounded, closed-loop mobile code-as-policy agent.","institution":"New York University","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Real world"],"embodiments":["Mobile manipulator","Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.20998","project":"https://mocap-7.github.io/MoCAP-page/","code":"https://github.com/MoCAP-7/MoCAP"},"release":{"code":"Available","weights":"Not applicable","data":"Not verified","license":"Repository-wide source license not verified. External models, motion tools and video assets retain separate terms.","notes":"Actual yor_agent and nav_planner implementations are public, including spatial-memory and robot-controller tools. No repository-wide license was established; included CaP-X mobilemanip baselines are not substituted for the original method."},"evidence":[{"url":"https://arxiv.org/html/2610.20998","label":"Original full paper, methods and evaluation","fields":["title","date","institution","summary","modalities","policy","model","benchmark","benchmark.dataCollection","caveats"],"note":"Publication date is arXiv v1; implementation and deployment observations are distinguished from training supervision, generated geometry and base-model identity."},{"url":"https://github.com/MoCAP-7/MoCAP","label":"Official artifact: actual source files, inventory and license","fields":["links","release"],"note":"Actual original yor_agent and nav_planner runtime/source inspected, including nav_planner/src/nav_planner/memory.py. Root source license not found."}],"caveats":["Human-video spatial memory is a prior; deployment separately observes live RGB-D, mobile-base pose and arm/joint state.","Depth lifts current candidate image targets to 3D for robot-grounded manipulation tools; human videos alone are not an executable action policy.","The real evaluation has two tasks; it is not a RoboDojo leaderboard participant."],"verified":"2026-10-21","policy":{"family":"Robot agent","baseModel":"Frozen VLM-driven mobile code-as-policy with passive-video spatial memory and robot-grounded navigation/manipulation primitives.","modelSize":"Not reported","inputs":"Task and RGB-only passive-video spatial/approach memory; live robot observation o=(RGB I, measured depth D, base pose x, joint state z). ZED2i stereo RGB-D supplies metric depth for 3D grounding; VGGT-relative geometry uses that scale.","outputs":"Closed-loop tool/code calls coordinate base navigation, manipulation-ready base pose selection and end-effector grasping under kinematic constraints.","trainingMethod":"Extract human spatial memory/approach cues; frozen-model execution observes outcomes and replans through robot-grounded tool use.","trainingData":"Passive human activity videos provide reusable priors rather than per-task robot demonstrations.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","evaluation":"Real YOR mobile dual-arm robot:80% success on each of two manipulation tasks. Navigation/base-pose ablations are separate subtask metrics."}},{"id":"tactile-guided-survey","name":"Tactile-Guided Robot Learning — Survey","title":"Tactile-Guided Robot Learning: From Representation Learning to Foundation Models","date":"2026-10-14","year":2026,"collections":["policy"],"categories":["tactile-foundation","tactile-representation"],"resourceType":"survey","summary":"Connects tactile representation, executable policies, foundation/world models and adaptation through the functional role of touch in robot learning.","institution":"University College London; MIT CSAIL; University of Michigan; University of Tokyo","venue":"Preprint · Survey","modalities":[],"environments":["Literature review"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2610.20134","project":"https://sites.google.com/view/touch-in-robot-learning-review"},"survey":{"scope":"Learning-centric tactile manipulation, from reusable representations and language grounding to control and generalization.","taxonomy":["Tactile representation learning","Tactile-conditioned policy learning","Tactile-informed foundation and world models","Predictive and adaptive tactile manipulation","Generalization of tactile-conditioned policies"],"adaptationRegimes":["Offline self-supervised pretraining","Imitation and reinforcement learning","Language/action integration","Predictive and adaptive control","Object, task, sensor, embodiment and sim-to-real transfer"],"contextSources":["Optical and non-optical tactile readings","Paired vision/language","Interaction history and predicted contact states","Privileged simulation information and real demonstrations"],"evaluationAxes":["Perceptual representation quality","Policy success and contact-sensitive behavior","Cross-sensor and cross-embodiment transfer","Real-world grounding and adaptation","Data scale and physical observability"],"corpus":"Narrative review with an author-maintained online literature/resource database; source bibliography extracted independently for citation reconciliation.","reviewMethod":"Functional-role synthesis of tactile-guided skill learning; comparison tables organize learning paradigms and recent tactile foundation/world models.","coverageLimits":"The categories combine encoders, perceptual reasoning and action-generating systems. A shared table does not establish that every model emits robot actions, has a released checkpoint or supports the same sensors."},"release":{"code":"Not applicable — survey; no new robot implementation","weights":"Not applicable — survey","data":"Available — author-maintained literature database","license":"Paper and third-party figures retain their stated original rights","notes":"Literature inventories and review tables are not robot demonstration data or deployable model weights."},"evidence":[{"url":"https://arxiv.org/abs/2610.20134","label":"Original survey and first submission","fields":["title","date","summary","institution","survey"],"note":"Manuscript structure, tables and bibliography inspected; underlying method specifications require their original paper."},{"url":"https://arxiv.org/html/2610.20134","label":"Primary full-text survey and references","fields":["survey"],"note":"Citation extraction preserves original bibliography identities, including background and adjacent resources."},{"url":"https://sites.google.com/view/touch-in-robot-learning-review","label":"Author-maintained review database","fields":["links","release.data"],"note":"Literature database; no claim of robot training data."}],"caveats":["Survey, not an executable policy or model; deployment modalities, training costs and control rates do not apply."],"relatedIds":["sparsh","unitouch","t3","tvl","anytouch","anytouch2","sparsh-x","sparsh-skin"],"verified":"2026-10-17","surveyAuditId":"tactile-guided-survey-references"},{"id":"tgrasp6d","name":"TGrasp6D","title":"TGrasp6D: Task-Informed Vision-Language Priors and Pose Refinement enable Zero-Shot Task-Oriented 6DoF Grasping","date":"2026-10-14","year":2026,"collections":["policy"],"categories":["rgbd-3d","geometry-models"],"resourceType":"model","summary":"A VLM identifies a task-relevant affordance/grasp axis in RGB, lifts it with depth, then refines six-degree-of-freedom grasps for collision avoidance.","institution":"Purdue University","venue":"Preprint","modalities":["RGB","Depth","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.20901"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The inspected primary sources do not establish a public implementation, model checkpoint or dataset download. Paper figures retain their original rights."},"evidence":[{"url":"https://arxiv.org/html/2610.20901","label":"Original full paper, methods and evaluation","fields":["title","date","institution","summary","modalities","policy","model","benchmark","benchmark.dataCollection","caveats"],"note":"Publication date is arXiv v1; implementation and deployment observations are distinguished from training supervision, generated geometry and base-model identity."}],"caveats":["Grasp synthesis is not itself a long-horizon coding agent or learned closed-loop VLA.","TaskGrasp 94% success and physical execution are separate evaluation settings; throughput is not servo frequency."],"verified":"2026-10-21","model":{"role":"Task-oriented 6-DoF grasp proposal/refinement model.","family":"Grasp synthesis","baseModel":"Pretrained VLM semantic priors plus depth alignment and geometric pose optimization; GPT/Gemini/Qwen ablations separate.","modelSize":"Not reported","inputs":"Task language and one RGB-D object observation.","outputs":"Collision-aware 6-DoF task-consistent grasp poses.","trainingMethod":"No grasp-specific model training: image-space affordance/axis reasoning,3D lifting, proposal sampling and pose optimization.","trainingData":"TaskGrasp object-task pairs for evaluation; baseline retraining is separate from proposed method training.","trainingTime":"Not reported","trainingHardware":"Evaluation workstation RTX 5080 +20-core Intel Core i7; this includes separately retrained baselines.","inference":"Not reported","evaluation":"TaskGrasp and Franka FR3 real novel-object/task grasps."}},{"id":"contact-trajectory-prompting","name":"Contact Trajectory Prompting (CTP)","title":"Contact Trajectory Prompting: In-Context Transfer of Contact-Rich Behaviors from a Single Demonstration","date":"2026-10-13","year":2026,"collections":["policy"],"categories":["tactile-il","agent-rsi-icl"],"resourceType":"policy","summary":"Encodes a reference motion/contact trajectory into a prompt for one-shot transfer without test-time weight updates.","institution":"Institute of Automation, Chinese Academy of Sciences","venue":"arXiv preprint","modalities":["RGB","Tactile","Force/Torque","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.18848","project":"https://ctp-robotics.github.io/project_page/"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Paper and public presentation verified; no implementation, weight or data release is inferred from a publication or demonstration."},"evidence":[{"url":"https://arxiv.org/abs/2610.18848","label":"Canonical author manuscript and arXiv submission history","fields":["title","date","institution","venue"],"note":"Date retains the initial v1 submission, independently of revisions or current artifact releases."},{"url":"https://arxiv.org/html/2610.18848v1","label":"Full author paper: method, observations and evaluation scope","fields":["summary","policy","modalities","environments","embodiments","caveats"],"note":"Physical sensors, predicted signals, training labels and policy inputs are recorded separately; undisclosed quantities remain unknown."},{"url":"https://ctp-robotics.github.io/project_page/","label":"Official paper-linked project page; release review on 19 October 2026","fields":["links","release"],"note":"Current artifacts are checked separately from paper experiments. A project-only link is not proof of an implementation release."}],"caveats":["Fingertip F/T sensors and optical tactile maps are separate channels.","The camera is RGB-D-capable; the described online policy consumes RGB, not a verified depth-image stream."],"verified":"2026-10-19","policy":{"family":"Flow matching","baseModel":"DINOv2 visual features, contact-trajectory prompt encoder and flow-matching action head.","modelSize":"Not reported","inputs":"RGB, eight-step proprioceptive history, bilateral fingertip wrenches and distributed force maps; one reference contact trajectory.","outputs":"32-step relative 10D action chunk: translation, 6D rotation and gripper; first 16 actions executed.","trainingData":"PICO teleoperated wiping, rotation and insertion demonstrations with synchronized contact observations.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Data synchronized at 30 Hz; inference measured on RTX 4090, deployed control frequency Not reported.","trainingMethod":"Prompt reconstruction pretraining then joint policy learning; encoder and policy stay fixed at test time.","evaluation":"xArm7 on unseen wiping, rotation and insertion objects, with contact-intensity/direction specification."}},{"id":"embodiedevo","name":"EmbodiedEvo","title":"EmbodiedEvo: Self-Evolving Coding Agent For Physical Intelligence","date":"2026-10-13","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A coding agent develops executable robot-control programs and reusable Skills and Lessons through Playground interaction. Fresh test agents inherit the frozen artifacts on new scenes.","institution":"NAVER LABS Europe / Einsia.AI; Tsinghua University","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.18867","project":"https://lab.einsia.ai/embodiedevo","code":"https://github.com/Einsia/EmbodiedEvo"},"policy":{"family":"Robot agent","baseModel":"Frozen coding agents, including GPT-6 Astra, GPT-5.6 Sol and Luna; model and reasoning-effort variants are evaluated separately.","modelSize":"Not reported","inputs":"Task instruction, RGB observations, robot proprioception, execution feedback and persistent code / Skills / Lessons files.","outputs":"Executable Python programs issue native environment actions through get_instruction, get_observation and step; reset is restricted to Playground.","trainingData":"No task-specific neural weight training. Task-specific Playground interactions develop reusable code and textual artifacts.","trainingTime":"Up to 100 accepted code executions per task in the reported Playground protocol; wall-clock time is not reported as neural training time.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen model weights; recursive improvement changes programs, Skills and Lessons, then freezes the resulting harness for test.","evaluation":"RoboDojo: 42 tasks, 210 development scenes and a 2,100-scene full test set; task and controller variants have separately reported protocols."},"release":{"code":"Available","weights":"Not applicable","data":"Partial","license":"Repository-wide license not verified; bundled skill recipes have separate terms.","notes":"Public control framework and recipes; evaluation archives and hosted-model access are separate from code availability."},"evidence":[{"url":"https://arxiv.org/abs/2610.18867","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/Einsia/EmbodiedEvo","label":"Author-linked repository","fields":["links.code"],"note":"Repository identity; code and checkpoint release status are stated separately."}],"caveats":["Distinct from Song et al.'s EmbodiedRSI (arXiv: 2610.10498), even though the latter project page also uses EmbodiedEvo branding.","Paper and project leaderboard snapshots report different aggregate values; results must retain their snapshot and protocol."],"verified":"2026-10-17"},{"id":"fact-force-action-track","name":"FACT","title":"FACT: A Multimodal Force-Action-Track Foundation Model for Human-to-Robot Dexterity","date":"2026-10-13","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"A visual track-guided world/action planner and force-aware refiner support human-to-robot dexterous transfer.","institution":"University of Michigan / Stanford University / Microsoft Research","venue":"Preprint","modalities":["RGB","Tactile","Force/Torque","Proprioception","Language"],"environments":["Real world"],"embodiments":["Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2610.19155","project":"https://fact-1-0.github.io/"},"policy":{"family":"World-action model","baseModel":"Wan2.2-TI2V-5B track-guided video/action planner; force–action joint refiner trained from scratch; frozen VAE, umT5-XXL and CLIP encoders.","modelSize":"5.05B coarse planner and 65M force–action refiner (Table IV); frozen external VAE/text/image encoders are separate.","inputs":"RGB, task language, robot state, visual track history and five fingertip 6-axis wrenches/hand.","outputs":"16-step 62D bimanual action chunks plus future video, tracks and force predictions.","trainingData":"Ego Force~2,000 h/40k episodes/1,000 tasks/120 scenes/1,000 demonstrators; ten evaluation tasks each use 10 robot+90 human demos for fine-tuning.","trainingTime":"Not reported","trainingHardware":"Table IV: 240 Alibaba T-Head PPU Zhenwu 810E accelerators,96GB HBM2e each. Task-specific fine-tuning reports8 GPUs separately, without type.","controlRate":"Actions 20 Hz; contact refinement 5 Hz; coarse 16-step chunk.","trainingMethod":"Joint pretraining then 100-trajectory/task fine-tuning; coarse planner runs per chunk and refiner refreshes from measured contact.","evaluation":"Bimanual YAM station with two Sharpa Wave hands across 10 real tasks; task-specific post-training, not zero-shot all-task execution."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2610.19155","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://fact-1-0.github.io/","label":"Official author project","fields":["links","release"],"note":"Live artifact status reviewed separately from paper claims."}],"caveats":["Fingertip 6-axis contact wrenches differ from a single wrist F/T sensor.","Ego Force collection corpus and task-specific 100 demo mixtures have distinct roles.","EgoForce human fingertip6D wrenches are derived from12 normal-pressure taxels: normal force and center-of-pressure moments, with shear/twist components set tozero. Robot Sharpa tactile wrenches are a different sensing realization.","EgoForce human fingertip6D wrenches are derived from12 normal-pressure taxels: normal force and center-of-pressure moments, with shear/twist components set tozero. Robot Sharpa tactile wrenches are a different sensing realization."],"verified":"2026-10-17"},{"id":"forcewam","name":"ForceWAM","title":"ForceWAM: Decoupling Future and Response Learning from Demo and Play Data for Contact-Rich Manipulation","date":"2026-10-13","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Generates visual-wrench futures and translates them into reactive actions using task and play data.","institution":"Fudan University","venue":"arXiv preprint","modalities":["RGB","Force/Torque","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.18852","project":"https://force-wam.github.io/"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Paper and public presentation verified; no implementation, weight or data release is inferred from a publication or demonstration."},"evidence":[{"url":"https://arxiv.org/abs/2610.18852","label":"Canonical author manuscript and arXiv submission history","fields":["title","date","institution","venue"],"note":"Date retains the initial v1 submission, independently of revisions or current artifact releases."},{"url":"https://arxiv.org/html/2610.18852v1","label":"Full author paper: method, observations and evaluation scope","fields":["summary","policy","modalities","environments","embodiments","caveats"],"note":"Physical sensors, predicted signals, training labels and policy inputs are recorded separately; undisclosed quantities remain unknown."},{"url":"https://force-wam.github.io/","label":"Official paper-linked project page; release review on 19 October 2026","fields":["links","release"],"note":"Current artifacts are checked separately from paper experiments. A project-only link is not proof of an implementation release."}],"caveats":["Six-axis end-effector F/T is a wrist wrench stream, not tactile skin.","The paper announces code via the project; no downloadable implementation link was verified on the inspected project page."],"verified":"2026-10-19","policy":{"family":"World-action model","baseModel":"Cosmos Predict 2B-initialized visual-wrench world model and diffusion inverse-dynamics model.","modelSize":"2B pretrained world-model backbone; total adapted system size Not reported.","inputs":"Wrist RGB, measured six-axis TCP wrench, robot state and instruction; generated visual/wrench futures condition the IDM.","outputs":"Relative TCP pose and gripper commands; auxiliary virtual-pose/stiffness prediction targets are discarded for execution.","trainingData":"Successful task demonstrations train the world model; task and task-agnostic play trajectories train the IDM.","trainingTime":"World model: 20,000 iterations; wall-clock Not reported.","trainingHardware":"World-model training uses eight GPUs; model unspecified.","controlRate":"Not reported","trainingMethod":"Visual-wrench future prediction and conditional diffusion IDM with future-conditioning augmentation.","evaluation":"Flexiv Rizon 4S force-regulated sliding, box flipping with/without disturbance and Hanoi ring placement."}},{"id":"foretac","name":"ForeTac","title":"ForeTac: Predictive Contact Guidance for Generative Robot Policies","date":"2026-10-13","year":2026,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"Guides diffusion/flow action generation with gradients of predicted future contact quality.","institution":"Not reported","venue":"arXiv preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.18118","project":"https://foretac.github.io/"},"release":{"code":"Not released","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Official project explicitly labels Code as Coming soon. Paper results do not establish a downloadable release."},"evidence":[{"url":"https://arxiv.org/abs/2610.18118","label":"Canonical author manuscript and arXiv submission history","fields":["title","date","institution","venue"],"note":"Date retains the initial v1 submission, independently of revisions or current artifact releases."},{"url":"https://arxiv.org/pdf/2610.18118","label":"Full author paper: method, observations and evaluation scope","fields":["summary","policy","modalities","environments","embodiments","caveats"],"note":"Physical sensors, predicted signals, training labels and policy inputs are recorded separately; undisclosed quantities remain unknown."},{"url":"https://foretac.github.io/","label":"Official paper-linked project page; release review on 19 October 2026","fields":["links","release"],"note":"Current artifacts are checked separately from paper experiments. A project-only link is not proof of an implementation release."}],"caveats":["ForeTac is distinct from the existing ForeTac-VLA work (arXiv:2609.20980).","The inspected project anonymizes affiliations; no institution is inferred from unrelated names."],"verified":"2026-10-19","policy":{"family":"Hybrid","baseModel":"TacVAE, action-conditioned tactile foresight and learned quality scorer guiding frozen Diffusion Policy / pi0.5.","modelSize":"DP configuration: 338.043M backbone plus 34.922M ForeTac parameters, 372.965M total.","inputs":"Visual/proprioceptive context and eight-frame 9×9×2 tactile marker-displacement history; candidate actions enter the predictor.","outputs":"Guided 16-step action sequences; model parameters stay frozen during sampling-time guidance.","trainingData":"Successful and failed physical interactions supervise a task-specific contact-quality objective.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"261.26±5.16 ms per batch-one replan on RTX 4090; this is replanning latency, not low-level control frequency.","trainingMethod":"Tactile representation/prediction/scoring followed by gradient guidance of a pretrained generator.","evaluation":"Four physical tasks: planar wiping, vase wiping, card swiping and fragile-chip grasping; 20 trials/task."},"relatedIds":["foretac-vla"]},{"id":"sear","name":"SEAR","title":"SEAR: Self-Evolving Agentic Robotics","date":"2026-10-13","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Studies self-evolving robotics through code policies, online agents and automated learned-policy research, with explicit measurements of transfer and negative transfer.","institution":"Luma AI","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2610.18792","project":"https://sear.bot/","code":"https://github.com/Self-Evolving-Agentic-Robotics/SEAR"},"policy":{"family":"Hybrid","baseModel":"Seven language-model backbones under code-as-policy, agent-as-policy and auto-research configurations; learned robot policies belong to the auto-research branch.","modelSize":"Not reported","inputs":"Robot observations, task context and persistent skills / memory; simulator and tool interfaces depend on the evaluated branch.","outputs":"Executable code policies, live agent actions, or revised learned-policy training pipelines, depending on the branch.","trainingData":"330 simulated tasks in two engines and five real-robot tasks; the paper reports over four years of aggregate experiment runtime.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Code / memory evolution in frozen-model branches; auto-research can update learned-policy training. Skill transfer and failure-memory transfer are evaluated separately.","evaluation":"330 simulation tasks, five real-robot tasks and typing / piano studies across multiple agent and policy configurations."},"release":{"code":"Partial","weights":"Not verified","data":"Not verified","license":"MIT license is visible in the placeholder repository; downstream artifacts need separate terms.","notes":"The public repository currently contains a project README / assets and says implementation is coming soon; it is not a full released system."},"evidence":[{"url":"https://arxiv.org/abs/2610.18792","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/Self-Evolving-Agentic-Robotics/SEAR","label":"Author-linked repository","fields":["links.code"],"note":"Repository identity; code and checkpoint release status are stated separately."}],"caveats":["The paper combines several distinct control paradigms; its aggregate compute budget is not training time for one policy.","Persistent memory and skills can transfer mistakes as well as successful strategies."],"verified":"2026-10-17"},{"id":"astra-robomme","name":"Astra on RoboMME","title":"Can a Frontier VLM Serve as a Robot Policy for Memory-Dependent Manipulation?","date":"2026-10-12","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A full-context frontier VLM retains the episode video and prior responses while commanding every end-effector waypoint in memory-dependent manipulation.","institution":"Harvard University, Computational Robotics Group","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.17350","project":"https://embodied-astra.github.io","code":"https://github.com/jackbjed/embodied-astra-robomme"},"policy":{"family":"Robot agent","baseModel":"GPT-6 Astra through the Responses API, frozen.","modelSize":"Not reported","inputs":"Goal, task demonstration video, subsampled episode RGB frames, prior responses, current TCP / finger state, elapsed steps and camera calibration. No depth, oracle object state or subgoal labels.","outputs":"World-frame TCP position, Euler orientation and gripper waypoints, executed by RoboMME motion planning.","trainingData":"No RoboMME-specific weight training; demonstrations and the complete retained episode supply context.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen model; full-context memory and tool-based waypoint control. Oracle-planner experiments are diagnostic conditions only.","evaluation":"All 800 RoboMME test episodes across 16 tasks; one reported run achieves 737 successes (92.1%), plus failure and oracle-interface diagnostics."},"release":{"code":"Available","weights":"Not applicable","data":"Partial","license":"MIT for the repository; external assets and APIs retain separate terms.","notes":"Public waypoint agent, configuration and RoboMME evaluation code; hosted-model API access is separate."},"evidence":[{"url":"https://arxiv.org/abs/2610.17350","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/jackbjed/embodied-astra-robomme","label":"Author-linked repository","fields":["links.code"],"note":"Repository identity; code and checkpoint release status are stated separately."},{"url":"https://github.com/jackbjed/embodied-astra-robomme","label":"Official repository README and LICENSE","fields":["release","caveats"],"note":"Implementation scope and repository license are verified separately from model access and full benchmark reproducibility."}],"caveats":["The reported run had not been validated by benchmark maintainers when the paper was published.","Waypoint control and the privileged oracle-skill diagnostic are separate protocols.","Only one full run is reported, whereas the submission template asks for three. Configuration and interface notes were informed by earlier test-split runs, so this is not an untouched held-out evaluation."],"verified":"2026-10-17"},{"id":"dexninja","name":"DexNinja","title":"DexNinja: Learning Robust Dexterous Cutting Policy with a Real-to-Sim-to-Real Data Engine","date":"2026-10-12","year":2026,"collections":["policy"],"categories":["tactile-il","rgbd-3d"],"resourceType":"policy","summary":"Builds real-to-sim-to-real dexterous cutting data with deformable simulation and tactile contact.","institution":"Tsinghua University","venue":"CoRL 2026","modalities":["RGB","Depth","Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2610.17208","project":"https://dex-ninja.github.io/","data":"https://huggingface.co/datasets/DexNinja/DexNinja_Simulated_Assets"},"release":{"code":"Not released","weights":"Not verified","data":"Access by request — simulated assets","license":"Not verified","notes":"Official project marks code Coming soon. Hugging Face lists simulated mesh/voxel/SDF/point-cloud assets with manual access approval; this does not verify downloadable demonstration trajectories."},"evidence":[{"url":"https://arxiv.org/abs/2610.17208","label":"Canonical author manuscript and arXiv submission history","fields":["title","date","institution","venue"],"note":"Date retains the initial v1 submission, independently of revisions or current artifact releases."},{"url":"https://arxiv.org/html/2610.17208v1","label":"Full author paper: method, observations and evaluation scope","fields":["summary","policy","modalities","environments","embodiments","caveats"],"note":"Physical sensors, predicted signals, training labels and policy inputs are recorded separately; undisclosed quantities remain unknown."},{"url":"https://dex-ninja.github.io/","label":"Official paper-linked project page; release review on 19 October 2026","fields":["links","release"],"note":"Current artifacts are checked separately from paper experiments. A project-only link is not proof of an implementation release."},{"url":"https://huggingface.co/datasets/DexNinja/DexNinja_Simulated_Assets","label":"Official simulated-asset inventory and gated access state","fields":["release.data","links","caveats"],"note":"Hugging Face API exposes mesh, point-cloud, Gaussian, voxel and SDF paths with gated=manual; file listing is not anonymous download access."},{"url":"https://dex-ninja.github.io/","label":"Author project venue and current code-release notice","fields":["venue","release.code"],"note":"Author BibTeX/footer specifies CoRL 2026; Code is explicitly marked Coming soon."}],"caveats":["Simulation-generated contact and topology changes should not be conflated with released real measurements.","Simulated assets are a separate gated resource; access to meshes does not establish released policy code or demonstration data."],"verified":"2026-10-19","policy":{"family":"ACT","baseModel":"Stage-conditioned ACT-style arm/hand action-chunking imitation policy trained with real demonstrations and augmented simulated episodes.","modelSize":"Not reported","inputs":"RGB-D visual observations, proprioception and effector-specific tactile tokens.","outputs":"Stage-conditioned chunks for arm/hand motion and local grasp/contact regulation.","trainingData":"Tens of real cutting demonstrations augmented into hundreds of simulated episodes; exact splits remain setting-specific.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Object reconstruction, constrained material/shape randomization, differentiable cutting simulation and behavior cloning.","evaluation":"Food slicing with DR3/DR5 arms and LinkerHand L10; embodiment-shift study with dual Franka arms and LinkerBot L20."}},{"id":"fomo-dex","name":"FOMO-Dex","title":"Force-Modulated Dexterity: Unified Compliance for Contact-Rich Manipulation","date":"2026-10-12","year":2026,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"Applies task-coordinate force modulation to teleoperation and learns policies from corrected commands.","institution":"Amazon FAR","venue":"arXiv preprint","modalities":["RGB","Tactile","Force/Torque","Proprioception"],"environments":["Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2610.17161","project":"https://yingyuan0414.github.io/fomo-dex/"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Paper and public presentation verified; no implementation, weight or data release is inferred from a publication or demonstration."},"evidence":[{"url":"https://arxiv.org/abs/2610.17161","label":"Canonical author manuscript and arXiv submission history","fields":["title","date","institution","venue"],"note":"Date retains the initial v1 submission, independently of revisions or current artifact releases."},{"url":"https://arxiv.org/html/2610.17161v1","label":"Full author paper: method, observations and evaluation scope","fields":["summary","policy","modalities","environments","embodiments","caveats"],"note":"Physical sensors, predicted signals, training labels and policy inputs are recorded separately; undisclosed quantities remain unknown."},{"url":"https://yingyuan0414.github.io/fomo-dex/","label":"Official paper-linked project page; release review on 19 October 2026","fields":["links","release"],"note":"Current artifacts are checked separately from paper experiments. A project-only link is not proof of an implementation release."}],"caveats":["Controller/user-study results and autonomous policy evaluations cover different task subsets.","Fingertip contact forces and wrist wrench retain distinct roles."],"verified":"2026-10-19","policy":{"family":"Diffusion","baseModel":"Task-frame compliance controller; DINOv2 visual encoder with diffusion imitation policy.","modelSize":"Not reported","inputs":"Head ZED RGB, robot proprioception and measured forces; autonomous policy consumes the observations rather than nominal operator commands.","outputs":"Joint-space actions imitating the force-modulated commands sent to the robot.","trainingData":"Contact-rich teleoperation for card picking, paper cutting, screwdriving and nut/bolt assembly.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Compliance controller 90 Hz; logged demonstrations 30 Hz; policy inference frequency Not reported.","trainingMethod":"Direction-selective force corrections followed by behavior cloning with proprioceptive masking.","evaluation":"Controller-force analysis, operator data-collection study and autonomous card/rotation policies."}},{"id":"labit","name":"LABIT","title":"LABIT: Long-Horizon Robotic Assembly Benchmark for Industrial Tasks","date":"2026-10-12","year":2026,"collections":["benchmark"],"categories":["precision-assembly"],"resourceType":"benchmark","summary":"Hierarchical industrial assembly with seven operation types, scoring that accounts for geometric/material/tolerance complexity, and physical plus MuJoCo implementations.","institution":"Karlsruhe University of Applied Sciences","venue":"Preprint","modalities":["RGB","Depth","Force/Torque","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.16398","project":"https://irp-hka.github.io/robotic-assembly-benchmark/","code":"https://github.com/IRP-HKA/LABIT-simulation"},"benchmark":{"simulator":"MuJoCo","simulatorVersion":"Not reported; paper gives 0.5 ms timestep, implicitfast integrator and Newton solver, which do not identify a release.","taskCount":"59 assembly primitives spanning 7 operation types and 3 ordered subassemblies; 15 representative task geometries illustrated","tasks":["Insert spring-loaded","Insert internal plug","Insert external plug","Insert rotor","Insert peg","Reorient center housing","Align gear wheel","Insert gear wheel","Reorient top housing","Screwing","Place o-ring","Align cover plate","Slide on the hose","Push clamp","Clamp together"],"taskListScope":"Complete 15 representative operations in Figure 4; repeats and grasp/alignment primitives expand to the 59-primitive sequence.","capabilities":["Hierarchical assembly","Insertion","Screwing","Press-fitting","Snapping","Clamping","Subassembly reorientation"],"metrics":"Complexity-weighted score and reliability at primitive/subtask/category/subassembly level; time and force-interaction diagnostics are secondary.","robots":"Industrial single-arm parallel-gripper setup with wrist RGB-D and F/T sensing; exact arm model Not verified in excerpt.","sensors":"Wrist RGB-D camera, wrist F/T, end-effector/joint logging. No tactile images claimed.","dataCollection":"Manually engineered force–position primitive sequences execute the assembly plan; success/failure baseline executions are recorded. A standardized training-demonstration collector is not prescribed.","data":"CAD, bill of materials, assembly/evaluation protocol and simulator; standardized demonstration count Not reported.","protocol":"Three provisioning levels: oriented feeder, unoriented plate, cluttered box. Housing fixtures and downstream sequence held fixed. 59 operations grouped into SA1 bottom housing, SA2 middle module and SA3 final integration.","baselines":"Manually engineered hybrid force-position primitives; real and simulated execution compared."},"release":{"code":"Available","weights":"Not applicable","data":"Available","license":"Not verified","notes":"Project contains CAD/material/evaluation pages and linked simulation repository; manuscript link on homepage is still a placeholder, so use arXiv directly."},"evidence":[{"url":"https://arxiv.org/abs/2610.16398","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://irp-hka.github.io/robotic-assembly-benchmark/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/IRP-HKA/LABIT-simulation","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://arxiv.org/abs/2610.16398","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Section4 manually engineered force–position baseline; recorded execution data are separate from a fixed training corpus."}],"caveats":["A 59-primitive product assembly is not 59 unrelated semantic tasks.","Other simulation engines are not directly supported by the current release."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not applicable","data":"Available","license":"Not verified","notes":"Project contains CAD/material/evaluation pages and linked simulation repository; manuscript link on homepage is still a placeholder, so use arXiv directly."}},"relatedIds":[]},{"id":"pace","name":"PACE","title":"PACE: Temporal-Role Tactile Conditioning for Chunk-Based Insertion Policies","date":"2026-10-12","year":2026,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"Assigns touch to persistent-state conditioning, contact-event correction and training-only consequence prediction for insertion policies.","institution":"Xiaomi Robotics","venue":"CoRL 2026","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.16308","project":"https://pace-insertion.github.io/"},"policy":{"family":"ACT","baseModel":"ACT CVAE-Transformer; Diffusion Policy transfer variant","modelSize":"ACT83.87M; PACE adds0.841M train/0.575M inference; DP+PACE310.5M","inputs":"Global/wrist RGB, joint state and default 6D optical-tactile force summary","outputs":"Chunked joint actions with near-term gated residual corrections","trainingData":"Five real insertion tasks: approximately 160 demonstrations per task, approximately 300 steps per episode; additional ManipulationNet geometry/clearance settings","trainingTime":"DP transfer: 8k epochs budget; converges≈epoch1100/680k steps; wall-clock not reported","trainingHardware":"DP transfer: RTX4090 or H20; inference latency measured on RTX3090","controlRate":"20 Hz control; extra tactile overhead<0.3 ms; ACT backbone6.2 ms","trainingMethod":"Phase modulation, event residual and auxiliary contact prediction","evaluation":"Five real insertion tasks; ManipulationNet six geometry-clearance settings including 0.1 mm"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."},"evidence":[{"url":"https://arxiv.org/abs/2610.16308","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://pace-insertion.github.io/","label":"Author project page","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2610.16308","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."},{"url":"https://arxiv.org/html/2610.16308v1","label":"Independent review: dataset statistics, Section4.1 and AppendixC.5","fields":["policy.trainingData"],"note":"TableC3 reports approximately 160 episodes/task and 300 steps/episode for the real-robot demonstration dataset."}],"caveats":["Default tactile input is a low-dimensional force signal; F/T and joint-current variants are separate experiments.","Future-contact predictor is removed for inference."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."}},"relatedIds":[]},{"id":"streamingwam-staircase","name":"StreamingWAM — staircase flow","title":"StreamingWAM: Streaming World Action Modeling via Chunk-Wise Staircase Flow Matching","date":"2026-10-11","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Maintains a persistent noisy action buffer and amortizes denoising across replans, using executed-action prefixes and dense visual history for continuity.","institution":"University of Chinese Academy of Sciences / Beijing Zhongguancun Academy / CAS Institute of Automation / University of Science and Technology of China","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2610.15585","project":"https://renshaojie233.github.io/StreamingWAM/","code":"https://github.com/renshaojie233/StreamingWAM","data":"https://huggingface.co/datasets/rsj2003/StreamingWAM-RealWorld","weights":"https://huggingface.co/rsj2003/StreamingWAM-LIBERO"},"policy":{"family":"World-action model","baseModel":"Wan2.2-TI2V-5B with ActionDiT; staircase action-noise schedule and prefix rollout module","modelSize":"No additional trainable parameters over the chosen WAM; full aggregate Not reported here","inputs":"RGB observation history, language, state and recently executed actions","outputs":"Persistent future-action buffer, with one Euler update per steady-state replan after multi-step warm start","trainingData":"Standard benchmark demonstrations. Physical training uses 590 trajectories across seven collection tasks, grouped into six evaluation families.","trainingTime":"Benchmark-specific extra streaming optimization; wall-clock Not reported","trainingHardware":"Training Not reported; physical inference on RTX 5090","controlRate":"Steady-state replanning takes 57.3 ms on the physical setup and 72.9 ms in simulation. Physical execution is 15 Hz. Once-per-episode initialization is timed separately.","trainingMethod":"Randomized staircase widths, prefix-conditioned denoising and dense observed-history memory; no consistency-distillation claim","evaluation":"LIBERO, LIBERO-Plus, and RoboTwin 2.0; six real task families: block stacking, open drawer, weigh-and-place, conveyor handover, car pursuit, and whack-a-mole. Official release distinguishes corrected LIBERO-Plus clean instructions:82.12% (8,237/10,030) versus legacy raw-instruction protocol79.36% (7,960/10,030), using the same verified checkpoint; training is unchanged."},"release":{"code":"Partial","weights":"Partial","data":"Available","license":"MIT original code, verified from LICENSE; retained third-party code and upstream model terms are separate.","notes":"The author release provides LIBERO-Plus training and rolling inference, a LIBERO policy checkpoint, and the 590-trajectory real-world dataset. Complete RoboTwin and physical deployment implementations are not established by this release."},"evidence":[{"url":"https://arxiv.org/abs/2610.15585","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://renshaojie233.github.io/StreamingWAM/","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."},{"url":"https://renshaojie233.github.io/StreamingWAM/","label":"Author project","fields":["links","institution","release"],"note":"Resources section scopes code to LIBERO-Plus and links the checkpoint and 590-trajectory dataset; populated Hugging Face files verified."},{"url":"https://github.com/renshaojie233/StreamingWAM/blob/main/LICENSE","label":"Code license","fields":["release.license"]},{"url":"https://github.com/renshaojie233/StreamingWAM/blob/main/docs/evaluation/README.md","label":"Official current source / artifact inventory — 21 October 2026","fields":["policy.evaluation","caveats"],"note":"Current release separates corrected/legacy instruction protocols and checkpoint identity; no new training performance is inferred."}],"caveats":["Distinct authors and method from the action-conditioned Streaming-WAM paper, arXiv: 2609.28927.","Steady-state replanning excludes the once-per-episode warm start. Speedups against ten-step baselines include different execution horizons."],"verified":"2026-10-21"},{"id":"bimanual-rma","name":"Bimanual RMA","title":"Scaling Latent Motor Adaptation to Coordinated Bimanual Manipulation","date":"2026-10-10","year":2026,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Latent adaptation inferred from wrist depth and interaction history supports contact-rich dual-arm control.","institution":"University of Oxford; Technical University of Munich; Toyota Motor Europe","venue":"IROS 2026 Bimanual Manipulation Workshop","modalities":["Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.14833"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2610.14833","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."}],"caveats":["Privileged object parameters are teacher-training information, not deployment observations.","Long-horizon stacking remains limited for all compared methods."],"verified":"2026-10-17","policy":{"family":"RL / sim-to-real","baseModel":"Privileged PPO teacher with dynamics latent, depth/history adaptation student and bimanual motor policy.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Dual wrist depth images, observation-action history and proprioception.","outputs":"Coordinated bimanual motor actions.","trainingMethod":"Privileged dynamics training followed by history/depth latent adaptation and sim-to-real.","trainingData":"Randomized YCB object mass/friction/scale/geometry in ManiSkill.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Three simulated bimanual tasks; zero-shot dual-Franka lifting experiment.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"deft-vla","name":"DEFT-VLA","title":"DEFT-VLA: Stage-Localized Force Specialization for Contact-Rich Robotic Manipulation","date":"2026-10-10","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Specializes a contact expert while preserving a separate vision-dominated non-contact expert.","institution":"University of Science and Technology of China","venue":"arXiv preprint","modalities":["RGB","Force/Torque","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2610.14854"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Paper and public presentation verified; no implementation, weight or data release is inferred from a publication or demonstration."},"evidence":[{"url":"https://arxiv.org/abs/2610.14854","label":"Canonical author manuscript and arXiv submission history","fields":["title","date","institution","venue"],"note":"Date retains the initial v1 submission, independently of revisions or current artifact releases."},{"url":"https://arxiv.org/html/2610.14854v1","label":"Full author paper: method, observations and evaluation scope","fields":["summary","policy","modalities","environments","embodiments","caveats"],"note":"Physical sensors, predicted signals, training labels and policy inputs are recorded separately; undisclosed quantities remain unknown."}],"caveats":["Force is measured by wrist F/T sensors, not tactile skin.","RGB-D camera hardware does not establish depth as a policy observation."],"verified":"2026-10-19","policy":{"family":"VLA","baseModel":"pi0.5 shared vision-language prefix with independent base/contact action experts and stage routing.","modelSize":"Not reported","inputs":"Three camera RGB views, instruction and robot state; bilateral wrist six-axis F/T for contact specialization.","outputs":"Stage-routed manipulation action chunks.","trainingData":"Task demonstrations segmented into contact and non-contact stages for pipette attachment, bimanual aspiration and whiteboard wiping.","trainingTime":"Contact experts / applicable stage estimators: 30,000 steps, batch size 8; wall-clock Not reported.","trainingHardware":"Not reported","controlRate":"Mercury B2 robot operates at 15 Hz.","trainingMethod":"Task adaptation, contact-localized expert training and force-attending curriculum with visual suppression/state masking.","evaluation":"Three real-robot contact-rich tasks and held-out grasp heights."}},{"id":"dexbiolab","name":"DexBioLab","title":"DexBioLab: Benchmarking Dexterous Manipulation with Dynamic Experimental Phenomena in Realistic Laboratories","date":"2026-10-09","year":2026,"collections":["benchmark"],"categories":["dexterous-benchmarks","precision-assembly"],"resourceType":"benchmark","summary":"Laboratory dexterity benchmark with 14 tool/instrument tasks and controllable dynamic visual phenomena, using hybrid Gaussian-background rendering.","institution":"Shanghai AI Laboratory","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand","Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2610.13643","project":"https://cumulusalpha.github.io/DexBioLabWebPage/","code":"https://github.com/CumulusAlpha/DexBioLab"},"benchmark":{"simulator":"mjlab / MuJoCo Warp with Gaussian-splatting backgrounds","simulatorVersion":"Exact mjlab / MuJoCo Warp release Not reported in retrieved manuscript.","taskCount":"14 tasks across 4 robot embodiments; three visual experimental phenomena and six laboratory backgrounds","tasks":["Close Centrifuge Lid","Load Centrifuge","Transfer Tube (Shadow)","Ignite Bunsen Burner","Loosen Tube Cap","Attach Pipette Tip","Aspirate with Pipette","Manipulate Tweezers","Transfer Tube (XHand)","Tighten Tube Cap","Heat & Stir Agar Solution","Open Thermal Cycler Lid","Close Thermal Cycler Lid","Operate Mixer Panel"],"taskListScope":"Complete fourteen-task Figure 2 labels in Oct 14 v3.","capabilities":["Precision tool manipulation","Instrument operation","Visual grounding","Dynamic visual distractors"],"metrics":"Task success; progressive score for Operate Mixer Panel / real tube-transfer validation; three seeds ×100 rollouts for simulation comparisons.","robots":"UR5e and ALOHA arms; Shadow, XHand and Wuji hand configurations yield four evaluated embodiments.","sensors":"RGB from wrist/third-person viewpoints, proprioception and language; physical UR5e-XHand validation uses RealSense D405 wrist and D435 scene cameras. Depth/tactile policy inputs not established.","dataCollection":"Scripted Parallel Finite-State Machine (PFSM) experts with end-effector waypoints and hand targets; batched simulation and decoupled replay generate observations.","data":"1,400+ collected trajectories; parallel state-machine generation and demonstration replay. Real transfer experiment mixes 10 real +90 simulated demonstrations per condition.","protocol":"Shared task/model interface; compare Gaussian vs standard backgrounds. Physical Transfer Tube XHand: 10 initial positions ×2 trials per condition, same task scoring.","baselines":"ACT / diffusion / VLA families in paper; per-task table distinguishes 13 simulation tasks from physical-transfer validation."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Not reported","notes":"Official simulation, scripted data generation, LeRobot rendering and policy evaluation code now verified; pretrained data/model download not established."},"evidence":[{"url":"https://arxiv.org/abs/2610.13643","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://cumulusalpha.github.io/DexBioLabWebPage/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/CumulusAlpha/DexBioLab","label":"Official source","fields":["release.code","benchmark"],"note":"Official current code linked by project, README read."},{"url":"https://arxiv.org/abs/2610.13643","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["arXiv v1 was titled AutoBio+; v3 (Oct 14 2026) renamed the work DexBioLab. This is one canonical work.","Dynamic phenomena are rendered observations, not a claim of experimentally validated chemical/biological process simulation."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Not reported","notes":"Official simulation, scripted data generation, LeRobot rendering and policy evaluation code now verified; pretrained data/model download not established."}},"relatedIds":[]},{"id":"coap","name":"Code-Only-as-Policy (COAP)","title":"Embodied Turing Machines: Stateful Code for Robot Recursive Self-Improvement","date":"2026-10-08","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Offline coding-agent improvement produces a reusable library that measures state and executes stateful robot programs. Deployment runs the resulting code without an agent in the control loop.","institution":"Nanyang Technological University; Ropedia","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.12369"},"policy":{"family":"Robot agent","baseModel":"Opus 5.5 Max coding agent during development; no foundation model is required in the deployed control loop.","modelSize":"Not reported","inputs":"Deployment code reads RGB observations, proprioception and geometric state computed from observations.","outputs":"Stateful executable control programs with branches, recovery logic and shared-library calls that issue robot actions.","trainingData":"No task-specific neural weight training; agents revise and validate code on development layouts.","trainingTime":"The paper reports a 15-hour software-development example for new tasks; it is not GPU model-training time.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen development model; source-code revision and offline validation. The deployed policy is code-only.","evaluation":"RoboDojo: 42 tasks, reported 70.24% success under the paper protocol; additional transfer / new-task experiments."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"Uses a hosted or pre-existing frozen model; the paper introduces a robot interface or agent system rather than new base-model weights."},"evidence":[{"url":"https://arxiv.org/abs/2610.12369","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["Code-only execution depends on the published state-measurement and controller assumptions.","Cross-paper leaderboard comparisons are not necessarily paired evaluations with identical development access."],"verified":"2026-10-17"},{"id":"how-firm-grasp","name":"How Firm Should a Grasp Be?","title":"How Firm Should a Grasp Be?","date":"2026-10-08","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Material-property estimation from evolving visuotactile contact selects the minimum grasp force required for gentle lifting.","institution":"Columbia University","venue":"Preprint","modalities":["Tactile","Force/Torque","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.11221","project":"https://cave.cs.columbia.edu/projects/categories/project?cid=Physics-Based+Vision&pid=How+Firm+Should+a+Grasp+Be"},"policy":{"family":"Hybrid","baseModel":"Sequence model trained on IPC/UIPC grasp simulations; material-aware force controller.","modelSize":"Not reported","inputs":"Tactile images and load-cell/contact force history with object class priors.","outputs":"Estimated mass, elasticity/friction parameters and minimum grasp-force command.","trainingData":"Squash:270 measured fruit/vegetable samples across 52 classes;200 k simulated pickups with property augmentation.","trainingTime":"Approximately 48 h,500 epochs.","trainingHardware":"8 NVIDIA RTX6000Ada Generation GPUs.","controlRate":"250 Hz material-aware force control.","trainingMethod":"Simulated haptic sequence learning for 500 epochs then online property-based force control.","evaluation":"UR5/WSG50-110/Gel Sight gentle grasping of real fruits and vegetables."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2610.11221","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://cave.cs.columbia.edu/projects/categories/project?cid=Physics-Based+Vision&pid=How+Firm+Should+a+Grasp+Be","label":"Official author project","fields":["links","release"],"note":"Live project artifact links inspected on2026-10-17."}],"caveats":["Object class priors and simulated material augmentations are part of the method; notan unrestricted unknown-material guarantee."],"verified":"2026-10-17"},{"id":"pantact","name":"PanTact","title":"PanTact: Teaching Whole-Body Dexterity with a Wearable Contact Interface","date":"2026-10-08","year":2026,"collections":["policy"],"categories":["tactile-il","tactile-foundation","rgbd-3d"],"resourceType":"policy","summary":"Human body-contact supervision trains contact-predictive whole-body manipulation without additional robot body-touch sensors.","institution":"Stanford University / Amazon Frontier AI & Robotics / Carnegie Mellon University","venue":"Preprint","modalities":["RGB","Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Humanoid","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2610.12558","project":"https://pantact.github.io/"},"policy":{"family":"Diffusion","baseModel":"DINOv3/DiT diffusion policy and contact-aware IK or RL sparse tracker.","modelSize":"Not reported","inputs":"Masked egocentric 3D pointmap, wrist RGB and bimanual end-effector state history; human tactile patches supervise contact goals during training.","outputs":"Head look-at point, bimanual EEF/gripper trajectories and world-frame body contact goals; tracker outputs 29 joint targets.","trainingData":"100 bulky object+small object demos,150 sweep/cabinet demos,110 carry/push demos in distinct real cohorts.","trainingTime":"500 policy epochs; wall-clock time Not reported.","trainingHardware":"Not reported","controlRate":"Policy 10 Hz, tracker 50 Hz; recording 60 Hz, training subsampling 20 Hz.","trainingMethod":"Diffusion imitation from robot-free wearable demos; contact-aware IK / sim-trained tracking controller executes goals.","evaluation":"Humanoid upper-body support, arm sweeping, knee/body pushing and carrying during locomotion."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2610.12558","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://pantact.github.io/","label":"Official author project","fields":["links","release"],"note":"Live artifact status reviewed separately from paper claims."}],"caveats":["Body tactile patches record human training contact; deployed robot realizes predicted contact without additional body sensors.","Egocentric pointmaps use ZED stereo reconstructed depth. Head LiDAR/IMU support SuperOdometry frame localization; this is separate from the policy depth observation."],"verified":"2026-10-17"},{"id":"roborsi","name":"RoboRSI (Noematrix)","title":"RoboRSI: Stable, efficient, and reusable robot self-evolution in complex real-world environments","date":"2026-10-08","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A multi-agent skill hierarchy uses Top-down Skill Refinement to diagnose execution failures and revise reusable base, atomic and compound skills.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Mobile manipulator","Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2610.12424","project":"https://lab.noematrix.ai/blog/2-roborsi/","code":"https://github.com/nssmd/RoboRSI"},"policy":{"family":"Robot agent","baseModel":"Manager, Planner, Engineer and Reviewer agents with the paper's configured foundation-model and robot-skill components.","modelSize":"Not reported","inputs":"Task objectives, observations, tool traces, skill contracts and human objectives / corrections where supplied.","outputs":"Hierarchical execution plans, skill calls and tested skill revisions retained for subsequent iterations.","trainingData":"104 rounds of real mobile-cleanup evolution, plus simulated manipulation experiments; no general base-model retraining is claimed.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen agent-model reasoning with hierarchical skill / code revision and verification. Human goals and corrections remain part of the reported loop.","evaluation":"Real-world mobile cleanup and simulation on LIBERO, LIBERO-Pro, LIBERO-Plus and RoboTwin."},"release":{"code":"Available","weights":"Not applicable","data":"Not verified","license":"Apache-2.0 for repository code, verified from LICENSE; external models, simulator assets and datasets retain separate terms.","notes":"Public agent, hierarchical skill-evolution and evaluation implementations are present, including LIBERO/PRO/Plus reproduction scripts, RoboTwin integration and robot-interface guides. Upstream LIBERO-Pro assets are dependencies, not a newly released RoboRSI training corpus; no separate learned task-policy checkpoint was verified."},"evidence":[{"url":"https://arxiv.org/abs/2610.12424","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/nssmd/RoboRSI","label":"Author-linked repository","fields":["links.code"],"note":"Repository identity; code and checkpoint release status are stated separately."},{"url":"https://github.com/nssmd/RoboRSI","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["Distinct from the OpenSenseNova / SenseNova-RoboRSI repository.","The real-world evolution includes human objectives and corrections, so the system is not fully unsupervised."],"verified":"2026-10-19"},{"id":"secure-gentle-grasping","name":"Secure and Gentle Grasping","title":"Tactile Sensing-based Secure and Gentle Grasping of Unknown Objects","date":"2026-10-08","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Online dynamics learning and constrained predictive control regulate secure, gentle grasps of unknown objects.","institution":"New York Institute of Technology","venue":"Preprint","modalities":["Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.12790"},"policy":{"family":"Model-based planning","baseModel":"GP tactile calibration, finite-state force probing, online interaction dynamics and predictive controller.","modelSize":"Not reported","inputs":"Two single-channel FSR tactile sensors and gripper interaction history; no vision.","outputs":"Gripper motion/force control under identified slip/damage constraints.","trainingData":"Offline tactile calibration data; task interaction dynamics identified online, no pretrained task policy corpus.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"100 Hz real-time control.","trainingMethod":"GP sensor calibration plus online object constraint/dynamics identification and predictive control.","evaluation":"Unknown rigid/deformable objects, including raw tofu and metal blocks; slip prevention and structural protection."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2610.12790","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."}],"caveats":["No offline task-policy training does not mean no sensor calibration:the GP calibration model uses offline measurements."],"verified":"2026-10-17"},{"id":"tacross","name":"TACROSS","title":"TACROSS: An Efficient and Low-Cost Scalable Human Touch System Across Heterogeneous Tactile Sensors for Dexterous Robot Learning","date":"2026-10-08","year":2026,"collections":["policy"],"categories":["tactile-il","tactile-foundation"],"resourceType":"policy","summary":"Contact-event semantic alignment transfers heterogeneous human-glove touch into robot tactile-conditioned imitation.","institution":"University of Hong Kong / INFIFORCE / CUHK (Shenzhen) and collaborators","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2610.11945","project":"https://tacross-touch-project.github.io/"},"policy":{"family":"ACT","baseModel":"Domain canonicalizers, shared temporal/finger transformers and ACT.","modelSize":"Not reported","inputs":"Three RGB views, robot state and robot-domain contact-semantic tactile latent.","outputs":"60-action chunks of 8D arm/open close or 13D arm/six hand joint targets.","trainingData":"Recorded corpus 183.95 h/24,913 episodes, 22,486 valid; specific policy comparisons use smaller separately reported human/robot subsets.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"ACT15 Hz; robot touch 30 Hz; glove hardware~150 Hz.","trainingMethod":"Human–robot contact-event alignment; robot-ground-truth actions plus confidence-masked human hand pseudo-labels; encoder initially frozen 20k steps.","evaluation":"Real contact-rich dexterous manipulation across heterogeneous tactile sensors."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2610.11945","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://tacross-touch-project.github.io/","label":"Official author project","fields":["links","release"],"note":"Live project artifact links inspected on2026-10-17."}],"caveats":["285 physical glove locations differ from 256 streamed values.","Human hand retargets are pseudo-labels, not ground-truth robot arm actions.","Recorded corpus and valid subset are not all used in every policy comparison."],"verified":"2026-10-17"},{"id":"unitas","name":"UNITAS","title":"UNITAS: A 3D-Native World Action Model for Embodied Manipulation","date":"2026-10-08","year":2026,"collections":["policy"],"categories":["rgbd-3d","generalist-foundation"],"resourceType":"policy","summary":"Models observed geometry, action flow and scene flow in a common metric 3D frame, supporting executable actions and action-conditioned scene prediction.","institution":"DexForce / Chinese University of Hong Kong, Shenzhen","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2610.12099","project":"https://unitas-wam.github.io/","code":"https://github.com/DexForce/UNITAS","weights":"https://huggingface.co/RobbinWang123/Unitas"},"policy":{"family":"World-action model","baseModel":"Florence-2 vision-language backbone, point-dynamics and action experts, and physical-time trajectory tokenizer.","modelSize":"1.7B reported policy.","inputs":"RGB, language, state and calibration, with optional observed depth. Scene prediction requires depth or supplied 3D query coordinates.","outputs":"Executable action chunks, with optional action flow and action-conditioned scene flow; different modes have different costs.","trainingData":"Nine sources: RoboTwin, LIBERO, VLABench, DROID, AgiBot, RoboMIND, RH20T, VITRA and W1. Exact aggregate trajectory count not reported.","trainingTime":"Full elapsed duration not reported.","trainingHardware":"Training hardware not reported; latency on one RTX 5090D.","controlRate":"55.77 ms per chunk for actions only; 122.51 ms with action flow; 305.49 ms with 1,024 scene queries. These are separate output modes.","trainingMethod":"Tokenizer reconstruction followed by rectified-flow experts; common metric frame, physical time and with/without mixed-pretraining controls.","evaluation":"LIBERO, LIBERO-Plus, RoboTwin 2.0, VLABench, physical W1 tasks and action-conditioned 3D prediction."},"release":{"code":"Available","weights":"Available","data":"Not released","license":"Apache-2.0 code; weights/data/backbone terms separate","notes":"Current author repository provides code and checkpoint instructions; dataset badge still says Coming soon."},"evidence":[{"url":"https://arxiv.org/abs/2610.12099","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/DexForce/UNITAS","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://unitas-wam.github.io/","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."}],"caveats":["Depth-enabled and no-depth variants differ; scene prediction needs 3D query coordinates.","Action-only latency excludes generating both flows."],"verified":"2026-10-17"},{"id":"agentic-rsr","name":"Agentic RSR","title":"Agentic RSR: Real-to-Sim-to-Real through Scene Reconstruction and Execution-Grounded Robot Policies","date":"2026-10-07","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"An agent reconstructs an executable scene from real video, develops policy code through simulation feedback, and transfers code plus experience memory to physical execution.","institution":"Sun Yat-sen University","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.10479"},"release":{"code":"Not released","weights":"Not applicable","data":"Not released","license":"Not verified","notes":"Paper announces future code and reconstructed-scene data; no author implementation or dataset download was established."},"evidence":[{"url":"https://arxiv.org/html/2610.10479","label":"Original full paper, methods and evaluation","fields":["title","date","institution","summary","modalities","policy","model","benchmark","benchmark.dataCollection","caveats"],"note":"Publication date is arXiv v1; implementation and deployment observations are distinguished from training supervision, generated geometry and base-model identity."}],"caveats":["Scene reconstruction uses RGB-video-estimated depth aligned to known robot URDF geometry, not a measured reconstruction depth stream.","Policy-facing deployment independently includes wrist RGB-D/SAM3 object-position estimates. Privileged object poses are temporary simulation development inputs.","Reference depths in reconstruction metrics are estimates, not sensor ground-truth geometry."],"verified":"2026-10-21","policy":{"family":"Robot agent","baseModel":"Claude Opus 5.5 through DeepSeek Harness in latest v2; Codex comparison and historical reconstruction results are separate.","modelSize":"Not reported","inputs":"Workspace video/known robot model for reconstruction; task, wrist/third-person images, wrist RGB-D-derived object positions and robot state for execution.","outputs":"MuJoCo scene/model revisions and executable feedback-driven end-effector/gripper policy programs.","trainingMethod":"Agent revises scene, then three-pass policy development moves from privileged pose access to visual observations/randomized simulation; transfer code/memory to real robot.","trainingData":"18 reconstructed scenes on 2 robots plus simulation/physical task trials; no robot-weight fine-tuning claimed.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","evaluation":"Latest v2 joint scene checks, reconstruction quality and real/sim policy execution. Physical aggregate success is reported as 80% of simulation success, not 80% absolute task success."}},{"id":"embodiedrsi","name":"EmbodiedRSI","title":"EmbodiedRSI: Active Continual Robot Learning Through Hypothesis-Guided Co-Evolution","date":"2026-10-07","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A frozen robot foundation model is supported by a fast hypothesis-guided experimental loop and slower memory consolidation that co-evolve code and skills.","institution":"Columbia University; Princeton University","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.10498","project":"https://geeksongs.github.io/agentic_robotics/"},"policy":{"family":"Hybrid","baseModel":"Frozen robot foundation-model executor with code / skill agents; exact component checkpoints are study-specific.","modelSize":"Not reported","inputs":"Task, current observations, execution outcomes, hypothesis graph and hierarchical memories.","outputs":"Selected physical experiments, executable control-code changes and reusable skills composed around the frozen robot executor.","trainingData":"Physical or simulated experiments provide evidence for code / skill hypotheses; no foundation-model weight fine-tuning in the described loop.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen executor; value-of-information experiment selection updates a Hypothesis Graph, code–skill relations and retained memories.","evaluation":"RoboCasa365 and LIBERO-Pro, with separate evolution / evaluation seeds and real-world transfer experiments."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2610.10498","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["This is Song et al.'s arXiv: 2610.10498, distinct from Einsia's EmbodiedEvo arXiv: 2610.18867. The project page also uses EmbodiedEvo branding.","Published success rates belong to the paper's task subsets and adaptation protocol."],"verified":"2026-10-17"},{"id":"face-contact-factorization","name":"FACE","title":"Contact-Aware Imitation Learning Through Contact Factorization","date":"2026-10-07","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Contact-relative force factorization preserves learned behavior while online normal/friction estimation adapts its physical realization.","institution":"Kyung Hee University / Ewha Womans University / University of Central Florida","venue":"Preprint","modalities":["RGB","Force/Torque","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.09533","project":"https://rcilab.khu.ac.kr/face"},"policy":{"family":"Flow matching","baseModel":"Flow-matching imitation policy; learned GRU contact normal estimator and scalar Kal man friction filter.","modelSize":"Not reported","inputs":"RGB, proprioceptive motion and measured six-axis wrench; online estimated contact normal/friction scale.","outputs":"Normalized motion/force actions decoded into hybrid controller references.","trainingData":"200 human handheld demonstrations per object category on nominal paper-cup/egg instances; unseen friction/geometry conditions only at deployment.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"RGB/pose 30 Hz; wrist wrench 500 Hz retained for online contact processing.","trainingMethod":"Supervised policy/normal estimator learning; online contact factor estimation without task policy weight updates.","evaluation":"Franka FR3 marking an S on fragile paper cups/eggs with unseen geometries and friction conditions."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2610.09533","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://rcilab.khu.ac.kr/face","label":"Official author project","fields":["links","release"],"note":"Live project artifact links inspected on2026-10-17."}],"caveats":["Six-axis wrench is not skin touch; geometry-derived normal supervision is training only.","Online friction estimation does not imply online reinforcement learning."],"verified":"2026-10-17"},{"id":"factorized-tactile-control","name":"Factorized Tactile Representation and Control","title":"Factorized Tactile Representation and Control for Sim-to-Real Manipulation","date":"2026-10-07","year":2026,"collections":["policy"],"categories":["tactile-foundation","visual-precision-rl"],"resourceType":"policy","summary":"Separated contact geometry, force distribution and temporal-change representations enable mask-configurable tactile sim-to-real control.","institution":"Amazon Fulfillment Technologies & Robotics / University of Texas at Austin","venue":"Preprint","modalities":["Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.10510"},"policy":{"family":"RL","baseModel":"Tactile Gated Policy with recurrent PPO/LSTM actor and asymmetric privileged critic.","modelSize":"Not reported","inputs":"Deployment-available tactile/proprioceptive observations with separately masked tactile factor channels.","outputs":"Force-regulation and peg-insertion robot commands.","trainingData":"Calibration 280 localized pokes, 45 edge grasps, 20 whole-pad grasps (~29 min); GPU-parallel simulated policy learning.","trainingTime":"Not reported","trainingHardware":"4096 environments/A100reported for simulator timing; total training GPU count Not reported.","controlRate":"10 Hz policy;500 Hz sensor sampling;12 physics steps/policy step.","trainingMethod":"Sensor-response fitting and representation-specific randomization; recurrent PPO policy transfer.","evaluation":"UR5e/Robotiq 2 F-85 with two 7×4 TSF-85 capacitive pads: force regulation and adversarial peg insertion."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2610.10510","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."}],"caveats":["IsaacSim 5.1.0/IsaacLab 2.3.2 explicitly reported; actor excludes privileged simulation critic state.","A100figure characterizes timing/parallelism, not a reported training cluster budget.","Paper explicitly identifies this work as performed at Amazon and not associated with Northeastern University, despite a concurrent author appointment.","Paper explicitly identifies this work as performed at Amazon and not associated with Northeastern University, despite a concurrent author appointment."],"verified":"2026-10-17"},{"id":"openvitac","name":"OpenViTac","title":"OpenViTac: Learning and Benchmarking Visuo-Tactile Policies in a Unified Sim-and-Real Framework","date":"2026-10-07","year":2026,"collections":["benchmark","policy"],"categories":["tactile-benchmarks","precision-assembly","tactile-foundation"],"resourceType":"benchmark","summary":"Paired simulation and physical evaluation across property perception, fragility, sustained contact and precision insertion. The study compares VLA, world-action and tactile-enabled policies.","institution":"Fudan University","venue":"Preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.10384","project":"https://fvl-repo.github.io/OpenViTac/","code":"https://github.com/FVL-Repo/OpenViTac"},"benchmark":{"simulator":"NVIDIA Isaac Sim / Isaac Lab with modified TacEx / UIPC","simulatorVersion":"Paper Appendix B.2: Isaac Sim 5.1.0 / Isaac Lab 2.3.0. Current main README installer: Isaac Sim 4.5.0 / Isaac Lab 2.1.1. These are different scopes.","taskCount":"11 simulation tasks; 8 paired physical tasks","tasks":["Weight classification","Hardness classification","Roughness classification","Roughness-guided regrasp","Empty-can selection","Grasp chip","Gear assembly","Pull drawer","Insert USB","Insert block v1","Insert block v2"],"taskListScope":"Complete 11-task paper simulation suite; real subset excludes weight classification, hardness classification and roughness-guided regrasp.","capabilities":["Physical property perception","Fragility-aware manipulation","Contact-rich manipulation","Precision insertion"],"metrics":"Task-specific binary success; average is the unweighted task mean; sim-real ranking correlation; sim-real co-training comparisons.","robots":"Corresponding sim-real arms; Franka Hand and Robotiq gripper configurations. Arm model is not resolved from the public excerpt.","sensors":"GelSight, Xense and Neote tactile configurations. GelSight/Xense export tactile RGB, marker motion, depth and pose; Neote exports gel-particle or dense force fields. Exported tactile depth is not an external metric-depth policy input.","dataCollection":"Simulation and physical expert demonstrations are reported, but the collection controller and teleoperation interface are Not reported in the inspected manuscript.","data":"Per tactile configuration: 1,300 simulation episodes across 11 tasks; 2,400 real demonstrations across 8 tasks. Training uses 100/task except Insert Block v2 with 300. Public dataset download links still marked coming soon.","protocol":"Separate single-task fine-tuning; predefined balanced task conditions; task variants evaluated separately then aggregated. Co-training keeps 100 real demonstrations fixed and adds 250 or 500 simulated demonstrations for Insert USB / Gear assembly.","baselines":"π0.5, StarVLA (Qwen3), InternVLA-A1.5, X-VLA, Lingbot-VLA-2, Xiaomi-Robotics; FastWAM, Lingbot-VA, StarVLA (Cosmos); FTP-1; OpenVTLA."},"release":{"code":"Available","weights":"Not released","data":"Not released","license":"Not verified","notes":"Code and task workflows public. Project/repository mark dataset and model links coming soon; do not interpret paper data statistics as downloadable release. Policy release: Official project/repository exists; complete policy checkpoint and dataset release scope not verified."},"evidence":[{"url":"https://arxiv.org/abs/2610.10384","label":"Paper","fields":["title","date","benchmark","policy","modalities","environments","embodiments"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://fvl-repo.github.io/OpenViTac/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/FVL-Repo/OpenViTac","label":"Official repository","fields":["release","benchmark.simulatorVersion","links"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://arxiv.org/html/2610.10384","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."},{"url":"https://github.com/FVL-Repo/OpenViTac/blob/main/policy/openpi/README.md","label":"Official OpenPI vision/tactile client specification","fields":["release","policy.inputs","policy.outputs"],"note":"Client observation/action schema and remote-server dependency. Main README OpenVTLA section states that model and dataset release links will be added when available."},{"url":"https://arxiv.org/abs/2610.10384","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Paper simulator stack differs from current main installer; compare runs only with a recorded matching stack.","Repository includes additional scenarios and an eight-task default development collection list; neither is the 11-task paper benchmark.","Sim-real correlations differ between paper and homepage snapshots; use the manuscript protocol and domain subset explicitly.","Related benchmark is OpenViTac; policy and benchmark are separate catalog entries.","Pretrained temporal encoding+early token fusion is an empirical result for this benchmark, not a universal fusion rule."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not released","data":"Not released","license":"Not verified","notes":"Code and task workflows public. Project/repository mark dataset and model links coming soon; do not interpret paper data statistics as downloadable release."},"policy":{"code":"Partial","weights":"Not released","data":"Not released","license":"Not verified","notes":"Public benchmark repository includes the OpenPI vision/tactile client and deployment configurations; remote evaluation requires a separate model server and checkpoints. Official README explicitly marks model and dataset release links as forthcoming; this client adapter is not a complete released OpenVTLA training/checkpoint package."}},"policy":{"family":"VLA","baseModel":"π0.5 with AnyTouch2 temporally aware tactile encoder","modelSize":"Not reported","inputs":"RGB, language, robot state and tactile-image history","outputs":"Flow-matching manipulation action chunks","trainingData":"OpenViTac sim/real demonstrations; co-training study fixes100 real demos and adds250/500 simulated demos per task","trainingTime":"20k fine-tuning updates, global batch 64; wall-clock not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Task adaptation with temporal tactile tokens concatenated into VLM input; optional sim–real co-training","evaluation":"OpenViTac11 simulation/8 real tasks; co-training on Insert USB and Gear Assembly"},"relatedIds":[],"policyName":"OpenVTLA","benchmarkName":"OpenViTac","benchmarkSummary":"Paired simulation and physical evaluation across property perception, fragility, sustained contact and precision insertion. The study compares VLA, world-action and tactile-enabled policies.","policySummary":"Adapts a pretrained VLA using temporally aware tactile encoding and token-level tactile integration."},{"id":"tachair","name":"TacHair","title":"TacHair: Tactile Contact-Distribution Guided Online Correction for Robotic Hair Stroking and Perception","date":"2026-10-07","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"A visual/tactile imitation policy and separate contact-distribution residual module recenter deformable hair during stroking.","institution":"King's College London","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.10637","project":"https://tachair.github.io/"},"policy":{"family":"Hybrid","baseModel":"Nominal visuotactile policy; Gel Sight contact-distribution predictor and separately trained residual correction.","modelSize":"Not reported","inputs":"Third-person RGB, Gel Sight Mini tactile images and robot state.","outputs":"Nominal stroking actions with local contact deviation corrections.","trainingData":"97 nominal demos/7,002 frames;1,045 manually annotated tactile images;75 dedicated recentering demos.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Separate nominal policy, contact predictor and residual imitation training.","evaluation":"SO101quantitative experiments across five head forms and three hair conditions; UR5eadditionalvalidationwithseparatelytrainedpolicies."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2610.10637","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://tachair.github.io/","label":"Official author project","fields":["links","release"],"note":"Live artifact status reviewed separately from paper claims."}],"caveats":["SO101toUR5 e results use separately trained policies, not zero-shot cross-embodiment transfer."],"verified":"2026-10-17"},{"id":"vpp2","name":"Video Prediction Policy 2 (VPP2)","title":"Video Prediction Policy 2: Predict Better, Act Better","date":"2026-10-07","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Uses event-level manipulation video training, fixed-horizon distillation and an action expert conditioned on future-video latents.","institution":"Robotera / Tsinghua University / HKUST(GZ)","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2610.10270","project":"https://robert-gyj.github.io/video-prediction-policy-2/","code":"https://github.com/roboterax/video-prediction-policy-2","weights":"https://huggingface.co/Haodong082399/VPP2"},"policy":{"family":"World-action model","baseModel":"Wan2.1-I2V-14B video backbone; mixture-of-transformers action expert","modelSize":"Paper: 14B video backbone + 0.9B action expert; released RoboDojo checkpoint instead labels Action2B; full aggregate not reported","inputs":"RGB observation/history and task text; released RoboDojo runtime additionally normalizes current 16D EE/gripper state","outputs":"2-second action chunks and future-video latents; released RoboDojo 32 × 16D EE chunks with 24-step execution before replanning","trainingData":"Robot/human/general video mixture including OXE, DROID, RoboMIND, AgiBot, EgoDex, Ego4D; processed aggregate scale not reported","trainingTime":"Video continuation 30k updates, batch 1024; released RoboDojo video 10k then joint 100k, batch 288; wall-clock not reported","trainingHardware":"Not reported; available launchers allow distributed configurations without identifying the paper hardware","controlRate":"Paper video ≈ 0.12 s + action ≈ 0.1 s = ≈ 0.22 s/chunk with BF16/torch.compile; hardware and true feedback rate not disclosed","trainingMethod":"Event video continuation → fixed-horizon video post-training/consistency distillation → MoT action learning; optional VLM subtask planner","evaluation":"Real zero-shot ALOHA, LIBERO/Pro/OOD and RoboDojo simulation; separate high-level planning variants"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"MIT code; bundled third-party and model/data terms separate","notes":"Released video and action checkpoint pairs include RoboDojo joint2b_s100000; Stage 1/2 video checkpoints also public."},"evidence":[{"url":"https://arxiv.org/abs/2610.10270","label":"Primary manuscript and submission history","fields":["date","title","policy","modalities","environments","embodiments"],"note":"Publication date is first arXiv submission; quantitative details retain the reported setting."},{"url":"https://github.com/roboterax/video-prediction-policy-2","label":"Official model repository","fields":["release","links","policy.inputs","policy.outputs"],"note":"Deployment observations, released checkpoint versions and paper claims are checked separately."},{"url":"https://arxiv.org/html/2610.10270v3","label":"Latest inspected manuscript: Sections3–4","fields":["policy","imageCandidates"],"note":"Date retains v1Oct7; implementation facts use v3Oct13."},{"url":"https://github.com/roboterax/video-prediction-policy-2/blob/main/src/vpp2/policy.py","label":"Official deployment policy","fields":["policy.inputs","policy.outputs"],"note":"_normalize_state and _infer_action_chunk encode16D currentstate; do not infer input modalities from benchmark sensor availability."},{"url":"https://github.com/roboterax/video-prediction-policy-2/blob/main/docs/robodojo.md","label":"Official released RoboDojo recipe","fields":["policy.modelSize","policy.trainingTime","policy.outputs","release"],"note":"Action2B, video10k + joint100k/globalbatch288; released checkpoint scope differs from paper-general description."},{"url":"https://raw.githubusercontent.com/roboterax/video-prediction-policy-2/main/LICENSE","label":"Code license","fields":["release.license"],"note":"ActualMITtext; upstream copyright name is retained, not substituted."}],"caveats":["The published0.9B action expert and released Action2B checkpoint are different settings.","Video training/model release does not by itself imply public access to the complete curated corpus.","VPP2 and VPP2-Preview are listed separately on RoboDojo; do not merge their leaderboard scores."],"verified":"2026-10-17"},{"id":"depthworld","name":"DepthWorld","title":"DepthWorld: 3D World Model for Robot Manipulation","date":"2026-10-06","year":2026,"collections":["policy"],"categories":["geometry-models","rgbd-3d"],"resourceType":"model","summary":"An action-conditioned robot world model jointly predicts multi-view RGB and metric depth using recalibrated DROID-3D supervision.","institution":"Czech Technical University in Prague, CIIRC","venue":"CoRL 2026","modalities":["RGB","Depth","Proprioception"],"environments":["Real-world recordings"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2610.08780","project":"https://www.jaibardhan.com/depthworld","code":"https://github.com/Jai2500/depthworld","weights":"https://huggingface.co/jaibrdhn/depthworld","data":"https://huggingface.co/datasets/jaibrdhn/droid_3d_extrinsics","dataset-access":"https://forms.gle/E84Kg4BedVpgMe436"},"release":{"code":"Available","weights":"Available","data":"Partial — DROID-3D calibration/extrinsics; image/depth corpus access by request","license":"MIT code; SVD-derived checkpoints retain Stability AI Community License; DROID-3D extrinsics CC BY 4.0","notes":"Training/inference and 90k-step EMA checkpoint files are verified. Calibration/extrinsics are public; DROID-3D image/depth access uses a separate request form. The unreleased 40k warm-start checkpoint is not conflated with the 90k release."},"evidence":[{"url":"https://arxiv.org/abs/2610.08780","label":"Original manuscript and first submission history","fields":["title","date","summary","model","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://www.jaibardhan.com/depthworld","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/Jai2500/depthworld","label":"Official implementation, README and file/license inspection on 19 October 2026","fields":["links","release","venue"],"note":"Training/inference and 90k-step EMA checkpoint files are verified. Calibration/extrinsics are public; DROID-3D image/depth access uses a separate request form. The unreleased 40k warm-start checkpoint is not conflated with the 90k release. MIT code; SVD-derived checkpoints retain Stability AI Community License; DROID-3D extrinsics CC BY 4.0."},{"url":"https://huggingface.co/jaibrdhn/depthworld","label":"Official released artifact inventory and model/dataset card","fields":["release.weights","links"],"note":"File inventory and artifact-specific terms checked independently; no upstream artifact is counted as this work’s trained release."},{"url":"https://huggingface.co/datasets/jaibrdhn/droid_3d_extrinsics","label":"Official released artifact inventory and model/dataset card","fields":["release.data","links"],"note":"File inventory and artifact-specific terms checked independently; no upstream artifact is counted as this work’s trained release."}],"caveats":["Generates the outcome of provided actions; it does not itself select a manipulation action.","Depth labels are stereo-estimated/recalibrated metric geometry, not raw sensor ground truth."],"verified":"2026-10-19","model":{"role":"Action-conditioned RGB-D neural simulator","family":"Predictive world model","baseModel":"Ctrl-World / Stable Video Diffusion, spatial latent RGB-depth tiling and auxiliary point-map head.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Multi-view RGB-D initial observations and candidate 7-D end-effector/gripper action sequences.","outputs":"Future RGB/depth videos and robot-frame geometric predictions.","trainingMethod":"Joint RGB-depth diffusion with geometric pointmap supervision.","trainingData":"DROID-3D calibrates over 70K episodes; exact filtered training/validation subsets are reported separately.","trainingTime":"90K steps, approximately two days","trainingHardware":"Two H200 nodes; GPUs per node not specified","evaluation":"Held-out world-model rollouts, depth/calibration and 3D consistency; no new closed-loop action policy evaluated.","inference":"Not reported in the inspected primary-source sections"}},{"id":"robo-cop","name":"Robo-COP","title":"Co-Evolving Robot Orchestrators and Policies through Deployment","date":"2026-10-06","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Co-evolves a robot orchestrator and learned policy by selectively collecting skill-level data, training candidates and verifying whether to deploy them.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.09228","project":"https://robo-cop.pages.dev"},"policy":{"family":"Hybrid","baseModel":"Gemini 3.8 Flash orchestrator; π0.5-DROID initial VLA.","modelSize":"Not reported","inputs":"Task, robot observations, memory, skill-level execution traces and training / verification outcomes.","outputs":"Orchestrated scripted-motion and VLA calls; curated demonstrations, candidate policy checkpoints and deployment / rollback decisions.","trainingData":"Skill-level successful segments can be recovered from episodes that fail overall; deployment traces accumulate for selective fine-tuning.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Both memory / harness and VLA weights can change. A controller decides whether to wait, train, accept or revert rather than fine-tuning on a fixed schedule.","evaluation":"Ten selected RoboLab tasks with randomized objects and three real-robot tasks; memory-only and fixed-schedule comparisons."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2610.09228","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["The ten RoboLab tasks are a preselected subset covering the paper's operating regimes.","Candidate training does not automatically imply deployment; verification and rollback are part of the method."],"verified":"2026-10-17"},{"id":"taczero","name":"TacZero","title":"TacZero: Training-Free Peg Insertion Using a General-Purpose Vision-Language Model with Tactile Feedback","date":"2026-10-06","year":2026,"collections":["policy"],"categories":["tactile-il","agent-rsi-icl"],"resourceType":"policy","summary":"Uses a frozen general-purpose VLM to interpret tactile observations and choose insertion actions.","institution":"OMRON SINIC X","venue":"arXiv preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.07621","project":"https://omron-sinicx.github.io/taczero/"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Paper and public presentation verified; no implementation, weight or data release is inferred from a publication or demonstration."},"evidence":[{"url":"https://arxiv.org/abs/2610.07621","label":"Canonical author manuscript and arXiv submission history","fields":["title","date","institution","venue"],"note":"Date retains the initial v1 submission, independently of revisions or current artifact releases."},{"url":"https://arxiv.org/html/2610.07621v1","label":"Full author paper: method, observations and evaluation scope","fields":["summary","policy","modalities","environments","embodiments","caveats"],"note":"Physical sensors, predicted signals, training labels and policy inputs are recorded separately; undisclosed quantities remain unknown."},{"url":"https://omron-sinicx.github.io/taczero/","label":"Official paper-linked project page; release review on 19 October 2026","fields":["links","release"],"note":"Current artifacts are checked separately from paper experiments. A project-only link is not proof of an implementation release."}],"caveats":["The overhead RGB-D camera supplies RGB to the VLM; no depth stream is claimed as a VLM input.","One action is followed by an API wait; contact changes cannot trigger immediate VLM-directed correction during that interval."],"verified":"2026-10-19","policy":{"family":"Hybrid","baseModel":"GPT-6 Astra via API with structured robot tools; no task-specific VLM weight updates.","modelSize":"Not reported","inputs":"Task instruction, RGB workspace image, robot state and three-axis fingertip tactile responses, supplied numerically or graphically.","outputs":"Absolute XYZ end-effector targets and gripper commands with a fixed end-effector orientation.","trainingData":"Not applicable — no additional tactile/manipulation training.","trainingTime":"Not applicable","trainingHardware":"Not applicable — hosted API inference","controlRate":"Sequential API-mediated action selection; API waiting time and action execution time are reported separately, not a real-time tactile control rate.","trainingMethod":"Frozen-VLM action selection using observation/action history.","evaluation":"Physical cylindrical and square peg insertion with numerical/graphical tactile variants."}},{"id":"wearable-contact-shifts","name":"Wearable Contact Shifts","title":"From Wearable Interfaces to Dexterous Policies: Contact Shifts and Tactile Representations","date":"2026-10-06","year":2026,"collections":["policy"],"categories":["tactile-il","tactile-foundation"],"resourceType":"policy","summary":"A controlled study of wearable geometry and tactile representations trains dexterous policies from direct human interaction.","institution":"University of Florida","venue":"Preprint","modalities":["RGB","Tactile"],"environments":["Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2610.08870"},"policy":{"family":"Diffusion","baseModel":"Dex UMI configuration:DINO image encoder and temporal U-Net diffusion policy.","modelSize":"Not reported","inputs":"One wrist RGB image and tactile vector; no proprioception input.","outputs":"16 future wrist pose/12 hand motor command actions.","trainingData":"Shared lid-twisting/egg-carton cohorts; revised-interface precision tasks 311 soldering tool and 319 USB demos are separate cohorts.","trainingTime":"400 epochs; wall-clock Not reported.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"400 epoch behavior cloning; cross interface×binary/spatial-plus-force conditioning.","evaluation":"Real XHand task rollouts and session-level contact shift analysis."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2610.08870","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."}],"caveats":["Richer input changes both spatial structure and force; the experiment does not isolate which component causes gains.","One policy instance/condition; rollout variation does not quantify training seed variance."],"verified":"2026-10-17"},{"id":"geobridge-vla","name":"GeoBridge-VLA","title":"GeoBridge-VLA: Geometry-Aware Residual Adaptation for Vision-Language-Action Models","date":"2026-10-04","year":2026,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"A depth-trained bridge adds geometric residuals to frozen VLA visual tokens without a second image encoder at deployment.","institution":"Gachon University, School of Computing / iRASC Lab","venue":"arXiv preprint","modalities":["RGB","Language","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.05026"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2610.05026","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."}],"caveats":["Geometric conditioning is learned from depth supervision, not measured at deployment.","Disabling the residual within the same trained checkpoint gives a much smaller effect than the between-model headline comparison."],"verified":"2026-10-17","policy":{"family":"VLA","baseModel":"SmolVLA visual encoder; K4 geometry decoder, feature bridge and gated residual action interface.","modelSize":"Not reported in the inspected primary-source sections","inputs":"RGB, language, robot state; no rollout depth stream.","outputs":"Continuous action chunks.","trainingMethod":"Depth adaptation followed by frozen geometry modules and action-side residual training.","trainingData":"Stage-specific depth supervision and LIBERO / OMY manipulation demonstrations.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Matched LIBERO suites and four physical ROBOTIS OMY tasks; 200 real trials.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"awomo05","name":"Awomo-0.5","title":"Awomo-0.5: World Action Model Pre-trained at Scale","date":"2026-10-03","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Releases image- and video-conditioned world-action policy variants with a common embodied simulation action interface.","institution":"Awomo-WestlakeDI / Awomo-WAM Team","venue":"Official project / technical release","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"project":"https://github.com/Awomo-WestlakeDI/Awomo-0.5","code":"https://github.com/Awomo-WestlakeDI/Awomo-0.5","weights":"https://huggingface.co/Auwomo","weights-robodojo":"https://huggingface.co/Auwomo/Awomo-0.5-Robodojo"},"policy":{"family":"World-action model","baseModel":"Qwen3-VL-4B-Instruct; image variant uses FLUX.2-klein-base-4B, video variant Wan2.2-TI2V-5B","modelSize":"Foundation component labels4B Qwen, 4B FLUX or5B Wan; full action/model aggregate not disclosed","inputs":"Current RGB and language/state; video variant additionally temporal context","outputs":"Continuous robot action chunks through shared action interface; benchmark-specific layouts","trainingData":"Pretrained at scale according to project; exact corpus/scale not disclosed","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"World/action pretraining; October 20 release adds actual RoboDojo SFT data loaders, training builders/configurations and XPolicyLab inference support. Other benchmark training integrations remain planned.","evaluation":"Released RoboDojo checkpoint/SFT and RoboCasa365 evaluation; LIBERO-Plus, MolmoSpaces and RoboTwin coverage are reported separately from available adapters."},"release":{"code":"Partial","weights":"Available","data":"Not verified","license":"Apache-2.0 code; base/RoboCasa model-specific CC BY-NC 4.0 terms. The RoboDojo checkpoint has a separate evaluation-only LICENSE.md restricted to explicitly authorized official evaluators, with no redistribution, training, fine-tuning, distillation or commercial-use grant. Upstream components retain their own terms.","notes":"Actual RoboDojo SFT source/configs are public. Model file presence does not remove checkpoint-specific access/use restrictions: inspect the particular checkpoint license before using it. Broad family README licensing is superseded by the RoboDojo LICENSE.md."},"evidence":[{"url":"https://github.com/Awomo-WestlakeDI/Awomo-0.5","label":"Official model project / technical release","fields":["title","date","policy","release"],"note":"Research-report facts and current executable release are scoped separately; undated releases retain an unknown publication date."},{"url":"https://github.com/Awomo-WestlakeDI/Awomo-0.5","label":"Official implementation / author-contributed adapter","fields":["policy.inputs","policy.outputs","release.code"],"note":"Actual deployment contract is checked; adapter presence alone is not evidence of benchmark participation."},{"url":"https://github.com/Awomo-WestlakeDI/Awomo-0.5/blob/main/training/robodojo/README.md","label":"Official current source / artifact inventory — 21 October 2026","fields":["release","links","policy"],"note":"October 20 actual training package and source builders are released; this is not just an inference adapter."},{"url":"https://huggingface.co/Auwomo/Awomo-0.5-Robodojo/raw/main/LICENSE.md","label":"Official current source / artifact inventory — 21 October 2026","fields":["release","links","policy"],"note":"Actual LICENSE.md grants only designated official RoboDojo evaluation use; current model card explicitly retains this restriction, overriding the family README’s broader weight summary."}],"caveats":["Paper marked TBD; missing size/training costs and architecture figure remain undisclosed.","October3 dates the public RoboDojo release news; image/video variants and their scores remain distinct."],"verified":"2026-10-21"},{"id":"exstereo","name":"ExStereo","title":"ExStereo: Lifting 2D Vision-Language-Action Models to 3D with Explicit Stereo Representations","date":"2026-10-03","year":2026,"collections":["policy"],"categories":["rgbd-3d","generalist-foundation"],"resourceType":"policy","summary":"A stereo module reconstructs explicit metric scene geometry, renders depth/XYZ views, and injects stereo tokens into pretrained VLA action experts.","institution":"University of Southern California","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.04805","project":"https://exstereo-vla.github.io/ExStereo/"},"policy":{"family":"VLA","baseModel":"π0.5 or SmolVLA, augmented with Fast-FoundationStereo, a multi-view ViT, action–stereo cross-attention and a training-only masked-autoencoder decoder.","modelSize":"ExStereo adds21.2M parameters: 3.2M multi-view ViT, 17.3M cross-attention projections, 0.6M MAE decoder discarded after mid-training. Base VLA parameters are separate.","inputs":"Calibrated synchronized stereo RGB pair, regular RGB observations, language and proprioception. Metric depth/XYZ reconstructed from stereo with Fast-FoundationStereo; no active depth sensor is required.","outputs":"Base VLA continuous action chunks, conditioned on four rendered geometry views via action–stereo cross-attention.","trainingData":"Mid-training: 500 randomized episodes for each of7 RoboTwin tasks (3500 episodes); downstream post-training100 clean episodes per task, with task/object-disjoint mid-training and evaluation sets.","trainingTime":"Wall-clock duration not reported. Mid-training300 K steps, downstream post-training30 K steps; no hyperparameter tuning and final checkpoint evaluated.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Mid-train stereo module and VLA with geometry/MAE supervision; freeze stereo module during downstream post-training.","evaluation":"10 RoboTwin simulation tasks with randomization, 25 unseen episodes per method across 3 training seeds; 3 physical tasks on bimanual AgileX, 20 trials per task."},"release":{"code":"Not released","weights":"Not released","data":"Not verified","license":"Code/model/data license not verified.","notes":"Author project and full manuscript accessible; no complete public training/inference repository or policy checkpoints verified."},"evidence":[{"url":"https://arxiv.org/abs/2610.04805","label":"Paper and first-public arXiv record","fields":["title","date","institution","policy","modalities","embodiments","environments"],"note":"Technical fields follow the inspected full manuscript and appendices; date is arXiv v1, not the current revision."},{"url":"https://exstereo-vla.github.io/ExStereo/","label":"Official project","fields":["summary","imageCandidates","release"],"note":"Current author project and release links."}],"caveats":["Geometry is reconstructed from stereo RGB. Depth is not listed as an independent measured policy input.","Canonicalization assumes a dominant planar support surface; mid-training uses one bimanual embodiment and benchmark.","Parameter breakdown uses rounded manuscript values and does not replace a full deployed-parameter count."],"verified":"2026-10-17"},{"id":"foreact3d","name":"ForeAct3D","title":"ForeAct3D: Policy-Grounded Future World Modeling for VLA Policies","date":"2026-10-03","year":2026,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Action-conditioned semantic 3D forecasts and physical-consistency losses regularize a VLA during training.","institution":"University of Illinois Urbana-Champaign; University of Illinois Chicago; Cisco","venue":"arXiv preprint","modalities":["RGB","Language","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.04607","code":"https://github.com/anthonytao80-crypto/ForeAct3D"},"release":{"code":"Not released","weights":"Not verified","data":"Not verified","license":"MIT for repository code; pretrained models, data and figure rights remain separate","notes":"Official repository contains LICENSE, README and four illustration assets; no training/inference implementation at review time."},"evidence":[{"url":"https://arxiv.org/abs/2610.04607","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://github.com/anthonytao80-crypto/ForeAct3D","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/anthonytao80-crypto/ForeAct3D/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/anthonytao80-crypto/ForeAct3D/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"7 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["No future model prediction is required at inference; auxiliary depth supervision is not measured deployment depth."],"verified":"2026-10-17","policy":{"family":"VLA","baseModel":"VLA backbone with current/future geometric queries for depth, segmentation and camera pose.","modelSize":"Not reported in the inspected primary-source sections","inputs":"RGB, language and robot state at deployment.","outputs":"Action chunks; current/future semantic 3D predictions are training-side auxiliaries.","trainingMethod":"Behavior cloning plus action-conditioned geometric forecasting, rigidity/background and wrist-kinematic constraints.","trainingData":"Target LIBERO/CALVIN and physical task demonstrations; no robot pretraining.","trainingTime":"30K optimization steps","trainingHardware":"16 NVIDIA A100 40GB GPUs","evaluation":"LIBERO, CALVIN and three real tabletop manipulation tasks.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"tacot","name":"TacOT","title":"TacOT: Learning Contact-Rich Dexterous Manipulation from Human Demonstrations via Tactile-Guided Optimal Transport","date":"2026-10-03","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Action–tactile temporal alignment and optimal transport transfer independently collected human demonstrations to dexterous robot policies.","institution":"Tsinghua University / Shanghai Jiao Tong University / Xspark AI / Nanyang Technological University","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2610.04363"},"policy":{"family":"Flow matching","baseModel":"16-layer transformer observation encoder and 8-layer action decoder with flow-matching policy; action–tactile DTW/OT during training.","modelSize":"Not reported","inputs":"Wrist/third-person RGB, unified proprioceptive motion and tactile observations.","outputs":"Robot arm–hand action chunks.","trainingData":"Per task 60 robot ID episodes+200 human episodes (100 ID,100 OOD); correspondences are inferred, not recorded paired sessions.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"30 Hz; execute first 8 actions per chunk before replanning.","trainingMethod":"Multimodal flow imitation with action–tactile-guided latent alignment.","evaluation":"UR5–XHand:Tissue, Wiping, Egg, Block;30 ID and 15 OOD rollouts/method/task; OOD configurations excluded from robot training but represented in human data."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2610.04363","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."}],"caveats":["OOD means unseen in robot demonstrations; human demonstrations include the OOD configurations.","DTW/OT correspondence optimization is a training procedure, not inference-time privileged access."],"verified":"2026-10-17"},{"id":"visual-predicted-force","name":"Visual Predicted Force","title":"Robot Learning with Visual Predicted Force","date":"2026-10-03","year":2026,"collections":["policy"],"categories":["tactile-il","tactile-foundation"],"resourceType":"policy","summary":"A diffusion policy jointly proposes actions and forces, then selects actions matching a task-specific target without a deployment force sensor.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.04741"},"policy":{"family":"Diffusion","baseModel":"Visual Fin Ray-force estimator for demonstration labeling; Diffusion Policy action–force proposal model.","modelSize":"Not reported","inputs":"RGB and robot state; force estimator used to label training demos, not to rank test time candidate actions.","outputs":"Candidate end-effector actions and force values; execute candidate closest to demonstration target force.","trainingData":"Calibration 117 episodes/47.5 min;40 robot demonstrations per each of four tasks.","trainingTime":"Not reported","trainingHardware":"Single NVIDIA GeForce RTX5090for policy training and execution.","controlRate":"Not reported","trainingMethod":"Calibration force regression, frozen-label annotation and joint action–force diffusion imitation.","evaluation":"ARX arm/Fin Ray gripper:berry picking, empty-can grasping, in-hand reorientation, plug insertion."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2610.04741","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."}],"caveats":["Runtime force values are policy-generated predictions, not sensor readings.","Only normal grasping force is modeled; the visual force estimator does not provide online correction of sampled actions."],"verified":"2026-10-17"},{"id":"dexjoco-x","name":"DexJoCo-X","title":"DexJoCo-X: Benchmarking Action Representations for Multi-Hand Dexterous Manipulation","date":"2026-10-02","year":2026,"collections":["benchmark"],"categories":["dexterous-benchmarks"],"resourceType":"benchmark","summary":"Controlled seven-hand comparison of action representations under shared scenes, task criteria and balanced demonstration budgets.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Simulation"],"embodiments":["Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2610.03278"},"benchmark":{"simulator":"MuJoCo (DexJoCo-based benchmark)","simulatorVersion":"Not reported","taskCount":"6 tasks × 7 hands = 42 hand–task pairs","tasks":["Bucket","Hammer","Hanoi","Microwave","Unlock iPad","Photo"],"taskListScope":"Complete six paper task labels; first three single-arm, last three bimanual.","capabilities":["Cross-embodiment dexterity","Action representation","Functional tool use","Bimanual control"],"metrics":"Success per hand/task and mean across 42 pairs; three independent 50-rollout evaluations/cell = 6,300 evaluation episodes.","robots":"XHand, Inspire, Wuji, LEAP, Sharpa Wave, LinkerHand, Allegro hands with arm setups.","sensors":"RGB, robot state and action records; no tactile sensing established.","dataCollection":"Motion-capture teleoperation using Rokoko gloves and Vive wrist trackers, followed by reviewed trajectory replay, scene expansion and success verification.","data":"2,100 balanced demonstrations; 50 per hand–task pair; reviewed demonstrations expanded and verified in randomized scenes.","protocol":"Seven per-hand multitask π0.5/Ego-Pi policies (300 demos each) vs joint Being-H0.5 (2,100 demos); Native, function-aligned action slots and DexLatent within matched joint architecture.","baselines":"π0.5, Ego-Pi action-head adaptation; Being-H0.5 with Native, FAAS, DexLatent."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not reported","notes":"Paper accessible; independent public code/data release URL not resolved."},"evidence":[{"url":"https://arxiv.org/abs/2610.03278","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://arxiv.org/abs/2610.03278","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Cross-architecture comparisons differ in pretraining and training organization; the clean representation comparison is within Being-H0.5.","The six tasks are a subset/reformulation of DexJoCo task families."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not reported","notes":"Paper accessible; independent public code/data release URL not resolved."}},"relatedIds":[]},{"id":"simpletouch","name":"SimpleTouch","title":"SimpleTouch: Can Vision-Language-Action Models Master Contact-Rich Manipulation Without Tactile Policy Pretraining?","date":"2026-10-02","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Adds a tactile expert and training-only future-touch supervision to π0.5, using full frozen tactile token maps without tactile policy pretraining.","institution":"Tsinghua University / Amap / Hong Kong Polytechnic University","venue":"Preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.02784","project":"https://simpletouch-robot.github.io/","code":"https://github.com/ccyydd/SimpleTouch","weights":"https://huggingface.co/cyang203912/SimpleTouch"},"policy":{"family":"VLA","baseModel":"π0.5 / PaliGemma; frozen T3-large encoder","modelSize":"Gemma 2B language backbone; 300M action expert and same-width/depth tactile expert; total not reported","inputs":"RGB, instruction, absolute robot state and two tactile RGB streams","outputs":"50-step chunks; relative arm joints and absolute gripper, padded to 32 internal dimensions","trainingData":"50 demonstrations per task; six UniVTAC tasks and four real tasks","trainingTime":"20k updates per task; wall-clock not reported","trainingHardware":"8 NVIDIA H20 GPUs","controlRate":"Real commands 20 Hz; executes 25 or 50 actions before re-observation; tactile cameras 25 Hz","trainingMethod":"Joint task fine-tuning with action flow matching and multi-horizon future tactile latent loss","evaluation":"UniVTAC six-task subset; Play Mahjong, Pick Up Chips, Insert USB, Wipe Board"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Project links official implementation and model hub; dataset release not independently resolved."},"evidence":[{"url":"https://arxiv.org/abs/2610.02784","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/ccyydd/SimpleTouch","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2610.02784","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."},{"url":"https://github.com/ccyydd/SimpleTouch/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["A frozen tactile representation and pretrained VLA are still required.","Future tactile targets are supervision only; no future-touch generation at deployment."],"verified":"2026-10-19","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Project links official implementation and model hub; dataset release not independently resolved."}},"relatedIds":[]},{"id":"vista","name":"VISTA","title":"Equivariant Visual-Tactile Diffusion Policy for Contact-Rich Manipulation","date":"2026-10-02","year":2026,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"An equivariant visual–tactile diffusion policy uses spherical tokens and end-effector-aligned fusion for contact-rich manipulation.","institution":"Not reported","venue":"CoRL 2026","modalities":["RGB","Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.03333","code":"https://github.com/Kenn3o3/Vista"},"policy":{"family":"Diffusion","baseModel":"Equivariant visual/tactile encoders and diffusion action decoder","modelSize":"Not reported","inputs":"Wrist RGB, optical tactile images and proprioception","outputs":"Action chunks with end-effector-relative alignment","trainingData":"UniVTAC simulation; 100 real demonstrations per task, 80/20 training/validation","trainingTime":"Not reported","trainingHardware":"Inference measured on one RTX 3090; training hardware not reported","controlRate":"Actions 10 Hz; replans every 0.6 s for insertion / 0.8 s erasing; low-level 300 Hz","trainingMethod":"Spherical tokenization and equivariant multimodal fusion","evaluation":"UniVTAC and real USB insertion, pencil insertion and erasing"},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"Apache-2.0 for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Official repository contains training/evaluation recipes and UniVTAC-derived data instructions."},"evidence":[{"url":"https://arxiv.org/abs/2610.03333","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/Kenn3o3/Vista","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2610.03333","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."},{"url":"https://github.com/Kenn3o3/Vista/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["New tactile frames influence actions only at chunk replanning, despite sub-100 ms model-side inference.","One training seed and ten real evaluation trials per task."],"verified":"2026-10-19","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Available","license":"Apache-2.0 for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Official repository contains training/evaluation recipes and UniVTAC-derived data instructions."}},"relatedIds":[]},{"id":"pyrualean","name":"PyRUA-Lean","title":"Fewer Tokens, Better Action: GPT-6 Astra Robot Agents with 14% Higher Success Rate but 65% Fewer Tokens","date":"2026-10-01","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Uses Python cells to compose existing robot primitives with control flow and selective feedback, reducing repeated model calls relative to tool-by-tool execution.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.01939","project":"https://dagroup-pku.github.io/PyRUA-Lean/","code":"https://github.com/DAGroup-PKU/PyRUA-Lean","weights":"https://huggingface.co/DAGroup-PKU/PyRUA-Lean-Coder-v0.1-4B"},"policy":{"family":"Robot agent","baseModel":"GPT-6 Astra for the main frozen-planner comparison; a separate compact 4B coder is trained with GRPO.","modelSize":"4B for the separately trained coder; GPT-6 Astra size is not disclosed.","inputs":"Task, robot observations / state and selectively requested primitive feedback; observation and tool access depend on the benchmark backend.","outputs":"Python programs call robot primitives through a persistent object and can express loops, branches and local retries.","trainingData":"Main Astra condition has no weight training. A separate GRPO experiment uses 45 LIBERO-Pro instances.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen Astra with a code-execution interface in the main experiment; reinforcement training updates the compact coder in a separate study.","evaluation":"700 instances across LIBERO-Pro, RoboTwin 2.0 and RoboCasa365; additional RoboDojo evaluation without a VLA or privileged state."},"release":{"code":"Available","weights":"Available — PyRUA-Lean Coder v0.1-4B","data":"Not verified","license":"Apache-2.0 code and Coder checkpoint, verified from repository LICENSE and model LICENSE; external backbones/assets retain separate terms.","notes":"Actual Python-cell runtime, simulator backends and RoboDojo/XPolicyLab adapter are public. The separate GRPO-trained Coder v0.1-4B repository contains model.safetensors and model-extra-00001.safetensors. Physical-robot integration is still marked coming soon; hosted Astra and the released trained coder are separate branches."},"evidence":[{"url":"https://arxiv.org/abs/2610.01939","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/DAGroup-PKU/PyRUA-Lean","label":"Author-linked repository","fields":["links.code"],"note":"Repository identity; code and checkpoint release status are stated separately."},{"url":"https://github.com/DAGroup-PKU/PyRUA-Lean","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/DAGroup-PKU/PyRUA-Lean-Coder-v0.1-4B","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["Token and call savings are calculated over jointly solved instances; success rates use all instances.","The frozen Astra result and trained compact-coder result must not be conflated."],"verified":"2026-10-19"},{"id":"rpg","name":"Reconstruct, Practice, Go Real (RPG)","title":"Reconstruct, Practice, Go Real: Guided Self-Improvement for Embodied Agents","date":"2026-10-01","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Uses dataset-derived practice tasks, privileged simulator comparison and video analysis to improve a reusable symbolic robot-skill system before physical deployment.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.02204"},"policy":{"family":"Robot agent","baseModel":"Frozen coding / runtime agents, a privileged-agent diagnostic and a video analyzer sharing the skill-development loop.","modelSize":"Not reported","inputs":"Offline task videos, reconstructed practice scenes, runtime observations and skill library. Only the privileged diagnostic receives simulator state.","outputs":"Revised reusable symbolic skills and system prompts; physical execution uses a frozen calibrated runtime system.","trainingData":"Practice tasks are reconstructed from the ABC dataset covering 193 source tasks; source tasks need not be reproduced exactly.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen model; compare runtime and privileged executions, diagnose with video, validate skill / prompt revisions and retest merges before freezing.","evaluation":"22 held-out-initialization practice tasks and physical YAM deployment; calibration is shared with the adapted CaP-Agent0 comparison."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2610.02204","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["Privileged simulator execution is an improvement diagnostic, not the physical runtime observation.","Physical adaptation includes common coordinate calibration and hardware offsets, while behavior code is frozen before physical evaluation."],"verified":"2026-10-17"},{"id":"residual-compliant-policy","name":"Residual Compliant Policy","title":"Residual Compliant Policy: Learning Compliant Policies from Few Demonstrations","date":"2026-10-01","year":2026,"collections":["policy"],"categories":["visual-precision-rl"],"resourceType":"policy","summary":"Combines visible compliance during teaching, simulation augmentation and residual RL for contact-rich control.","institution":"University of Pennsylvania","venue":"arXiv preprint","modalities":["Force/Torque","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Other"],"links":{"paper":"https://arxiv.org/abs/2610.14035","project":"https://residual-compliant-policy.github.io/"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Paper and public presentation verified; no implementation, weight or data release is inferred from a publication or demonstration."},"evidence":[{"url":"https://arxiv.org/abs/2610.14035","label":"Canonical author manuscript and arXiv submission history","fields":["title","date","institution","venue"],"note":"Date retains the initial v1 submission, independently of revisions or current artifact releases."},{"url":"https://arxiv.org/html/2610.14035v1","label":"Full author paper: method, observations and evaluation scope","fields":["summary","policy","modalities","environments","embodiments","caveats"],"note":"Physical sensors, predicted signals, training labels and policy inputs are recorded separately; undisclosed quantities remain unknown."},{"url":"https://residual-compliant-policy.github.io/","label":"Official paper-linked project page; release review on 19 October 2026","fields":["links","release"],"note":"Current artifacts are checked separately from paper experiments. A project-only link is not proof of an implementation release."}],"caveats":["RGB-D supports the digital twin; raw images/depth are not direct inputs to the described state-based policy.","Physical evaluation uses a pusher, not a claim of dexterous-hand or parallel-gripper deployment."],"verified":"2026-10-19","policy":{"family":"Hybrid","baseModel":"Consistency flow-matching base policy with residual RL over an admittance controller.","modelSize":"Not reported","inputs":"Four-step history of digital-twin object pose, end-effector pose, filtered F/T feedback and commanded stiffness.","outputs":"16-step reference-pose/stiffness chunks; residual policy corrects reference position while base retains orientation/stiffness.","trainingData":"Seven physical demonstrations across three tasks, augmented using Laplacian editing and simulation trajectory optimization.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Base observations at 30 Hz; F/T acquisition 1 kHz.","trainingMethod":"Imitation learning followed by simulation residual RL over a frozen base policy.","evaluation":"Franka FR3 flipping/pushing tasks with out-of-distribution object poses; Newton digital twin and haptic teleoperation."}},{"id":"uniwam","name":"UniWAM / OLA-Sem","title":"UniWAM: Unified World-Action Model","date":"2026-10-01","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Unifies physical-language reasoning, world generation and action prediction, combining robot, human and VQA supervision with efficient history-conditioned action generation.","institution":"HKUST (Guangzhou) / OLA Dimensions","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2610.02054","project":"https://uniwam.github.io/","code":"https://github.com/UniWAM/UniWAM","weights":"https://huggingface.co/collections/chenpyyy/uniwam"},"policy":{"family":"World-action model","baseModel":"Qwen3-VL-2B physical reasoner, Wan2.2-TI2V-5B world generator and action expert in a three-expert MoT","modelSize":"8B reported model","inputs":"RGB, language, robot state and action history; future visual targets used in training","outputs":"Continuous action chunks with optional joint world generation and physical-language outputs","trainingData":"Table 1 reports approximately 4,958 robot hours and 5,072 human egocentric hours; the VQA table reports 8.293M pairs. Main-text robot-data prose instead states approximately 4,363 hours, so the paper contains an internal scale discrepancy. Robot sources include AgiBot, Bridge, DROID, Fractal, RoboCOIN, and Interdata-A1.","trainingTime":"Pretraining: seven days. RoboTwin post-training: 200,000 steps, 12 hours; LIBERO post-training: 20 hours. The real multi-task study trains 80,000 steps.","trainingHardware":"Pretraining: 32 H800 GPUs. RoboTwin and LIBERO post-training: eight H800 GPUs; real multi-task study: eight H100 GPUs.","controlRate":"History-conditioned flow matching supports few-step action generation. A common end-to-end query/control rate is Not reported in the inspected sources.","trainingMethod":"Physical actions rendered as language for compatible VLM supervision; differentiated expert losses, future-noise augmentation and history-conditioned flow matching","evaluation":"RoboTwin C2R, LIBERO/Plus, physical long-horizon/instruction-following/robustness tasks"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 code, verified from LICENSE. Model and data terms are separate.","notes":"Current release covers RoboTwin and LIBERO workflows; Bridge/DROID/Fractal and real inference explicitly outside public release."},"evidence":[{"url":"https://arxiv.org/abs/2610.02054","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/UniWAM/UniWAM","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://uniwam.github.io/","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."},{"url":"https://github.com/UniWAM/UniWAM/blob/main/LICENSE","label":"Verified code license","fields":["release.license"]}],"caveats":["Official RoboTwin news dated 10 October 2026 renames OLA-Sem to UniWAM; these names refer to one canonical work.","This is distinct from the unrelated mobile-manipulation UniWAM report, arXiv: 2609.39388.","The robot-data total differs between the main prose and Table 1."],"verified":"2026-10-17"},{"id":"vigar","name":"ViGAR","title":"Rethinking World-Action Model for Compositional and In-Context Robotic Manipulation","date":"2026-10-01","year":2026,"collections":["policy"],"categories":["generalist-foundation","agent-rsi-icl"],"resourceType":"policy","summary":"Separates visual subgoal planning from subgoal-conditioned video-action execution, enabling goal-image steering of new task compositions without parameter updates.","institution":"Peking University / AgiBot / CocoMatrix","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2610.02368","project":"https://dagroup-pku.github.io/ViGAR/","code":"https://github.com/DAGroup-PKU/ViGAR","weights":"https://huggingface.co/DAGroup-PKU/ViGAR"},"policy":{"family":"World-action model","baseModel":"Cosmos3-Nano initialization for both visual planner and WAM executor; dual-branch MoT backbone","modelSize":"Cosmos3-Nano has 16B total and 8B active parameters. The planner and executor are separately adapted copies; 16B is not a complete end-to-end system count.","inputs":"Multi-view RGB, language and state; optional global goal image for in-context task composition","outputs":"Visual subgoal images and continuous action chunks with predicted visual trajectories","trainingData":"RoboTwin training uses 2,500 clean demonstrations. Physical mid-training uses approximately 900 hours on AgiBot A2 across 752 tasks, followed by task-specific post-training.","trainingTime":"Planner: 100,000 steps, batch 32. Policy: 50,000 steps, batch 256; the goal-injection ablation instead trains 30,000 steps. Wall-clock duration is Not reported.","trainingHardware":"Training hardware is Not reported in the inspected sources. Inference optimization is profiled on one B20Z GPU.","controlRate":"Reported inference speedups are 2.5 × for the policy and 11.2 × for the planner on B20Z. An absolute complete-system rate is Not reported.","trainingMethod":"Image-to-video backbone adapted into an image-editing subgoal planner, plus goal-conditioned joint video-action policy; inference-time goal context changes behavior without weight updates","evaluation":"RoboTwin: 50 tasks, 100 rollouts per task and setting. Physical evaluation covers five compositional tasks and two goal-image in-context tasks."},"release":{"code":"Available","weights":"Available","data":"Partial","license":"MIT original code, verified from LICENSE. Bundled components retain upstream licenses; Cosmos-derived model materials use OpenMDW-1.1.","notes":"The author repository and Hugging Face release provide RoboTwin policy/planner weights and 2,500 segmented demonstrations with generated subgoals. The roughly 900-hour real-robot dataset is explicitly marked coming soon."},"evidence":[{"url":"https://arxiv.org/abs/2610.02368","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/DAGroup-PKU/ViGAR","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://dagroup-pku.github.io/ViGAR/","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."},{"url":"https://github.com/DAGroup-PKU/ViGAR/blob/main/LICENSE","label":"Verified code license","fields":["release.license"]},{"url":"https://github.com/XPolicyLab/XPolicyLab/tree/94dbf9505f2f87cf59c3ee8ba5912cd236781771/policy/ViGAR","label":"Current XPolicyLab alias and interface","fields":["links.code","policy.evaluation"],"note":"ViGAR adapter directly cites arXiv:2610.02368 and DAGroup-PKU/ViGAR. It is the existing canonical policy, not a new work. RGB/BGR preprocessing, planner/policy GPU split and released interface are explicit."}],"caveats":["Planner and executor have different training/inference costs.","Goal-image ICL specifies terminal state, unlike a full demonstration-video prompt."],"verified":"2026-10-17"},{"id":"discrete-forcing","name":"Discrete Forcing","title":"Discrete Forcing: Infusing Discrete Guidance into Continuous Denoising for Few-Step Action Experts","date":"2026-09-30","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Uses discrete coarse action tokens to guide a continuous action expert, producing actions in one pass per branch.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.39526","project":"https://discrete-forcing.github.io/","code":"https://github.com/Jbo-Wang/discrete_forcing"},"policy":{"family":"VLA","baseModel":"StarVLA vision-language backbone and partly shared Action DiT with discrete and continuous branches.","modelSize":"LIBERO: 1.55B total. Main RoboTwin/RoboCasa: 5.5B. Scaling studies use 1.0B/2.6B/5.5B totals with 175M/400M/900M action experts.","inputs":"RGB, language and base-policy proprioception.","outputs":"Eight-action LIBERO chunks; discrete tokens guide continuous refinement using two function evaluations.","trainingData":"Official LIBERO, RoboTwin 2.0 and RoboCasa-GR1 task data; physical precision/dynamic demonstrations.","trainingTime":"LIBERO: 80k optimizer steps. Wall-clock duration not reported.","trainingHardware":"LIBERO: four H100 GPUs, batch 16 per GPU / global 64.","controlRate":"LIBERO median 78.95 ms end-to-end; 24.91 ms for action generation. These are not low-level servo intervals.","trainingMethod":"Flow matching with shared layers and specialized branches; discrete guidance compared with parameter-matched continuous experts.","evaluation":"LIBERO, RoboTwin 2.0, RoboCasa-GR1 and physical high-precision/dynamic tasks."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT code; weights/data/backbone terms separate","notes":"Implementation is public; the README Models button points to the code repository rather than a verified checkpoint."},"evidence":[{"url":"https://arxiv.org/abs/2609.39526","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/Jbo-Wang/discrete_forcing","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://discrete-forcing.github.io/","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."}],"caveats":["This is a flow-matching VLA action expert, not a video world-action model.","Sizes and costs are benchmark-specific."],"verified":"2026-10-17"},{"id":"embodirsi","name":"EmbodiRSI","title":"EmbodiRSI: Recursive Self-Improvement for Data-Efficient Robot Adaptation","date":"2026-09-30","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Real-to-simulation reconstruction and policy rollout feedback drive targeted error correction, adaptive demonstration collection and repeated policy fine-tuning.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.38905"},"policy":{"family":"Hybrid","baseModel":"GPT-5.5 agent; π0.5 policy with an added value head.","modelSize":"Not reported","inputs":"Task language, head and wrist RGB images, and 14-dimensional ALOHA joint / gripper state at policy deployment. The simulated rollout monitor additionally has simulator-state access.","outputs":"Bimanual manipulation action chunks from the learned policy; the agent constructs environments and corrective demonstrations during improvement.","trainingData":"Simulated retained trajectories per subtask: 50 at R0, 200 at R1 and 400 at R2; small real-world human-in-the-loop data support adaptation.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Policy weights are updated. Collaborative Error Correction recovers from reached states, Adaptive Data Collection fills deficits, and advantage-annotated behavioral cloning uses value estimates.","evaluation":"Three paired real / simulated tabletop scenes with 14 individually evaluated subtasks: Office 6, Kitchen 4 and Household 4."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2609.38905","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["Simulator-state privileges belong to training / monitoring, not the stated real-world policy observation.","The 14 subtask results do not constitute an evaluation of a single uninterrupted whole-scene cleanup."],"verified":"2026-10-17"},{"id":"mikasa-robo-vla","name":"MIKASA-Robo-VLA","title":"MIKASA-Robo-VLA: Benchmarking Memory in VLA Models for Long-Horizon Manipulation","date":"2026-09-30","year":2026,"collections":["benchmark"],"categories":["robustness-evaluation"],"resourceType":"benchmark","summary":"Language-conditioned memory benchmark with 90 tasks and explicit cue-hiding design, extending the earlier 32-task RL suite.","institution":"AIRI / collaborating institutions","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.00604","project":"https://mikasarobo.github.io/","code":"https://github.com/CognitiveAISystems/MIKASA-Robo","data":"https://huggingface.co/datasets/mikasa-robo/mikasa-robo-vla-lerobot"},"benchmark":{"simulator":"ManiSkill / SAPIEN / Gymnasium","simulatorVersion":"Current VLA package distinct from legacy mikasa-robo-suite==0.0.5 RL release; exact SAPIEN/ManiSkill pins in pyproject/uv.lock.","taskCount":"90 language-conditioned tasks across 10 memory types; 38 Short / 30 Medium / 22 Long","tasks":["ShellGameTouch-VLA-v0","ShellGamePush-VLA-v0","InterceptSlow-VLA-v0","InterceptMedium-VLA-v0","InterceptFast-VLA-v0","InterceptGrabSlow-VLA-v0","InterceptGrabMedium-VLA-v0","InterceptGrabFast-VLA-v0","RotateLenientPos-VLA-v0","RotateLenientPosNeg-VLA-v0","RotateStrictPos-VLA-v0","RotateStrictPosNeg-VLA-v0","TakeItBack-VLA-v0","RememberColor3-VLA-v0","RememberColor5-VLA-v0","RememberColor9-VLA-v0","RememberShape3-VLA-v0","RememberShape5-VLA-v0","RememberShape9-VLA-v0","RememberShapeAndColor3x2-VLA-v0","RememberShapeAndColor3x3-VLA-v0","RememberShapeAndColor5x3-VLA-v0","BunchOfColors3-VLA-v0","BunchOfColors5-VLA-v0","BunchOfColors7-VLA-v0","SeqOfColors3-VLA-v0","SeqOfColors5-VLA-v0","SeqOfColors7-VLA-v0","ChainOfColors3-VLA-v0","ChainOfColors5-VLA-v0","ChainOfColors7-VLA-v0","ShellGameShuffleTouch-VLA-v0","ShellGameShuffleColorLampTouch-VLA-v0","ShellGameColorLampTouch-VLA-v0","FindImposterColor3-VLA-v0","FindImposterColor5-VLA-v0","FindImposterColor9-VLA-v0","FindImposterShape3-VLA-v0","FindImposterShape5-VLA-v0","FindImposterShape9-VLA-v0","FindImposterShapeAndColor3x2-VLA-v0","FindImposterShapeAndColor3x3-VLA-v0","FindImposterShapeAndColor5x3-VLA-v0","BatteriesCheckerEasy-3-VLA-v0","BatteriesCheckerEasy-6-VLA-v0","BatteriesCheckerHard-3-VLA-v0","BatteriesCheckerHard-6-VLA-v0","BlinkCountButtonPressEasy-VLA-v0","BlinkCountButtonPressMedium-VLA-v0","BlinkCountButtonPressHard-VLA-v0","RememberColor3-Long-VLA-v0","RememberColor5-Long-VLA-v0","RememberColor9-Long-VLA-v0","RememberShape3-Long-VLA-v0","RememberShape5-Long-VLA-v0","RememberShape9-Long-VLA-v0","RememberShapeAndColor3x2-Long-VLA-v0","RememberShapeAndColor3x3-Long-VLA-v0","RememberShapeAndColor5x3-Long-VLA-v0","BunchOfColors3-Long-VLA-v0","BunchOfColors5-Long-VLA-v0","BunchOfColors7-Long-VLA-v0","SeqOfColors3-Long-VLA-v0","SeqOfColors5-Long-VLA-v0","SeqOfColors7-Long-VLA-v0","ChainOfColors3-Long-VLA-v0","ChainOfColors5-Long-VLA-v0","ChainOfColors7-Long-VLA-v0","ShellGameShuffleTouch-Long-VLA-v0","ShellGameShuffleColorLampTouch-Long-VLA-v0","BlinkCountButtonPressEasy-Long-VLA-v0","BlinkCountButtonPressMedium-Long-VLA-v0","BlinkCountButtonPressHard-Long-VLA-v0","TraceShapeEasy-VLA-v0","TraceShapeMedium-VLA-v0","TraceShapeHard-VLA-v0","TraceShapeSeqEasy-VLA-v0","TraceShapeSeqMedium-VLA-v0","TraceShapeSeqHard-VLA-v0","TimedTransferEasy-VLA-v0","TimedTransferMedium-VLA-v0","TimedTransferHard-VLA-v0","TimedTransferEasy-Long-VLA-v0","TimedTransferMedium-Long-VLA-v0","TimedTransferHard-Long-VLA-v0","GatherAndRecall1-VLA-v0","GatherAndRecall3-VLA-v0","GatherAndRecall5-VLA-v0","GatherAndRecall7-VLA-v0","GatherAndRecall9-VLA-v0"],"taskListScope":"Complete 90-task canonical CSV from current VLA release.","capabilities":["Spatial/object memory","Temporal memory","Prospective/procedural recall","Sequential/checklist memory","Capacity / tracking","Reactive controls"],"metrics":"Per-split task-macro success, full per-task table and per-memory-type success; optional return diagnostics.","robots":"ManiSkill robot-arm/tabletop manipulation setup.","sensors":"RGB, proprioception and instruction for deployable policy; task_cue / oracle_info fields are privileged and must be hidden by required wrappers.","dataCollection":"PPO-oracle policy rollouts for 34 tasks and scripted motion planners for 56 tasks; collectors use privileged simulation state and retain successful trajectories.","data":"22,500 oracle trajectories (250/task), 6,306,953 transitions, RLDS and LeRobotDataset v3; 34 PPO-oracle and 56 motion-planning tasks.","protocol":"Train multitask within horizon split; canonical evaluation 50 episodes/task using seeds 4242424242–4242424291. Paper π0.5 reference evaluates 14-task subset with 20 episodes/task; report subset explicitly.","baselines":"π0.5 current-frame/no-explicit-memory baseline; PPO and motion-planning oracles for data collection."},"release":{"code":"Available","weights":"Partial","data":"Available","license":"MIT code; dataset/model terms separate","notes":"VLA suite, wrappers, manifests and data public. Legacy RL implementation retained separately."},"evidence":[{"url":"https://arxiv.org/abs/2610.00604","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://mikasarobo.github.io/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/CognitiveAISystems/MIKASA-Robo","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://github.com/CognitiveAISystems/MIKASA-Robo/blob/main/mikasa_robo_vla_envs.csv","label":"Official implementation / documentation","fields":["benchmark.tasks"],"note":"Complete canonical 90-task manifest."},{"url":"https://mikasarobo.github.io/benchmarking.html","label":"Official implementation / documentation","fields":["benchmark.protocol"],"note":"Canonical split / seed / reporting protocol."},{"url":"https://arxiv.org/abs/2610.00604","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Memory failure is confounded by open-loop action chunking and task composition in the small reference baseline.","Follow current wrapper contract to prevent privileged cue leakage; original 32-task RL suite is not the 90-task VLA release."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Partial","data":"Available","license":"MIT code; dataset/model terms separate","notes":"VLA suite, wrappers, manifests and data public. Legacy RL implementation retained separately."}},"relatedIds":[]},{"id":"scale-selection","name":"Scale and Selection: Harness Evolution","title":"Scale and Selection: What Makes Automatic Harness Evolution Work for Visual-Interface Robot Agents","date":"2026-09-30","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Studies visual-interface robot agents under repeated harness search with paired champion–challenger selection and held-out evaluation.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.39304"},"policy":{"family":"Robot agent","baseModel":"Codex GPT-5.6 Sol at maximum reasoning effort for task and optimizer agents, in separate sessions.","modelSize":"Not reported","inputs":"Browser-based 3D / point-cloud robot interface, task goal, harness context and permitted MCP interaction tools.","outputs":"Visual-interface gripper manipulation commands and revised harness artifacts retained by selection.","trainingData":"Thirty-round harness-evolution campaigns; rollout batches of different sizes measure the effect of selection noise.","trainingTime":"Thirty rounds per main campaign; each rollout has a 30-minute wall-clock limit, with independent rollouts executed concurrently.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen model weights; a persistent optimizer proposes harness revisions and paired held-out-blind selection promotes candidates.","evaluation":"Held-out LIBERO tasks; additional frozen-VLA-tool composition on RoboCasa365."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2609.39304","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["The optimizer never sees held-out results during a campaign.","The visual interface exposes structured 3D geometry and is not equivalent to raw RGB input."],"verified":"2026-10-17"},{"id":"simex","name":"SimEX","title":"SimEX: Simulation-Integrated Robotics AutoResearch","date":"2026-09-30","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Reconstructs an approximate simulation workspace, develops tools through simulation autoresearch and repairs them with a small physical interaction budget.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.38982","project":"https://robo-simex.github.io"},"policy":{"family":"Robot agent","baseModel":"Fable 5.1 coding agent; maximum reasoning effort in Stage 1 and medium in Stage 2, except in cross-agent comparisons.","modelSize":"Not reported","inputs":"Workspace image and task description, executable approximate simulation, rollout feedback and a bounded set of real-robot trials. The toolbox uses raw RGB-D and robot proprioception, with no privileged object state; it can query IK and reachability.","outputs":"Revised simulator and robot toolbox, then task programs generated from the frozen optimized toolbox.","trainingData":"Simulation autoresearch plus five on-robot adaptation trials per task; final evaluation does not revise the toolbox.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen coding model; joint simulation / toolbox development, bounded real-feedback repair and frozen-toolbox testing.","evaluation":"Seven physical task settings; five adaptation / evaluation seeds yield 25 final evaluation trials per method–task pair."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2609.38982","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["The reconstructed sandbox is an executable approximation, not a claimed precisely calibrated or photorealistic digital twin.","Five physical adaptation trials precede final evaluation and must be counted as adaptation access."],"verified":"2026-10-17"},{"id":"tacdyn-wam","name":"TacDyn-WAM","title":"TacDyn-WAM: Learning Implicit Tactile Dynamics in a Heterogeneous Visuo-Tactile World Action Model","date":"2026-09-30","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Models visual futures and implicit tactile dynamics in separate latent spaces, while a read-only memory supplies current contact state.","institution":"Tsinghua University AIR","venue":"Preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.00638"},"policy":{"family":"World-action model","baseModel":"InternVLA-A1 3B; Qwen3-VL-2B understanding expert; TacRep encoder","modelSize":"≈3.6B total; implicit tactile predictor ≈0.4B","inputs":"RGB history, tactile history, language and robot state","outputs":"50-step action chunks; visual/tactile latent predictions and temporal changes","trainingData":"Per-task UniVTAC demonstrations; 60 real demonstrations for each of five tasks; optional modest tactile pretraining","trainingTime":"Four-stage recipe; wall-clock not reported","trainingHardware":"Pretraining: 8 H100 80GB; real-task fine-tuning: 4 H100 80GB","controlRate":"Not reported","trainingMethod":"TacRep masked temporal learning and relational distillation; staged joint policy training","evaluation":"Eight UniVTAC tasks; Stack Cups, Remove Plug, Insert Plug, Unscrew Cup Lid, Wipe Whiteboard"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."},"evidence":[{"url":"https://arxiv.org/abs/2610.00638","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://arxiv.org/html/2610.00638","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Base WAM is pretrained; “demonstrations only” refers to tactile policy adaptation.","No public code or checkpoint link was verified in the inspected manuscript."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."}},"relatedIds":[]},{"id":"tacex-curiosity","name":"TacEx — tactile curiosity","title":"Tactile Curiosity Drives Robot Interaction","date":"2026-09-30","year":2026,"collections":["policy"],"categories":["tactile-foundation","visual-precision-rl"],"resourceType":"policy","summary":"Modality-weighted dynamics disagreement directs exploration toward useful physical contact.","institution":"ETH Zürich / University of California, Berkeley","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.40134"},"policy":{"family":"RL","baseModel":"Ensemble dynamics / Max Info RL; tactile diffusion steering of frozen π0 action prior in an additional setting.","modelSize":"Not reported","inputs":"Visual/state/tactile observations and per-modality prediction uncertainty.","outputs":"Exploratory robot actions or diffusion-noise steering actions.","trainingData":"Reward-free robot-environment interaction for data collection, downstream offline pick-and-place learning; eight LIBERO-90 tasks in frozen-VLA post-training study.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Intrinsic tactile uncertainty maximization; offline downstream learning and constrained online steering experiments.","evaluation":"Simulated parallel-jaw contact manipulation; task-dependent benefits, not fragile/deformable force-regulation evaluation."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2609.40134","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."}],"caveats":["Distinct from the TacEx tactile simulator.","Extrinsic-reward-free collection and later task-conditioned policy evaluation are separate phases."],"verified":"2026-10-17"},{"id":"urai","name":"Universal Robot-Agent Interface (URAI)","title":"Make Code as Policy Great Again: Frontier Agents Write, Call, and Evolve Robot Tools","date":"2026-09-30","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Separates an offline programming agent that writes and validates tools from an execution agent that selects their parameters through a shared GUI / API backend.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.39018"},"policy":{"family":"Robot agent","baseModel":"Frozen frontier agents including GPT-6 Astra, Claude Fable 5.1, Opus 5.5 and DeepSeek V4 Flash.","modelSize":"Not reported","inputs":"Camera observations, calibrated metric depth, robot state, tool feedback and a validated tool collection.","outputs":"Parameterized reusable robot tools; the backend back-projects image points, checks poses and executes multi-phase motions locally.","trainingData":"No base-model weight training; between-episode programming and validation improve the tool collection.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"The AgileX motion backend executes at 50 Hz; this is local control, not the frontier-agent query rate.","trainingMethod":"Frozen weights; programming-agent revisions occur between episodes, while evaluated tool versions are frozen within an episode.","evaluation":"Five selected RoboDojo tasks plus real-robot tasks, with trial counts and preparation budgets reported separately."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2609.39018","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["The paper acknowledges human programming guidance; it is not a fully unsupervised policy-discovery protocol.","Published leaderboard references are not paired with the paper's five-task evaluations."],"verified":"2026-10-17"},{"id":"cogwam","name":"CogWAM","title":"CogWAM: Aligning Semantic Cognition with World Action Modeling via Event-Driven Interfaces","date":"2026-09-29","year":2026,"collections":["policy"],"categories":["generalist-foundation","agent-rsi-icl"],"resourceType":"policy","summary":"Maintains explicit completed-event and active-subtask state through event-triggered KEEP/UPDATE decisions, conditioning latent-world learning and action generation.","institution":"Horizon Robotics / Xi’an Jiaotong University","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.37721","project":"https://horizonrobotics.github.io/CogWAM/","code":"https://github.com/HorizonRobotics/CogWAM","weights":"https://huggingface.co/HorizonRobotics/CogWAM"},"policy":{"family":"World-action model","baseModel":"RynnBrain1.1-2B semantic backbone, world/action MoT and frozen DINOv3-B future-feature teacher.","modelSize":"4.19B trainable; 4.27B including frozen teacher. Components: 2.72B VLM, 442M world stream, 1.01B action stream and 85.7M teacher.","inputs":"RGB views, language, robot state and persistent semantic state predicted online.","outputs":"25-action, 14D dual-arm horizon; replans every ten steps. Future-world latents are training-only.","trainingData":"RoboDojo demonstrations with semantic progress annotations; physical task-specific data. Complete corpus volume not reported.","trainingTime":"RoboDojo: 50k steps. Wall-clock duration not reported.","trainingHardware":"64 H20 GPUs, described as eight nodes with eight GPUs; global physical batch 768.","controlRate":"Physical control at 30 Hz, replanning every ten steps. Reported Place in Bag inference averages 146.7 ms with KEEP/UPDATE state handling.","trainingMethod":"Action flow matching, future DINO-latent prediction and semantic-state learning; boundary-aware sampling and scheduled sampling.","evaluation":"RoboDojo, BiCoord and physical manipulation; event-memory and semantic-interface controls."},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"MIT code and released weights, as specified by the author release. RynnBrain and DINO components retain separate terms.","notes":"Availability concerns author-linked artifacts; no training or installation reproduced."},"evidence":[{"url":"https://arxiv.org/abs/2609.37721","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/HorizonRobotics/CogWAM","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://horizonrobotics.github.io/CogWAM/","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."}],"caveats":["The world stream is removed at inference; actions do not consume an imagined future.","Public paper and Horizon Robotics release supersede the adapter’s stale not-public claim."],"verified":"2026-10-17"},{"id":"equivdp3","name":"EquivDP3","title":"EquivDP3: A SIM(3)-Invariant Point-Cloud Encoder for Data-Efficient Humanoid Loco-Manipulation","date":"2026-09-29","year":2026,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"A SIM(3)-invariant point-cloud encoder improves low-data humanoid diffusion control over a frozen locomotion executor.","institution":"KAIST","venue":"arXiv preprint","modalities":["Depth","Point cloud","Proprioception"],"environments":["Simulation"],"embodiments":["Humanoid","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.36575"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2609.36575","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."}],"caveats":["Results are simulation only; low-data improvements diminish near the high-data benchmark ceiling."],"verified":"2026-10-17","policy":{"family":"Diffusion","baseModel":"DP3 with vector-neuron encoder, frozen RL locomotion policy and differential arm IK.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Depth-derived point cloud and proprioception.","outputs":"Whole-body command chunks executed by locomotion and IK controllers.","trainingMethod":"Behavior cloning; shared DP3 decoder isolates point-encoder comparisons.","trainingData":"IsaacLab Mimic produces 200 and 360 episodes; 5–100 demonstration budgets.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Two simulated Unitree G1 loco-manipulation tasks with in-/out-of-distribution tests.","controlRate":"~6 Hz high-level chunks; 50 Hz frozen low-level execution."}},{"id":"astra-embodied-policies","name":"GPT-6 Astra as Embodied Policies","title":"Systematically Exploring the Capabilities of GPT-6 Astra as Embodied Policies","date":"2026-09-29","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Evaluates direct Astra-generated commands with analytic control and hybrid configurations that review learned-policy proposals or command frozen whole-body controllers.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Dexterous hand","Mobile manipulator","Humanoid"],"links":{"paper":"https://arxiv.org/abs/2609.38537","project":"https://galaxygeneralrobotics.github.io/astra-policy/","code":"https://github.com/anonymous-report-421/GPT-as-Policy"},"policy":{"family":"Robot agent","baseModel":"GPT-6 Astra. Hybrid conditions additionally use frozen task policies such as π0.5, ScaleTrack or HumanoidGPT, depending on the domain.","modelSize":"Not reported","inputs":"Study-specific observations: RGB and robot state in manipulation; dexterous studies can supply object pose / velocity, target state, contacts and task-native errors. Allegro includes 16 signed three-axis tactile readings (48 scalars). HumanoidBench provides privileged state / geometry / guidance; SIMPLE relative-depth variants infer depth from RGB.","outputs":"Direct: commands interpreted by analytic control. Hybrid: acceptance or modification of VLA proposals, or high-level commands / motion references for a frozen controller.","trainingData":"No Astra robot-specific weight training; learned policies and controllers retain their own prior training.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Some humanoid conditions use a 50 Hz controller and 500 Hz physics / PD loop; these are not Astra inference rates.","trainingMethod":"Frozen foundation model with direct or hybrid execution, evaluated separately across six embodied domains.","evaluation":"Gripper manipulation, dexterous manipulation, mobile manipulation, navigation, locomotion and humanoid loco-manipulation, with separate sample sizes per study."},"release":{"code":"Partial","weights":"Not applicable","data":"Partial","license":"MIT for the repository; external assets and APIs retain separate terms.","notes":"The public evaluation repository covers a RoboDojo / RoboLab subset, not the complete six-domain evaluation."},"evidence":[{"url":"https://arxiv.org/abs/2609.38537","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/anonymous-report-421/GPT-as-Policy","label":"Author-linked repository","fields":["links.code"],"note":"Repository identity; code and checkpoint release status are stated separately."},{"url":"https://github.com/anonymous-report-421/GPT-as-Policy","label":"Official repository README and LICENSE","fields":["release","caveats"],"note":"Implementation scope and repository license are verified separately from model access and full benchmark reproducibility."}],"caveats":["The accompanying GPT-as-Policy technical report and web report are linked artifacts of this study rather than an additional base GPT model entry.","Direct end-effector commands and hybrid learned-controller results are not the same action interface.","Some dexterous and HumanoidBench experiments expose privileged object or task state; these are not uniformly RGB-only policies. Allegro tactile observations are scoped to that hand study, and inferred relative depth is not measured depth."],"verified":"2026-10-17"},{"id":"roboharn-evo","name":"RoboHarn-Evo","title":"RoboHarn-Evo: Evolving Hierarchical Physical Knowledge for Self-Improving Robotic Manipulation","date":"2026-09-29","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Retains task-level and action-level physical knowledge and repairs or deactivates entries using interaction evidence.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.37583","code":"https://github.com/RUCKBReasoning/RoboHarn-Evo"},"policy":{"family":"Robot agent","baseModel":"Qwen3.8-27B, GPT-5.5 and GPT-6 variants with a fixed perception / executor harness.","modelSize":"27B for Qwen3.8-27B; hosted-model parameter counts are not reported.","inputs":"RGB-D observations, object segmentation, robot state, task progress and action history condition runtime memory and retrieved task / action knowledge.","outputs":"Robot operations plus revised hierarchical knowledge about task structure and object-relative action effects.","trainingData":"80 interaction rollouts in the reported RMBench evolution experiment, then held-out evaluation and frozen-knowledge transfer.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen model weights; an inner execution loop records outcomes and an outer evolution loop updates hierarchical physical knowledge.","evaluation":"Six RMBench tasks, knowledge-repair audits and zero-shot frozen-knowledge transfer to RoboDojo. Three real AgileX Cobot Magic tasks compare transferred frozen knowledge with knowledge refined through physical interaction: Cover with Assigned Cups, Press by Digit–Color Mapping, and Uncover, Count, and Press."},"release":{"code":"Available","weights":"Not applicable","data":"Not verified","license":"Apache-2.0 original code, verified from LICENSE; vendored code, external policies, datasets and simulator assets retain separate terms.","notes":"Public implementation contains the agent, hierarchical knowledge evolution, RMBench/LIBERO-Pro/RoboDojo integrations and run configurations. External simulator assets and policy/SAM3 checkpoints are installed separately; no new method-specific weight or full rollout-corpus release was verified."},"evidence":[{"url":"https://arxiv.org/abs/2609.37583","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/RUCKBReasoning/RoboHarn-Evo","label":"Author-linked repository","fields":["links.code"],"note":"Repository identity; code and checkpoint release status are stated separately."},{"url":"https://github.com/RUCKBReasoning/RoboHarn-Evo","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["Knowledge-editing accuracy and manipulation success are separate measurements.","The RoboDojo transfer condition freezes knowledge learned on RMBench rather than evolving on the target test set."],"verified":"2026-10-19"},{"id":"roboskill","name":"RoboSkill","title":"Explore, Execute, Evolve: A Skill Acquisition and Reuse Loop for Embodied Agents","date":"2026-09-29","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"An Explore–Execute–Evolve loop acquires and reuses robot skills, including contact-based exploration that combines visual and tactile feedback.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Depth","Tactile","Force/Torque","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.37810","code":"https://github.com/SII-dannyXSC/RoboSkill"},"policy":{"family":"Robot agent","baseModel":"Frozen VLM agents evaluated with GPT-6 Astra, Sol, Fable 5.1 and Opus 5.","modelSize":"Not reported","inputs":"Task, RGB-D, proprioception, interaction history and skills. The LIBERO backend exposes measured simulated end-effector force and torque (three scalars each); the real Piper has tactile sensors on both gripper fingers. Controller-commanded joint torque is a separate field.","outputs":"Exploratory or task-execution robot actions and reusable text / code skills; contact probes can estimate missing quantities such as table height.","trainingData":"Interactive exploration and retained successful skills; no task-specific VLM weight training.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen model weights; skill acquisition, contact-informed execution, and cross-task skill retrieval / composition.","evaluation":"LIBERO-10 and twelve physical tasks (six easy, six hard). Astra uses ten trials per physical task; Sol uses five trials per easy task. Real tactile removal is tested on Remove Test Tube."},"release":{"code":"Partial","weights":"Not applicable","data":"Not verified","license":"MIT for the public repository.","notes":"LIBERO simulation agent / SDK source is public; the complete real-robot setup and artifacts are not bundled."},"evidence":[{"url":"https://arxiv.org/abs/2609.37810","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/SII-dannyXSC/RoboSkill","label":"Author-linked repository","fields":["links.code"],"note":"Repository identity; code and checkpoint release status are stated separately."},{"url":"https://github.com/SII-dannyXSC/RoboSkill/blob/main/libero/src/libero_gateway/backend.py","label":"Official LIBERO observation backend","fields":["modalities","policy.inputs","release"],"note":"The extended proprioception exposes robot0_eef_force and robot0_eef_torque; these are distinct from controller-commanded joint torque."}],"caveats":["The paper uses tactile terminology across force-related simulation observations and real finger sensors; optical tactile images and wrist force/torque are not interchangeable.","The policy family is an agent with skill reuse, rather than a supervised tactile imitation model despite its touch-conditioned execution.","The simulated without-tactile ablation removes both force-related fields and gripper proprioceptive state; it is not an isolated finger-touch ablation. The real Remove Test Tube ablation removes actual finger tactile observations."],"verified":"2026-10-17"},{"id":"appl","name":"Agent Priors-guided Policy Learning (APPL)","title":"Agent Priors-guided Policy Learning","date":"2026-09-28","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A construction agent designs structural priors and trains skill specialists, while a runtime agent composes skills using the same explicit generalization priors.","institution":"Not reported","venue":"Preprint","modalities":["Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.35690"},"policy":{"family":"Hybrid","baseModel":"GPT-6 Astra at maximum reasoning effort designs candidates; skill policies use a conditional 1D U-Net diffusion backbone. The ManiSkill long-horizon experiments use GPT-5.6 Sol at extra-high effort for the agent roles.","modelSize":"Not reported","inputs":"Demonstrations and standardized state histories for skill learning; runtime task / state and explicit skill priors for composition.","outputs":"Prior-specific diffusion skill policies, structural representations / objectives and runtime skill-handoff decisions.","trainingData":"Demonstration segments for individual skills; training windows use two observations and sixteen future actions.","trainingTime":"The ManiSkill setting uses 20,000 updates per APPL specialist and 60,000 per Diffusion Policy / SinglePrior baseline; elapsed duration is not reported.","trainingHardware":"Not reported","controlRate":"ManiSkill: 20 Hz joint-position control after shared IK, with a binary gripper; MetaWorld uses an 80 Hz native control setting. These are task-specific execution rates.","trainingMethod":"Agent-written representations / objectives guide learned-policy training. Candidates can add encoders, heads or diffusion channels; runtime composition does not replace offline skill learning.","evaluation":"MetaWorld skill generalization and long-horizon ManiSkill recomposition / out-of-distribution tasks."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2609.35690","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["The main specialist setup is state-based rather than a claimed RGB-D or tactile policy.","This is trained skill construction plus agentic composition, not only test-time demonstration prompting."],"verified":"2026-10-17"},{"id":"modex","name":"MoDex","title":"MoDex: Towards Universal Multi-Object Dexterous Grasping","date":"2026-09-28","year":2026,"collections":["policy"],"categories":["rgbd-3d","visual-precision-rl"],"resourceType":"policy","summary":"A single-step residual RL policy adapts reference grasp trajectories to multi-object layouts; a separate distilled visual student transfers to real dexterous grasping.","institution":"Peking University","venue":"CoRL 2026","modalities":["RGB","Point cloud","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2610.23888","code":"https://github.com/Aurora-Xiong/MoDex","weights":"https://github.com/Aurora-Xiong/MoDex/tree/main/checkpoints"},"release":{"code":"Partial — state-based RL train/evaluation","weights":"Partial — ten binary-reward state-policy .pt checkpoints","data":"Partial — robot/object assets; full visual-student training corpus not verified","license":"Apache-2.0 root implementation and checkpoints/LICENSE; third-party modules/assets retain separate terms.","notes":"Ten actual state-based PPO checkpoints and reproducible configurations are public. The paper’s separate RGB student/real-robot deployment release is not established by these state-policy files."},"evidence":[{"url":"https://arxiv.org/html/2610.23888","label":"Original full paper, methods and evaluation","fields":["title","date","institution","summary","modalities","policy","model","benchmark","benchmark.dataCollection","caveats"],"note":"Publication date is arXiv v1; implementation and deployment observations are distinguished from training supervision, generated geometry and base-model identity."},{"url":"https://github.com/Aurora-Xiong/MoDex","label":"Official artifact: actual source files, inventory and license","fields":["links","release"],"note":"329 files, ten checkpoints/*/model_8000.pt and checkpoint-specific Apache license; release README explicitly says state-based policies."}],"caveats":["State teacher consumes object poses/point clouds and emits one residual before open-loop trajectory execution. This differs from the visual student.","Physical cameras are RealSense D435i, but depth is explicitly discarded; real inference uses two RGB views and proprioception at approximately3 Hz, without tactile/F/T.","Do not count the released state-policy checkpoints as the real visual student."],"verified":"2026-10-21","policy":{"family":"Hybrid","baseModel":"PPO reference-trajectory residual teacher; distilled visual VLM/DiT action student.","modelSize":"Not reported","inputs":"State teacher: initial object pose/point clouds and robot state. Physical student: two RGB images and 13D arm/hand state.","outputs":"Reference wrist/hand trajectory corrections for teacher;13D low-level action chunks for visual student.","trainingMethod":"Single-step sparse-reward residual PPO with spatially adaptive single/sequential strategies, then distillation and visual behavior cloning.","trainingData":"Simulation training objects and 30,000 successful visual episodes/1.2M frames from sim at3 Hz.","trainingTime":"Not reported","trainingHardware":"State training single GPU/8,000 parallel environments; physical visual inference RTX 4090.","controlRate":"Physical visual student≈3 Hz; not the single-shot teacher’s inference rate.","evaluation":"IsaacGym proximal/separated layouts and cross-hand generalization; Franka FR3+Inspire real zero-shot student transfer."}},{"id":"physical-coding","name":"Physical Coding","title":"Self-Evolving Coding Agents: From Digital Programs to Physical-World Intelligence","date":"2026-09-28","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Couples an executable account of task state and constraints with executable control programs, using independently checked physical outcomes to revise the system.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.35432"},"policy":{"family":"Hybrid","baseModel":"Coding agents and task-specific local robot executors, including Qwen / GR00T-based components in the reported system.","modelSize":"Not reported","inputs":"Task goals, explicit world-state representations, visual / execution feedback, typed tools and retained code / skill artifacts. Deployed perception tools can return depth and poses from public RGB-D observations; simulator-state reads are excluded in the stated constrained setting.","outputs":"Code-as-World predicates and state models; Code-as-Policy task decompositions, tool calls, verification and recovery programs.","trainingData":"Physical and simulated execution evidence for code / system refinement; action-model training remains component-specific.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"System-level code evolution separates world representation from control, with external verification and rollback. This is broader than simply generating a robot program once.","evaluation":"LIBERO and additional simulated / physical embodied-control case studies, with system-component ablations."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2609.35432","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["The paper uses coding broadly across controllers, evaluators and workflows; it is not one fixed neural-policy architecture.","Executable state representations and task predicates are supplied system components, not tactile sensing."],"verified":"2026-10-17"},{"id":"roboicl","name":"RoboICL","title":"RoboICL: Embodied In-Context Learning with GPT-6 Astra","date":"2026-09-28","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A frozen VLM controls robots using a shared observation–action–receipt grammar for demonstration context and interaction memory, without an external skill library.","institution":"Samsung Robotics eXperience; Shanghai Jiao Tong University","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.34261","project":"https://github.com/Mosi-AI/RoboICL","code":"https://github.com/Mosi-AI/RoboICL"},"policy":{"family":"Robot agent","baseModel":"GPT-6 Astra, frozen hosted VLM.","modelSize":"Not reported","inputs":"Task instruction and rubric, head and bilateral-wrist RGB triptych, joint / end-effector / gripper state, camera calibration, demonstration blocks and bounded interaction history.","outputs":"The act tool returns an H × 14 array of bimanual incremental controls and gripper commands; execution receipts report realized outcomes.","trainingData":"No target-task weight training. RoboDojo uses one demonstration where available; Open uses interaction memory alone. Real-robot experiments use three demonstrations.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"The RoboDojo adapter executes the action matrix at 25 Hz; this is not the VLM API query rate.","trainingMethod":"Frozen weights; in-context demonstrations and anchored interaction memory share one record grammar. No learned VLA or external coding runtime.","evaluation":"RoboDojo main evaluation over 30 tasks; additional controller and memory ablations; three single-arm real-robot tasks."},"release":{"code":"Available","weights":"Not applicable","data":"Partial","license":"MIT for the public repository.","notes":"Robot agent and evaluation code are public; the hosted VLM and benchmark demonstrations have separate access conditions."},"evidence":[{"url":"https://arxiv.org/abs/2609.34261","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/Mosi-AI/RoboICL","label":"Author-linked repository","fields":["links.code"],"note":"Repository identity; code and checkpoint release status are stated separately."}],"caveats":["Reported Overall progress scores are not final task-success rates.","The Open category is zero-shot; it does not use the one-demonstration setting of the other categories.","The main thirty-task table uses task-dependent twenty / fifty evaluations; discontinued waived tasks are assigned zero. This is not the identical fifty-trial official-board protocol."],"verified":"2026-10-17"},{"id":"robot-icl-survey","name":"Robot ICL — Methods and Applications","title":"In-Context Learning for Robots: Methods and Applications","date":"2026-09-28","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"survey","summary":"A review connecting demonstrations, interaction and retained experience to executable robot behavior through four control interfaces.","institution":"Knowin AI; HKUST(GZ); CUHK; Tongji; Tsinghua; HIT Shenzhen; Westlake; Tianjin University","venue":"Preprint · Survey","modalities":[],"environments":["Literature review"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2609.36012","project":"https://jethrojames.github.io/awesome-robots-icl/","code":"https://github.com/JethroJames/awesome-robots-icl","data":"https://arxiv.org/src/2609.36012v1/anc/Supplement-S1/corpus.json"},"survey":{"scope":"Robot in-context learning across manipulation and navigation, with transfer, memory and physical self-improvement.","taxonomy":["Context-conditioned policies","Geometric demonstration transfer","World-model-based control","Skill- and agent-based execution"],"adaptationRegimes":["Fixed neural weights with supplied context","Interaction history and persistent memory","Code or skill revision","Neural parameter updates as a distinct comparison"],"contextSources":["Demonstrations","Instructions and corrections","Interaction outcomes","Retrieved experience"],"evaluationAxes":["Dependence on teaching","Object, scene and embodiment transfer","Retained-experience benefit","Physical execution","Teaching and inference cost"],"corpus":"100-page paper. Supplement S1 snapshot dated 2026-09-29: 412 numbered references, including 260 method entries. These are author-defined bibliography counts.","reviewMethod":"Narrative synthesis with a public coded corpus, reference locations and reported-comparison tables.","coverageLimits":"Navigation and general learning foundations exceed this atlas's manipulation scope. S5/S6 learning horizons are research objectives, not capabilities established for every cited method."},"release":{"code":"Available — bibliography and reproduction tooling; no new policy implementation","weights":"Not applicable — survey","data":"Available — coded literature corpus and comparison tables","license":"MIT for companion repository; paper and third-party figures retain their own rights","notes":"The Code and Data links refer to literature tooling and annotations, not robot training data or model weights."},"evidence":[{"url":"https://arxiv.org/abs/2609.36012","label":"Primary survey and first submission","fields":["title","date","survey","institution"],"note":"PDF Sections 2–8; Figure 7 compares four context-to-control interfaces."},{"url":"https://arxiv.org/src/2609.36012v1/anc/Supplement-S1/summary.json","label":"Author-coded Supplement S1 snapshot","fields":["survey.corpus","survey.reviewMethod"],"note":"Version 2026-09-29-primary-source-refresh; numbered-reference counts, not an independent count of manipulation policies."},{"url":"https://jethrojames.github.io/awesome-robots-icl/","label":"Official survey project","fields":["summary","survey","links"],"note":""},{"url":"https://github.com/JethroJames/awesome-robots-icl","label":"Official bibliography and reproduction tooling","fields":["release"],"note":""},{"url":"https://github.com/JethroJames/awesome-robots-icl/blob/main/LICENSE","label":"Companion repository MIT license","fields":["release.license"],"note":""}],"caveats":["Survey, not an executable policy; deployment inputs, model size, training resources and control rate do not apply.","The arXiv HTML conversion ends early; the full PDF and attached S1 files support this record."],"relatedIds":["icrt","instant-policy","ricl","vima","code-as-policies","enpire","worldscape-policy2","roboicl"],"verified":"2026-10-17","surveyAuditId":"robot-icl-2026"},{"id":"uni-vlat","name":"Uni-VLaT","title":"Uni-VLaT: Whole-Body Tactile Adaptation of VLA Policies for Humanoid Loco-Manipulation","date":"2026-09-28","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Distributed whole-body skin conditions humanoid VLA adaptation for sustained-contact loco-manipulation.","institution":"Tsinghua University / Bei hang University / Communication University of China / University of Hong Kong","venue":"Preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Real world"],"embodiments":["Humanoid","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.35450","project":"https://ggkiller-air.github.io/Uni-VLaT/","data":"https://huggingface.co/datasets/landauros/Uni-VLaT"},"policy":{"family":"VLA","baseModel":"Isaac-GR00T andπ0.5 variants with region-temporal tactile encoders; SONIC low-level whole-body controller.","modelSize":"Not reported","inputs":"Stereo RGB, language, proprioception and 624 effective torso/arm tactile channels with four-frame causal history.","outputs":"40-step chunks of 64D SONIC motion tokens; separate pretrained controller produces joint targets.","trainingData":"Nominal~50 demos/task; cleaned datasets 52–63 episodes/task across five real tasks; all retained episodes used in training split.","trainingTime":"50k steps; wall-clock Not reported.","trainingHardware":"Isaac-GR00T4H100;π0.5 variant 8 H100.","controlRate":"Motion tokens 50 Hz; nominal policy request 2.5 Hz with latency compensation; low-level motor loop 500 Hz.","trainingMethod":"50 k-step imitation adaptation with auxiliary future sensory prediction; predictive heads removed at inference.","evaluation":"Unitree G1: five sustained-contact tasks including hugging, box handling and composed cleanup; real rollout SR and cleanup stage score."},"release":{"code":"Not verified","weights":"Not verified","data":"Available","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2609.35450","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://ggkiller-air.github.io/Uni-VLaT/","label":"Official author project","fields":["links","release"],"note":"Live project artifact links inspected on2026-10-17."},{"url":"https://huggingface.co/datasets/landauros/Uni-VLaT","label":"Author-linked released dataset","fields":["links","release"],"note":"Public repository file inventory inspected; component/data scope follows model/dataset card."}],"caveats":["Stereo RGB cameras are not evidence of a measured depth input.","This is whole-body contact manipulation, not dexterous fingertip insertion.","DP baseline failed pre-execution validity checks and isN/A, not 0% physical success."],"verified":"2026-10-17"},{"id":"contactkernels","name":"ContactKernels","title":"ContactKernels: Adaptive Proposals using Geometry-COnditioned Wrenches for Contact-Rich Manipulation","date":"2026-09-26","year":2026,"collections":["policy"],"categories":["visual-precision-rl"],"resourceType":"policy","summary":"Geometry-conditioned wrist wrenches estimate contact location and normal, adapting the sampling proposals of model-predictive insertion control.","institution":"University of Edinburgh","venue":"Preprint","modalities":["Force/Torque","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2610.23887"},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The inspected primary sources do not establish a public implementation, model checkpoint or dataset download. Paper figures retain their original rights."},"evidence":[{"url":"https://arxiv.org/html/2610.23887","label":"Original full paper, methods and evaluation","fields":["title","date","institution","summary","modalities","policy","model","benchmark","benchmark.dataCollection","caveats"],"note":"Publication date is arXiv v1; implementation and deployment observations are distinguished from training supervision, generated geometry and base-model identity."}],"caveats":["Wrist F/T measurements are not skin tactile; known local geometry supports contact inference.","No RGB-D observation or learned neural policy is required by this controller.","The arXiv identifier month is October but its recorded v1 date is September 26."],"verified":"2026-10-21","policy":{"family":"Model-based planning","baseModel":"Predictive Sampling or MPPI with contact-adaptive proposals; MuJoCo dynamics.","modelSize":"Not reported","inputs":"Robot state, measured six-axis wrench and known peg/contact geometry.","outputs":"Optimized end-effector twists tracked by an operational-space controller.","trainingMethod":"No policy weight training: contact estimates bias proposal means and shape covariance online.","trainingData":"Simulation rollouts for sampling-based optimization; no learned demonstration dataset claimed.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Real controller20 Hz; ATI F/T sensor100 Hz.","evaluation":"MuJoCo insertion geometries and KUKA IIWA 7/ATI sensor real insertion; reported real success 53.3%→90% and mean insertion time 33% lower."}},{"id":"re-0","name":"RE-0","title":"RE-0: Verified Recursive Improvement of Embodied Code-as-Policy Agents through Local On-Policy Distillation","date":"2026-09-26","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Verifies bounded repairs to failing code-policy segments through paired counterfactual rollouts, then distills accepted interventions into a standalone student.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.32416"},"policy":{"family":"Robot agent","baseModel":"Qwen3.8-27B student; qwen3.8-max teacher / diagnostic API.","modelSize":"27B student backbone; teacher parameter count is not disclosed.","inputs":"Task and code-policy trajectory context; simulator snapshots and paired rollouts are used to verify proposed local repairs during learning.","outputs":"Standalone student-generated code-as-policy programs; the teacher supplies bounded replacement segments during training.","trainingData":"Only verified local intervention rows enter cold-start SFT and recursive on-policy distillation, with retention data; admission varies by task.","trainingTime":"Reported student setup includes 120 optimization steps, batch size 2 and LoRA rank 16 / alpha 32; absolute duration is not reported here.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Student weights are updated with LoRA. Repair candidates preserve the execution prefix and pass a lower-confidence-bound paired-rollout gate before distillation.","evaluation":"Five simulated manipulation tasks with held-out seeds; AgileX PiPER zero-shot sim-to-real trials on selected tasks."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2609.32416","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["This is parameter adaptation rather than only editing a prompt or persistent skill file.","Counterfactual snapshot access is a training verification privilege."],"verified":"2026-10-17"},{"id":"robofoundry","name":"RoboFoundry","title":"RoboFoundry: System-as-Policy Evolution for Self-Learning Embodied Agents","date":"2026-09-26","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Evolves an executable system of context, memory, hierarchical skills and recovery routines while binding skills to different robot embodiments.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Mobile manipulator","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.32862"},"policy":{"family":"Robot agent","baseModel":"Frozen backbone variants include GPT-6 Astra, GPT-5.5, Qwen3.7-Plus, Qwen3.8-27B and GLM5.3-Flash.","modelSize":"Not reported","inputs":"Task stream, robot observations, semantic embodiment bindings and persistent context / skill / recovery artifacts.","outputs":"Task actions through executable skills and promoted revisions of the broader system-as-policy.","trainingData":"Sequential evolution begins with the first scored episode; all evolution trajectories are retained in the reported online score.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen model weights; act–reflect–refine changes general system artifacts and task-specific realization. Initial atomic primitives are handcrafted and verified.","evaluation":"EmbodiedBench, including its robot-manipulation subset, plus additional embodiment / physical studies."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2609.32862","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["The 1,128-task EmbodiedBench total includes planning, navigation and manipulation; it is not a count of robot manipulation tasks alone.","The initial atomic primitive library is supplied rather than discovered from scratch."],"verified":"2026-10-17"},{"id":"dualmanip","name":"DualManip","title":"DualManip: Agentic Dynamic Manipulation via Dual-Path Semantic Reasoning and Geometric Adaptation","date":"2026-09-25","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Separates slow semantic planning from fast learned geometric correspondence updates, adapting grasp contacts to object motion and deformation.","institution":"Tsinghua University; Harbin Institute of Technology (Shenzhen); Beijing Institute of Control Engineering","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.31112","project":"https://lichengxi1.github.io/Dualmanip"},"policy":{"family":"Hybrid","baseModel":"GPT-5.6 Terra semantic VLM, Sofar orientation inference, AnyGrasp and a trained shape-adaptive correspondence network; Qwen3.5-2B is a latency-study variant.","modelSize":"Local latency-study VLM: 2B; total system size and correspondence-network parameters are not reported.","inputs":"Task, live RGB-D point clouds and a pre-scanned object reference template; geometric feasibility checks can request semantic replanning.","outputs":"Task-conditioned grasp plan, continuously reconstructed grasp contacts / poses and selective fallback to the semantic agent.","trainingData":"One hundred RGB-D observations per object from each camera viewpoint train correspondence learning; reference templates are fixed before evaluation.","trainingTime":"Not reported","trainingHardware":"Local perception / control runs on an RTX 3090 workstation; the paper does not separately specify correspondence-training hardware.","controlRate":"Mean geometric adaptation latency is 138.7 ms in the reported single-change study; this is not a guaranteed arm-servo frequency.","trainingMethod":"Offline correspondence-network training plus frozen semantic models / grasp tools; live geometric adaptation avoids repeated semantic reasoning until feasibility fails.","evaluation":"Six real KUKA LBR iiwa tasks under static, single-change and continuous dynamic conditions, including precision assembly; fifteen trials per object / condition / method."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The original paper is public; code, model and dataset release scopes are verified separately where established."},"evidence":[{"url":"https://arxiv.org/abs/2609.31112","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."}],"caveats":["Object-specific observations and a pre-scanned template are preparation requirements.","Reported semantic-agent latency and geometric-adaptation latency refer to different computation paths."],"verified":"2026-10-17"},{"id":"find","name":"FIND","title":"Find Something You Can't Do: Agentic Real-World Reinforcement Learning for Self-Improving VLA Models","date":"2026-09-25","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Runs real-world policy improvement in a persistent workspace by choosing feasible weak tasks and evaluating outcomes from before / after images.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.32069","project":"https://fangyzzz.github.io/FIND.github.io","code":"https://github.com/FangYzzz/FIND"},"policy":{"family":"Hybrid","baseModel":"Frozen π0.5 base policy plus an off-policy learned residual; GPT-5.6 Terra supplies feasible-task selection and paired-image success evaluation.","modelSize":"Not reported","inputs":"Current RGB scene, task-library context, recent task performance, policy observations and pre / post execution images.","outputs":"A chosen feasible task and binary outcome; a learned residual corrects the frozen VLA's actions.","trainingData":"Eight reversible manipulation tasks in one persistent workspace; a representative autonomous run collects 456 episodes over six hours.","trainingTime":"Representative autonomous interaction run: six hours; this is collection / learning-loop duration rather than isolated gradient-update time.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Residual-policy weights are updated by off-policy RL, while the base VLA remains frozen. A VLM-guided weakness curriculum schedules feasible tasks without resetting after each episode.","evaluation":"Eight real-world reversible tasks and human-assessed post-improvement success; VLM selector / judge accuracy is separately tested."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Not reported — no repository license found","notes":"Official paper-matched repository includes residual RL, DROID and policy training/rollout scripts; public code does not by itself grant an open-source license."},"evidence":[{"url":"https://arxiv.org/abs/2609.32069","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/FangYzzz/FIND","label":"Official implementation, README and file/license inspection on 19 October 2026","fields":["links","release"],"note":"Official paper-matched repository includes residual RL, DROID and policy training/rollout scripts; public code does not by itself grant an open-source license. Not reported — no repository license found."}],"caveats":["The task library is predefined; the agent chooses among feasible tasks rather than inventing arbitrary new tasks.","Frozen base-policy weights do not imply that the residual learning system is training-free."],"verified":"2026-10-19"},{"id":"hire","name":"HIRE","title":"HIRE: History-Conditioned Interaction Reasoning and High-Rate Execution for Visually Aliased Precision Manipulation","date":"2026-09-25","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","tactile-foundation","visual-precision-rl"],"resourceType":"policy","summary":"History-conditioned force reasoning and a high-rate interaction executor jointly resolve visual aliasing in precision manipulation.","institution":"Institute of Automation, Chinese Academy of Sciences / Shanghai Jiao Tong University","venue":"Preprint","modalities":["RGB","Force/Torque","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.30828"},"policy":{"family":"VLA","baseModel":"π0.5-initialized Interaction-State Reasoner, temporal wrench encoder / Force Perceiver and learned Interaction-Manifold Executor.","modelSize":"Not reported","inputs":"Global/wrist RGB observations, language, proprioception and ordered six-axis wrench history; insertion uses wrist view only.","outputs":"Reasoner continuous actions plus cross-rate context; executor predicts intrinsic progress, transverse correction, reference wrench and interaction regulation.","trainingData":"300 synchronized real-robot demonstrations per task, containing visual observations, wrench histories and executed actions. Learning-based baselines use the same demonstrations.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Interaction-State Reasoner at 5 Hz; Interaction-Manifold Executor at 50 Hz.","trainingMethod":"Imitation learning of history-conditioned reasoning and manifold-structured execution with physical progress/correction/wrench targets.","evaluation":"Flexiv Rizon 4S / two-finger gripper: transparent-object pick-and-place, EV connector mating/demating and quarter-turn latch actuation; stage-wise completion over 30 trials."},"release":{"code":"Not released","weights":"Not released","data":"Not released","license":"Not verified","notes":"Original abstract states code will be released upon publication."},"evidence":[{"url":"https://arxiv.org/abs/2609.30828","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."}],"caveats":["Uses six-axis wrench sensing, not skin tactile.","RGB-D cameras are present, but the paper’s runtime reasoning/execution inputs do not establish a depth stream.","Large external-view execution images in Figure 3 are visualization views, not policy camera observations."],"verified":"2026-10-17"},{"id":"internw0-delta","name":"InternW0-Δ","title":"InternW0-$\\Delta$: A World Action Model Bridging Predictive Dynamics and Actions with 20K+ Hours of Open Data","date":"2026-09-25","year":2026,"collections":["policy"],"categories":["generalist-foundation","rgbd-3d"],"resourceType":"policy","summary":"Combines video/action experts, frozen semantic context and training-only 4D distillation; predictive imprint features enable action generation without future-video rollout.","institution":"Shanghai AI Laboratory","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.31394","project":"https://internrobotics.github.io/InternW0-Delta/","code":"https://github.com/InternRobotics/InternW0-Delta","weights":"https://huggingface.co/InternRobotics/InternW0-Delta-Base"},"policy":{"family":"World-action model","baseModel":"Wan2.2-TI2V-5B video expert, ActionDiT, frozen RynnBrain1.1-2B VLM and training-only Track4World teacher","modelSize":"Full aggregate Not reported; video and frozen VLM names describe separate components","inputs":"RGB views, language, robot state and sparse visual history; no measured depth or tactile deployment channel established","outputs":"32-step actions in a masked canonical 80D interface, mapped to native embodiment controls","trainingData":"More than 20k hours processed robot/UMI/egocentric/Ego2Robot data; source coverage differs from retargeted robot-hour expansion","trainingTime":"245k pretraining steps, approximately 14 days; target posttraining separate","trainingHardware":"Pretraining uses 256 A800 GPUs with batch 16 per GPU and global batch 4,096.","controlRate":"Physical execution 30 Hz, replan every 16 actions; optimized controller-observed RTT 152.8 ms on RTX 5090","trainingMethod":"World/action flow matching, Causal Imprint future supervision, cached 4D geometry/motion distillation and asynchronous prefix-conditioned action inference","evaluation":"Simulation including LIBERO-Plus/RoboTwin C2R and four real robot platforms, including dexterous hands"},"release":{"code":"Available","weights":"Available","data":"Partial","license":"MIT code, verified from LICENSE. Third-party components and checkpoints have separate terms.","notes":"The author repository provides training, 4D distillation, LIBERO/RoboTwin/RoboDojo evaluation, and model-download documentation. Although the README banner says Hugging Face is coming soon, the model guide links four populated policy repositories; their checkpoint files were verified. Full pretraining data is not released there."},"evidence":[{"url":"https://arxiv.org/abs/2609.31394","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/InternRobotics/InternW0-Delta","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://internrobotics.github.io/InternW0-Delta/","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."},{"url":"https://github.com/InternRobotics/InternW0-Delta/blob/main/docs/models.md","label":"Author model guide","fields":["release","links.weights"],"note":"Base, LIBERO, RoboTwin, and RoboDojo policy checkpoints resolve to populated Hugging Face repositories."}],"caveats":["Track4World is a training teacher; predicted geometry does not establish a deployed depth stream.","Ego2Robot processing can generate multiple training segments/robot-hours per unique source episode."],"verified":"2026-10-17"},{"id":"phase-tactile","name":"PHASE","title":"PHASE: Compliance-Enabled Tactile Phase Retrieval for Few-Shot Insertion Learning","date":"2026-09-25","year":2026,"collections":["policy"],"categories":["tactile-il","tactile-foundation"],"resourceType":"policy","summary":"Tactile phase segmentation retrieves relevant prior insertion segments for few-shot ACT learning with a compliant wrist.","institution":"University of Tokyo / OMRON SINIC X","venue":"Preprint","modalities":["Tactile","Force/Torque","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.30889"},"policy":{"family":"ACT","baseModel":"Masked Tactile Trajectory Transformer (MAT3) embeddings and ACT.","modelSize":"Not reported","inputs":"Distributed 3×3 taxel 3 Dforce, wrist F/T and robot proprioception; no RGB required.","outputs":"50-step end-effector pose chunks.","trainingData":"122 prior demos:61 circle+61 square; four target demos pernew insertion geometry; retrieved segments are additional reused prior data.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"50Hz sampling/control.","trainingMethod":"Offline interaction-phase segmentation/retrieval, then ACT training on target+retrieved segments.","evaluation":"UR5e/Hand-E with soft wrist/Papill Array; five peg shapes:circle, square, rectangle, oval, hexagon."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2609.30889","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."}],"caveats":["Few-shot retrieval trains a new policy; this is not frozen-weight in-context execution.","Retrieval baselines instantiate core strategies under one shared ACT protocol rather than reproducing entire source systems."],"verified":"2026-10-17"},{"id":"rivet","name":"RIVET","title":"Representation-Guided Generation and Integration of Executable Programs for Robot Manipulation","date":"2026-09-25","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Generates coordinated perception, rendering, relation-checking and planning programs around a shared object-centric geometric representation.","institution":"Massachusetts Institute of Technology","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.31337"},"policy":{"family":"Robot agent","baseModel":"Claude Opus 5 generates cooperating domain programs; geometric tools maintain per-object 6D poses and a relation graph.","modelSize":"Not reported","inputs":"Task / target configuration, RGB-D observations, object 6D poses and structural relations represented in an object-centric graph.","outputs":"Reusable domain programs and closed-loop robot plans for new initial states / goal configurations.","trainingData":"No end-to-end policy training is required; generated programs are reused across configurations within a task domain.","trainingTime":"Not reported","trainingHardware":"Physical perception / grasp tools run on an RTX 4090 workstation with an AMD Threadripper PRO 5955WX; this is runtime hardware, not a reported model-training cost.","controlRate":"Not reported","trainingMethod":"Frozen VLM program synthesis guided by an explicit shared representation; generated programs cooperate through perception / rendering / relation / planning interfaces.","evaluation":"Cube stacking, tangram arrangement and 3D block assembly in simulation and on a Franka Research 3 with Robotiq 2F-85."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The original paper is public; code, model and dataset release scopes are verified separately where established. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms."},"evidence":[{"url":"https://arxiv.org/abs/2609.31337","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."}],"caveats":["Calibrated ZED M / ZED 2i observations support explicit geometric grounding; the VLM is not a raw-image action head.","Domain-level program reuse is narrower than unrestricted unseen-task generalization."],"verified":"2026-10-17"},{"id":"tactic-encoder-study","name":"TACTIC: Encoder Study","title":"TACTIC: Understanding Tactile Encoders and Conditioning for Contact-rich Robot Manipulation Policies","date":"2026-09-25","year":2026,"collections":["benchmark","policy"],"categories":["tactile-benchmarks","tactile-il"],"resourceType":"benchmark","summary":"Controlled real-world comparison of tactile encoder and policy-conditioning choices across contact-rich tasks, with over 2,000 rollouts.","institution":"University of Technology Nuremberg","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.30969","project":"https://utn-air.github.io/TACTIC","data":"https://huggingface.co/collections/UTN-AIR/tactic"},"benchmark":{"simulator":"Not applicable — real-world controlled study","simulatorVersion":"Not applicable","taskCount":"4 physical contact-rich tasks","tasks":["Board Wiping","Vase Wiping","USB-C Insertion","Screwing"],"taskListScope":"Complete four-task physical suite; Screwing tightens a pre-positioned nut.","capabilities":["Tactile representation selection","Policy conditioning","Precise insertion","Surface interaction"],"metrics":"Success at initialization, grasp, task attempt, task completion and termination; task-specific completion criteria.","robots":"Two Franka FR3 arms in leader/follower data collection; only follower used during inference, with Franka Hand.","sensors":"Two DIGIT tactile sensors; RealSense D405 wrist and D435 side RGB cameras. Depth-capable hardware does not establish depth policy input.","dataCollection":"Bilateral leader–follower teleoperation with two Franka FR3 arms and torque-based haptic feedback.","data":"Approximately 100 demonstrations/task at 30 Hz, with four DIGIT cartridge pairs.","protocol":"2,180 physical rollouts; 20 inference rollouts per ablation condition. Five tactile backbones crossed with five fusion schemes; stage-level success and OOD tests.","baselines":"ACT; ResNet18, SARL, UniT, Sparsh-DINO, T3 encoders; Concatenation, FiLM, Gated Cross-Attention, CLIP-R and CLIP-T fusion."},"release":{"code":"Not released","weights":"Not verified","data":"Available","license":"Not verified","notes":"Four datasets linked in official Hugging Face collection; code explicitly coming soon. Policy release: No verified public download found in the inspected primary sources."},"evidence":[{"url":"https://arxiv.org/abs/2609.30969","label":"Paper","fields":["title","date","benchmark","policy","modalities","environments","embodiments"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://utn-air.github.io/TACTIC/","label":"Official source","fields":["release","benchmark.tasks","benchmark.sensors"],"note":"Official project checked against full manuscript."},{"url":"https://arxiv.org/html/2609.30969","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."},{"url":"https://utn-air.github.io/TACTIC","label":"Official TACTIC project","fields":["institution","links","release"],"note":"Project identity and release links checked independently of manuscript claims."},{"url":"https://arxiv.org/abs/2609.30969","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Distinct from Cornell TACTIC (whole-arm control, arXiv: 2607.09218).","A controlled empirical study; not presented here as a downloadable simulation suite.","No universally best encoder/fusion across tasks was found.","Distinct from the whole-arm contact-learning paper also named TACTIC (2607.09218)."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Not released","weights":"Not verified","data":"Available","license":"Not verified","notes":"Four datasets linked in official Hugging Face collection; code explicitly coming soon."},"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."}},"policy":{"family":"ACT","baseModel":"ACT; ResNet18, SARL, UniT, Sparsh-DINO, T3 tactile backbones","modelSize":"Not reported","inputs":"Side/wrist RGB, paired DIGIT tactile images and robot state","outputs":"ACT robot action chunks","trainingData":"≈100 demonstrations per each of four tasks; four DIGIT cartridge pairs","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Demonstrations30 Hz; policy feedback rate not separately reported","trainingMethod":"Shared training/evaluation pipeline; compares raw feature and contrastive fusion strategies","evaluation":"Board Wiping, Vase Wiping, USB-C Insertion, Screwing; 2,180 rollouts including OOD conditions"},"relatedIds":[],"benchmarkSummary":"Controlled real-world comparison of tactile encoder and policy-conditioning choices across contact-rich tasks, with over 2,000 rollouts.","policySummary":"A controlled real-robot study of five tactile encoders and five fusion schemes under a common ACT pipeline."},{"id":"vistacalign","name":"VisTacAlign","title":"VisTacAlign: Co-Training Dexterous Policies on Tactile Human and Robot Demonstrations using Visual-Tactile Alignment","date":"2026-09-25","year":2026,"collections":["policy"],"categories":["tactile-il","rgbd-3d"],"resourceType":"policy","summary":"Aligns human/robot hand geometry and tactile forces to co-train dexterous flow-matching policies from both embodiments.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Depth","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2609.30959"},"policy":{"family":"Diffusion","baseModel":"Point-cloud flow-matching policy; Fast-FoundationStereo geometry frontend","modelSize":"23.5M total policy","inputs":"Colored metric point clouds, tactile forces and proprioception","outputs":"64-step end-effector/hand-joint chunks; executes first32 at 30 Hz before replanning","trainingData":"Paired human/robot demonstrations; representative settings 50 human + 10/20/30 robot demos","trainingTime":"5–25 GPU-hours per policy","trainingHardware":"One RTX 4090 for training; stereo geometry ≈20 Hz on RTX 4080","controlRate":"Robot taxels/commands30 Hz; stereo20 Hz; chunk inference 269 ms on RTX4080; replans after≈1.1 s","trainingMethod":"Human-to-robot point-cloud/force retargeting and mixed-embodiment co-training","evaluation":"Three real dexterous manipulation tasks with sequential-stage scoring"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."},"evidence":[{"url":"https://arxiv.org/abs/2609.30959","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://arxiv.org/html/2609.30959","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Metric depth is reconstructed from calibrated stereo RGB rather than a standalone depth camera.","Stereo throughput is not the action feedback rate."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."}},"relatedIds":[]},{"id":"adahvla","name":"AdaHVLA","title":"AdaHVLA: Adaptive Harnesses for Long-Horizon Vision-Language-Action Execution","date":"2026-09-24","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Adapts an executable harness around frozen robot / navigation policies through isolated analysis, revision and assessment contexts.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2609.29204","code":"https://github.com/Haaareally/AdaHVLA-Adaptive_Harness_VLA","data":"https://github.com/Haaareally/AdaHVLA-Adaptive_Harness_VLA/blob/main/benchmarks/navila-LH/dataset.json.gz"},"policy":{"family":"Hybrid","baseModel":"Qwen3.7-Flash runtime agent; GPT-5.6-sol Max for manipulation-harness adaptation. Executors: SmolVLA, OpenVLA-OFT or π0.5 for manipulation, NaVILA for navigation.","modelSize":"Not reported","inputs":"Long-horizon task, robot observations, execution history, state hypotheses and an isolated revision graph.","outputs":"Harness-controlled local instructions to frozen executors, plus candidate coordination-code revisions during adaptation.","trainingData":"Adaptation rollouts and execution feedback for code revision; no frozen-executor parameter updates.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen VLA / navigation backbones; isolate analysis, revision and assessment, then freeze selected source code during held-out tests.","evaluation":"Long-horizon manipulation and NaVILA-LH navigation, including real deployments and multiple frozen VLA backbones."},"release":{"code":"Partial — navigation harness and adaptation implementation","weights":"Not applicable","data":"Partial — bundled NaVILA-LH episode definitions","license":"Apache-2.0 code, verified from LICENSE; navigation assets and upstream NaVILA weights retain separate terms.","notes":"Actual harness.py, adaptation.py and evaluation scripts are released for the Go2/NaVILA navigation setting, with benchmarks/navila-LH/dataset.json.gz (50 episode definitions). This does not establish a complete release of the manipulation experiments; the NaVILA checkpoint is an upstream dependency, not an AdaHVLA-trained model."},"evidence":[{"url":"https://arxiv.org/abs/2609.29204","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/Haaareally/AdaHVLA-Adaptive_Harness_VLA","label":"Author-linked repository","fields":["links.code"],"note":"Repository identity; code and checkpoint release status are stated separately."},{"url":"https://github.com/Haaareally/AdaHVLA-Adaptive_Harness_VLA","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://github.com/Haaareally/AdaHVLA-Adaptive_Harness_VLA/blob/main/benchmarks/navila-LH/dataset.json.gz","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["Navigation and manipulation use different executors and adaptation configurations.","Both the initial and adapted harnesses use fixed source code during held-out evaluation."],"verified":"2026-10-19"},{"id":"behavior-predictive-control","name":"Behavior Predictive Control (BPC)","title":"Training-free Behavior Cloning","date":"2026-09-24","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A demonstration-memory controller combines action-aware retrieval, a Hankel continuation prior and closed-form residual correction without end-to-end policy training.","institution":"Stanford University","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.30134"},"policy":{"family":"Retrieval policy","baseModel":"Nonparametric demonstration windows, Hankel trajectory prior and one-step residual correction.","modelSize":"Not reported","inputs":"Recent observation/action history and a demonstration bank; robot settings use RGB/proprioception, with optional other available modalities.","outputs":"Continuous future robot actions blended from supporting demonstration windows with an explicit correction.","trainingData":"Existing demonstration trajectories retained as an editable deployment memory.","trainingTime":"Policy fitting 30–120 seconds on RTX 4090, depending on demonstration count; not end-to-end neural BC training.","trainingHardware":"RTX 4090 24GB for reported fitting timings.","controlRate":"Closed-loop control above 75 Hz on Jetson Orin Nano in the reported deployment setting.","trainingMethod":"Construct retrieval metric and continuation/correction models from demonstrations; adapt by editing the demonstration bank.","evaluation":"Simulated benchmarks and real robot manipulation across embodiments."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/2609.30134","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."}],"caveats":["“Training-free” refers to end-to-end policy optimization; fitting / preprocessing still occurs.","Optional multimodal support does not mean every experiment observes tactile or depth."],"verified":"2026-10-17"},{"id":"coding-agents-tamp","name":"Coding Agents for Generalized TAMP","title":"Coding Agents for Generalized Task and Motion Planning Problems","date":"2026-09-24","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Synthesizes executable policies through interactive coding and simulator testing, then freezes each program before evaluating larger unseen problem instances.","institution":"Princeton University; Fondazione Bruno Kessler; Carnegie Mellon University; University of Cambridge","venue":"Preprint","modalities":["Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.30233","project":"https://agenticgentamp.github.io","code":"https://github.com/tomsilver/robocode"},"policy":{"family":"Robot agent","baseModel":"Claude Code / Opus 5 high; Codex / GPT-5.6 Sol medium or GPT-6 Astra high.","modelSize":"Not reported","inputs":"Task / observation / action API specifications, full object-centric state and a simulator during synthesis; a separately named +source variant also exposes environment implementation.","outputs":"A generalized Python policy shared across instances, issuing simulator / robot actions after synthesis.","trainingData":"980 generated programs are evaluated over 100 held-out instances each, producing 98,000 evaluation episodes; these are not gradient-training trajectories.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen coding-model weights; bounded interactive code generation / testing / revision. Synthesized programs are frozen before held-out-instance evaluation.","evaluation":"Twenty-eight KinDER / PDDLStream simulated environments, including increased object counts and kinematic / dynamic tasks."},"release":{"code":"Available","weights":"Not applicable","data":"Not verified","license":"MIT (code); model, dependency and dataset terms are separate.","notes":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms."},"evidence":[{"url":"https://arxiv.org/abs/2609.30233","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/tomsilver/robocode/main/README.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."},{"url":"https://raw.githubusercontent.com/tomsilver/robocode/main/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Code-license text checked directly; this license is not automatically assigned to model weights or third-party data."}],"caveats":["Uses explicit object-centric state and development simulator access; this is not RGB-only physical robot control.","Source-code access is a separate condition and must not be pooled with the main API-only setting."],"verified":"2026-10-17"},{"id":"da-grd","name":"DA-GRD","title":"DA-GRD: Decision-Aware Grasp-Relevant Disambiguation for tactile recovery under perception-to-execution mismatches","date":"2026-09-24","year":2026,"collections":["policy"],"categories":["tactile-foundation","rgbd-3d"],"resourceType":"policy","summary":"Belief-space tactile probing recovers executable task grasps after object motion makes an initial visual grasp stale.","institution":"A*STAR IAIC / Nanyang Technological University / Tsinghua University","venue":"Preprint","modalities":["RGB","Depth","Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.29065"},"policy":{"family":"Model-based planning","baseModel":"Weighted planar pose belief, decision-aware probe selection and grasp-agreement stopping.","modelSize":"Not reported","inputs":"Initial RGB-D grasp perception; subsequent sparse contact-hit/position observations without further vision.","outputs":"Tactile probe motions and recovered task-conditioned grasps.","trainingData":"No learned policy training corpus required; object-model/grasp hypothesis priors support belief updates.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Analytic belief updates and active probe planning; no imitation/RL training claimed.","evaluation":"MuJoCo UR5/Robotiq 2 F-140 on 10 rigid objects; physical UR5transfer on 6 objects."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2609.29065","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."}],"caveats":["RGB-D cameras are disabled after hidden perturbation; deployment is a staged RGB-D-to-touch pipeline.","Simulated contact sensors report hit positions, not ground-truth object pose/contact normal/hidden mesh state."],"verified":"2026-10-17"},{"id":"grasptwin","name":"GraspTwin","title":"GraspTwin: Zero-Shot Task-Oriented Grasp Optimization via a Digital Twin","date":"2026-09-24","year":2026,"collections":["policy"],"categories":["rgbd-3d","geometry-models"],"resourceType":"model","summary":"A VLM seeds semantic grasp candidates in a reconstructed digital twin, then Thompson-sampling optimization tests physical robustness before robot execution.","institution":"Virginia Tech","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.30543","code":"https://github.com/VT-Collab/GraspTwin","data":"https://huggingface.co/datasets/devans-tech/GraspTwin"},"release":{"code":"Partial — client, perception/IK/simulation/optimization stack","weights":"Not applicable — external pretrained perception/VLM components","data":"Available — digital-twin scenes; not robot training trajectories","license":"MIT original code and published scene dataset card; external SAM3D, simulator, perception models and object assets retain separate terms.","notes":"Actual simulation, perception, IK and optimization modules are public. The hardware RGB-D camera server and robot-PC trajectory-replay server are excluded; only their client message contracts are supplied."},"evidence":[{"url":"https://arxiv.org/html/2609.30543","label":"Original full paper, methods and evaluation","fields":["title","date","institution","summary","modalities","policy","model","benchmark","benchmark.dataCollection","caveats"],"note":"Publication date is arXiv v1; implementation and deployment observations are distinguished from training supervision, generated geometry and base-model identity."},{"url":"https://github.com/VT-Collab/GraspTwin","label":"Official artifact: actual source files, inventory and license","fields":["links","release"],"note":"40 actual files include semantic_grasp camera/robot/IK modules and servers; actual root MIT LICENSE. HF scene JSON/GLB inventory is public/ungated with an MIT card."}],"caveats":["Grasp generation and physics-based optimization are not a recursive self-improvement agent or long-horizon closed-loop VLA.","The standardized simulation benchmark has 30 tasks; the real deployment is a separately selected subset, not all 30 real tasks.","The reported 33 percentage-point physical gain is an absolute difference, not 33% relative improvement.","SAM3D reconstruction is grounded by actual RGB-D; generated geometry is not an additional measured sensor stream."],"verified":"2026-10-21","model":{"role":"Task-oriented grasp proposal/refinement pipeline, with separately scoped physical execution.","family":"Grasp synthesis","baseModel":"SAM3D digital twin, Gemini Robotics ER semantic seed, Thompson-sampling/Bayesian pose optimizer and cuRobo motion planning.","modelSize":"Not reported","inputs":"One RGB-D observation, task instruction, camera calibration and robot/gripper state.","outputs":"Robust 6-DoF grasp candidates and planned execution trajectories.","trainingMethod":"Zero-shot semantic proposals followed by physics simulation/domain-randomized optimization; no method-specific robot-model fine-tuning or in-context demonstrations.","trainingData":"Released digital-twin scene JSON andGLB meshes; these are not policy-training trajectories.","trainingTime":"Not reported","trainingHardware":"Two L40S in reported setup.","inference":"Isaac Sim 4.5.0/Python 3.10 reference implementation; separate hardware servers are required.","evaluation":"30standardized simulated tasks and a separate physical subset on Franka Emika Panda (7 DoF)+UMI parallel gripper."}},{"id":"harnesspai","name":"HarnessPAI","title":"HarnessPAI: An Evolving Harness for Physical AI","date":"2026-09-24","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Evolves reusable executable control harnesses across robot platforms while keeping action models fixed within the described adaptation.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Mobile manipulator","Legged robot","Humanoid"],"links":{"paper":"https://arxiv.org/abs/2609.29166","project":"https://darwin-agent.github.io/HarnessPAI/"},"policy":{"family":"Hybrid","baseModel":"Platform-specific frozen action models, including WorldDreamer for RoboCasa and GraspNet-based household manipulation tools.","modelSize":"Not reported","inputs":"Task, platform observations, execution feedback and reusable harness artifacts; perception and action APIs vary by robot.","outputs":"Executable control programs that coordinate fixed action models, check progress and recover from failures.","trainingData":"Across-rollout evolution and retained code / skills; some reported variants re-adapt on previously failed evaluation seeds.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Program source is fixed within each rollout and evolves across rollouts. The frozen zero-shot test and re-adapted test variants are separate protocols.","evaluation":"RoboCasa, BEHAVIOR-1K and diverse physical platforms, including desktop arms, vacuum / mobile systems and legged robots."},"release":{"code":"Not released","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The official homepage and author repository are accessible, but the repository release-status section marks code coming soon; the tree contains presentation assets and no actual implementation. No method-specific weight or data release was independently verified."},"evidence":[{"url":"https://arxiv.org/abs/2609.29166","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["BEHAVIOR zero-shot and adapted variants have different test access: the latter re-evolves on previously failing test seeds.","A heterogeneous framework does not imply one identical observation or action interface across platforms."],"verified":"2026-10-19"},{"id":"hugo","name":"HuGo","title":"HuGo: LLMs as Whole-Body Policy Code Designers for Humanoid Loco-Manipulation","date":"2026-09-24","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Generates and iteratively refines high-level whole-body policy code over a frozen humanoid controller using numerical rollouts and selected video evidence.","institution":"University of California, Berkeley; ShanghaiTech University; Google DeepMind; Nomagic","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Humanoid"],"links":{"paper":"https://arxiv.org/abs/2609.30594","project":"https://iconlab.negarmehr.com/HuGo/","code":"https://github.com/labicon/HuGo","data":"https://github.com/labicon/HuGo/tree/main/docs/static/hardware_refinement"},"policy":{"family":"Robot agent","baseModel":"GPT-5.4 code designer / evaluator over frozen rl_isaac or SONIC 3-Point VR whole-body policies.","modelSize":"Not reported","inputs":"Task and robot / interface specifications; high-level code receives numerical state. Refinement uses trajectories, internal code state and ten selected video frames per rollout.","outputs":"Closed-loop policy code producing base motion, end-effector targets and torso / pelvis-height commands.","trainingData":"No task-specific demonstrations or example policy programs; executed simulation / physical trajectories provide refinement evidence. Low-level policies are pretrained separately.","trainingTime":"Not reported","trainingHardware":"IsaacSim experiments use a single NVIDIA L40S GPU; hosted LLM inference is separate.","controlRate":"Generated high-level policy code runs at 50 Hz with rl_isaac and 10 Hz with SONIC.","trainingMethod":"Frozen LLM and low-level controller weights; feedback-driven code diffs refine the high-level policy, including directly from physical rollouts.","evaluation":"Five Unitree G1 simulated tasks, including pushing / lifting, and physical Lift Box / Push Button transfer with fifteen trials per selected policy."},"release":{"code":"Partial — selected generated policies and refinement analyses","weights":"Not applicable","data":"Partial — selected public rollout/refinement traces","license":"No root repository LICENSE found in the inspected tree; paper, recordings and dependency rights remain separate.","notes":"The current official repository includes selected generated policy_code.txt files, refinement diffs/prompts/responses, executable analysis_code.py and simulation/physical rollout videos under docs/static/. It does not release a complete code-generation framework or the upstream whole-body policy weights; the selected examples must not be described as an empty placeholder."},"evidence":[{"url":"https://arxiv.org/abs/2609.30594","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/labicon/HuGo/main/README.md","label":"Author repository README","fields":["links.code"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://github.com/labicon/HuGo","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://github.com/labicon/HuGo/tree/main/docs/static/hardware_refinement","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["Physical state observations are constructed with motion capture; this is not camera-only state estimation.","Video is used for policy-code refinement, while the generated online controller consumes the specified numerical state.","Reported results use best-of-N policy selection and task-specific hints for the physical Lift Box setting."],"verified":"2026-10-19"},{"id":"poil","name":"POIL","title":"POIL: Point-based One-Shot Imitation Learning with Stable Dynamical Systems","date":"2026-09-24","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Functional-part point correspondences connect one-shot trajectory transfer to a stable closed-loop dynamical-system controller.","institution":"Seoul National University / Yonsei University","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.30404","project":"https://sangminkim-99.github.io/poil/"},"policy":{"family":"Geometric transfer","baseModel":"Point-based one-shot transfer with stable dynamics. Rex-Omni functional-part grounding, SAM2, multi-view point tracking and Point-set BCSDM.","modelSize":"Not reported","inputs":"Demonstration and current multi-view RGB-D, supplied object/part names, robot state and transferred point trajectory; changed fixture geometry can use user-clicked correspondences.","outputs":"Point velocities projected to a rigid-body twist and mapped to the robot wrist via the grasp transform.","trainingData":"One trajectory demonstration, no task-specific action pretraining; ten functional part points and forty support points used in experiments.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Ground a shared functional part, transfer tracked points, adapt the reference trajectory and execute with stable point-set dynamics.","evaluation":"MuJoCo isolated control tests with GT points/direct object actuation, plus full Franka transfer under pose/grasp/object/goal changes; physical hanging/placement transfers use two ZED 2i cameras."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No author-linked implementation was verified in the inspected primary manuscript/project. This is an evidence gap, not proof that no implementation exists."},"evidence":[{"url":"https://arxiv.org/abs/2609.30404","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://sangminkim-99.github.io/poil/","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."}],"caveats":["Control-isolation simulation uses ground-truth point trajectories and direct object actuation, distinct from full robot/perception evaluation.","Hardware objects are placed into the gripper by an operator; the measured pipeline excludes autonomous grasping. Ten sim-to-real mug trials achieve six successes; other transfers are qualitative.","Convergence guarantee concerns terminal rigid-body point-set dynamics, not arbitrary deformable contact or IK feasibility."],"verified":"2026-10-17"},{"id":"propra","name":"PROPRA","title":"Self-Supervised Anchoring of Fingertip Sensing to Proprioception and Proactive Actions for Robot Imitation Learning","date":"2026-09-24","year":2026,"collections":["policy"],"categories":["tactile-il","tactile-foundation"],"resourceType":"policy","summary":"Proprioceptive/action anchoring learns pressure and proximity representations for pre-contact and post-contact imitation.","institution":"AIST / CNRS-AIST JRL","venue":"Preprint","modalities":["RGB","Tactile","Proximity","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.29822","project":"https://tomohiromotoda.github.io/nia.propra/"},"policy":{"family":"Diffusion","baseModel":"Contrastive sensor encoders anchored to proprioception plus proactive action chunks; diffusion manipulation policy.","modelSize":"Not reported","inputs":"Two RGB views, robot state and 50-sample histories of three force andsix reflective-proximity channels.","outputs":"Robot joint and gripper commands, 16-step action horizon.","trainingData":"Four tasks×50 demonstrations:Pick Cup, Pick Sponge, Move Pen, Open Lid;96/4%episode train/validation split.","trainingTime":"Pretraining~3–4 h; policy training~8 h.","trainingHardware":"One NVIDIA H200141GB GPU and Intel Xeon Platinum 8558 CPU.","controlRate":"Sensors 100 Hz; policy 30 Hz.","trainingMethod":"Info NCE sensor pretraining 1000 epochs, then diffusion policy 1000 epochs.","evaluation":"UR5e/Robotiq 2 F-140 real manipulation; three seeds, 20 trials/seed."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2609.29822","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://tomohiromotoda.github.io/nia.propra/","label":"Official author project","fields":["links","release"],"note":"Live project artifact links inspected on2026-10-17."}],"caveats":["Reflective proximity sensors provide pre-contact signal; they are not depth cameras.","RealSense camera model alone does not establish RGB-D runtime input."],"verified":"2026-10-17"},{"id":"racap","name":"RACaP","title":"RACaP: Agentic Reasoning, Acting, and Coding as Policies for Evolvable Robot Learning","date":"2026-09-24","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Separates curriculum-driven robot-API evolution from a frozen runtime hierarchy that reasons over tasks, transports and local execution feedback.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.29394"},"policy":{"family":"Robot agent","baseModel":"GPT-5.5 for the reported agent, evolution and comparison calls.","modelSize":"Not reported","inputs":"Task, RGB-D observations, robot state, tool reports, a validated Policy API library and retained memory.","outputs":"ReAct-selected tool / transport calls at runtime; source-code and API-library changes occur in the preceding evolution stage.","trainingData":"Capability-curriculum development on LIBERO-90 with a shared reset ceiling; no language-model weight fine-tuning.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen model weights; API-source and memory evolution are followed by runtime selection from fixed code. Runtime cannot edit tool source.","evaluation":"90 LIBERO tasks and held-out generalization comparisons under shared budgets and matched GPT-5.5 endpoints."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2609.29394","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["A fixed runtime library is different from unrestricted online code editing.","Agent-as-Policy comparison adaptations use the shared endpoint / reset budget rather than exactly reproducing their original papers."],"verified":"2026-10-17"},{"id":"rapid","name":"RAPID","title":"RAPID: Robot Agentic Programming from Demonstrations","date":"2026-09-24","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Builds reusable object-centric relational programs from demonstrations, using simulation variants and cross-entropy trajectory optimization to verify generalization.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.30249"},"policy":{"family":"Robot agent","baseModel":"Coding-agent program synthesis with an object-centric relational program representation (OReP) and CEM trajectory optimization.","modelSize":"Not reported","inputs":"Demonstration and deployment RGB-D frames, task language and robot state. SAM 3 masks, SAM 3D meshes and FoundationPose registration reconstruct a MuJoCo scene for program verification and execution.","outputs":"Reusable relational manipulation programs with optimized object-relative motion parameters.","trainingData":"Demonstrations and simulated verification rollouts; no target-task foundation-model weight training.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen coding agent iterates program construction and verification; object-relative representation and CEM optimize execution without neural policy updates.","evaluation":"Manipulation tasks spanning prehensile / non-prehensile behaviors, scene generalization and physical transfer."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2609.30249","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["Some CaP-Agent0 comparisons are given ground-truth state and success feedback and can revise programs in test scenes; these conditions are explicitly different from RAPID.","Trajectory optimization is part of execution and must not be omitted when describing the policy."],"verified":"2026-10-17"},{"id":"robo-harness-k1","name":"Robo-Harness K1","title":"Robo-Harness K1: Harnessing Robot-Use Agents via Perception Augmentation","date":"2026-09-24","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Augments a direct robot-use VLM with calibrated geometry queries, persistent visual anchors and grasp hypotheses while retaining its native tool-call interface.","institution":"Chinese University of Hong Kong; HKUST (Guangzhou); Knowin AI","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.29389","project":"https://robo-harness.github.io/k1/","code":"https://github.com/Robo-Harness/k1"},"policy":{"family":"Robot agent","baseModel":"Gemini 3.7 Flash or GPT-6 Astra frozen agents; a separate Qwen3.5-9B student learns to pilot the same harness.","modelSize":"Student: 9B; frontier VLM sizes are undisclosed.","inputs":"Task, RGB views and robot state; tool queries expose measured geometry / persistent anchors while depth and calibration remain behind the tool boundary.","outputs":"Grounding / geometry / anchor / grasp queries and target-level robot actions executed through embodiment adapters.","trainingData":"Student learning uses 107 successful Gemini teacher episodes shared across policy / RUA baselines.","trainingTime":"Five student epochs, with checkpoints at epochs one, three and five; elapsed duration is not reported.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frontier agents use frozen weights. Student language-side LoRA uses tool-call supervision with the vision encoder frozen; tools stay frozen at evaluation.","evaluation":"LIBERO-PRO Object / Spatial / Goal with position / task perturbations, zero-shot robosuite embodiment transfer and dual-arm RoboTwin transfer."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT (code); model, dependency and dataset terms are separate.","notes":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."},"evidence":[{"url":"https://arxiv.org/abs/2609.29389","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/Robo-Harness/k1/main/README.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."},{"url":"https://raw.githubusercontent.com/Robo-Harness/k1/main/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Code-license text checked directly; this license is not automatically assigned to model weights or third-party data."}],"caveats":["Depth is consumed by the geometric tools, not supplied as an unprocessed VLM image channel.","All reported transfers are simulator experiments; the title does not imply verified physical deployment.","Student new-state development results and held-out-task results are distinct splits."],"verified":"2026-10-17"},{"id":"self-adaptive-vla","name":"Self-Adaptive VLA","title":"Self-Adaptive VLA for Robust Robot Deployment","date":"2026-09-24","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A rollout-context token adapts a VLA to hardware shifts without deployment-time weight changes.","institution":"UMass Amherst / Genesis AI","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.30092","project":"https://icefoxzhx.github.io/self-adaptive-vla"},"policy":{"family":"VLA","baseModel":"Qwen3.5-0.8B VLM, eight-layer 340M DiT action denoiser and DINOv3 context encoder.","modelSize":"0.8B VLM and 340M action-denoiser components; context encoder / total aggregate not reported.","inputs":"Current robot RGB/state/instruction and previous shifted rollout videos, proprioception and actions.","outputs":"Continuous action chunks modulated through context-dependent adaptive layer normalization.","trainingData":"Shift-injected policy rollouts plus expert actions pre-compensated for known training shifts; three camera streams recorded at 30 Hz.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Post-train a context encoder and shift-conditioned policy; ensemble context tokens across failed/successive deployment trials without weight updates.","evaluation":"Four hardware-shift manipulation tasks and iterative real deployment adaptation."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/2609.30092","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://icefoxzhx.github.io/self-adaptive-vla","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."}],"caveats":["Known training shifts supervise adaptation; test deployment uses rollout context, not privileged shift labels.","Camera recording frequency is not policy inference frequency."],"verified":"2026-10-17"},{"id":"streaming-wam","name":"Streaming-WAM — action-conditioned","title":"Streaming-WAM: Action-Conditioned World-Action Model for Asynchronous Robot Manipulation","date":"2026-09-24","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Conditions future visual prediction on actions already committed for asynchronous execution, then generates the remaining continuation.","institution":"Shanghai Jiao Tong University / Li Auto","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.28927","project":"https://sjtu-deng-lab.github.io/Streaming-WAM/","code":"https://github.com/SJTU-DENG-Lab/Streaming-WAM","weights":"https://huggingface.co/SJTU-DENG-Lab/Streaming-WAM"},"policy":{"family":"World-action model","baseModel":"Distilled Fast-WAM-Joint-style video/action experts with a shared-parameter condition-slot encoder","modelSize":"Base-model dependent; complete model count Not reported here","inputs":"RGB, language, state and a committed action prefix from the running plan","outputs":"32-action chunks with an eight-action committed prefix and newly predicted continuation","trainingData":"LIBERO/RoboTwin/RoboCasa demonstrations and physical task demonstrations; no new aggregate scale claimed","trainingTime":"Two-stage visual adaptation/action continuation; wall-clock Not reported","trainingHardware":"Training Not reported; simulation inference one H100 per worker, physical one RTX 5090","controlRate":"Physical execution is 30 Hz. The eight-action committed prefix provides an approximately 267 ms overlap window. Chunk-time measurements exclude initialization; episode time measures complete rollouts.","trainingMethod":"Action-conditioned visual adaptation, continuation learning retaining visual endpoint loss, consistency distillation and cached asynchronous serving","evaluation":"LIBERO, RoboTwin 2.0, RoboCasa, and physical stamp-paper and block manipulation; success, chunk inference time, and episode duration are reported."},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 code, verified from LICENSE. Backbone and checkpoint terms are separate.","notes":"Availability concerns author-linked artifacts; no training or installation reproduced."},"evidence":[{"url":"https://arxiv.org/abs/2609.28927","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/SJTU-DENG-Lab/Streaming-WAM","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://sjtu-deng-lab.github.io/Streaming-WAM/","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."},{"url":"https://github.com/SJTU-DENG-Lab/Streaming-WAM/blob/main/LICENSE","label":"Verified code license","fields":["release.license"]}],"caveats":["Distinct from the later staircase-flow StreamingWAM paper, arXiv: 2610.15585.","Pause-free execution requires inference to meet the overlap deadline; measured no misses apply to reported physical trials."],"verified":"2026-10-17"},{"id":"world-action-agent","name":"World Action Agent (WAA)","title":"World Action Agent: Harnessing VLMs for Robot Manipulation via World Action Rehearsal","date":"2026-09-24","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Lets a VLM rehearse geometric actions, inspect contact views and apply image-space corrections, while reusable skills grow from reviewed experience.","institution":"HKUST (Guangzhou); Chinese University of Hong Kong; Knowin AI","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.29964"},"policy":{"family":"Robot agent","baseModel":"Gemini 3.7 Flash main / grounding agents; GPT-5.5 skill extraction, editing and review. A Qwen3.5-9B student can replace only the main agent.","modelSize":"9B student with 21.6M trainable LoRA parameters; sub-agents remain hosted Gemini models.","inputs":"Task, calibrated global / Contact views, robot control context, rehearsal previews, execution feedback and retrieved skill text.","outputs":"Rehearsed target poses, in-view drag corrections mapped to bounded end-effector displacements and reviewed reusable skill updates.","trainingData":"112 successful LIBERO-Pro teacher episodes produce 1,774 main-agent tool-call training samples.","trainingTime":"Student: ten epochs / 4,440 optimization steps, about 9.5 hours.","trainingHardware":"Four NVIDIA H20 GPUs for student fine-tuning.","controlRate":"Not reported","trainingMethod":"Frozen frontier agents evolve external skills; separate Qwen student LoRA trains on harness traces, freezing its vision encoder and projector.","evaluation":"Six LIBERO-Pro splits and robosuite cube lifting / stacking / restacking with zero-shot or evolved skill libraries."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The original paper is public; code, model and dataset release scopes are verified separately where established. The paper includes trained component / policy variants; a complete method-specific checkpoint release has not been established."},"evidence":[{"url":"https://arxiv.org/abs/2609.29964","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."}],"caveats":["The smaller student replaces the main agent, not every sub-agent or grounding component.","No validation split is held out for student training; the final epoch adapter is evaluated.","Depth / calibration support action rehearsal and image-space correction tools."],"verified":"2026-10-17"},{"id":"rsi-123-rounds","name":"123 Rounds of Robot Skill Discovery","title":"What Stops Recursive Self-Improvement in Robotics? Lessons from 123 Rounds of Agentic Skill Discovery","date":"2026-09-23","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A longitudinal negative-result study audits why an agentic skill-discovery loop can accumulate accepted changes without solving its target manipulation task.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.31760"},"policy":{"family":"Robot agent","baseModel":"GPT-family coding agents in Codex CLI, with the model and reasoning configuration logged per round.","modelSize":"Not reported","inputs":"Camera keyframes, execution traces, declared-step judgements and a persistent skill / model registry.","outputs":"Revised robot skills or requests for new external models, admitted through the system's physical tests.","trainingData":"123 sequential improvement rounds, retained skill changes and model registrations; no new base-language-model training.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen coding models propose code or component changes; physical outcomes and admission procedures update the skill system.","evaluation":"A 123-round investigation of Fridge and Sink manipulation tasks and first-failure locations."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2609.31760","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["The target Fridge task records no successful completion in the reported run; accepted code changes are not evidence of increasing end-task competence.","This paper is included as empirical evidence about self-improvement failure, not as a successful benchmark-solving policy."],"verified":"2026-10-17"},{"id":"arms","name":"ARMS","title":"Watch, Recall, Act: Always-On Robots in Concurrent Embodied Streams","date":"2026-09-23","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Asynchronous context modules add live-stream awareness, embodied state and self-action history to a bimanual VLA.","institution":"Shanghai Jiao Tong University / The Chinese University of Hong Kong","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.28429"},"policy":{"family":"VLA","baseModel":"π0.5 action backbone with three lightweight streaming context providers and agent-causal self-history.","modelSize":"Not reported","inputs":"Live RGB streams, arriving language instructions, robot state and arm-specific action/outcome history.","outputs":"Concurrent dual-arm action chunks conditioned on the latest asynchronous context.","trainingData":"ARMS Dataset from staged real bimanual teleoperation; construction scripts provide module supervision without extra manual annotation.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Fine-tune context providers and the π0.5 action expert for always-on, non-resetting task streams.","evaluation":"Real concurrent bimanual tasks requiring watch, recall and act capabilities and their combined setting."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/2609.28429","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."}],"caveats":["ARMS integrates memory/streaming components; its comparison is within the specified author dataset/protocol."],"verified":"2026-10-17"},{"id":"embodiedswe","name":"EmbodiedSWE","title":"EmbodiedSWE: Coding Agents for Long Horizon Dexterous Robotics","date":"2026-09-23","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Uses coding agents to solve long-horizon simulator-control tasks, then diversifies verified programs into demonstrations for visuomotor policy training.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand","Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.27308","project":"https://embodiedswe.github.io","code":"https://github.com/EmbodiedSWE/EmbodiedSWE","data":"https://huggingface.co/datasets/EmbodiedSWE/robobench-assets"},"policy":{"family":"Hybrid","baseModel":"Coding-agent evaluation includes Fable 5.1, Opus 5 / 4.8, Astra, Sol and Terra; the sim-to-real learned policy is π0.5-DROID.","modelSize":"Not reported","inputs":"Coding agents have simulator APIs and full simulator-state access. The distilled visuomotor policy uses its own visual / robot-state interface at deployment.","outputs":"Executable controller programs; verified diversified rollouts become policy demonstrations, from which a VLA predicts physical actions.","trainingData":"The reported real-transfer example uses 500 successful simulated trajectories generated from an Opus 5 program; no real-robot demonstrations.","trainingTime":"Sim-to-real π0.5 training: 20,000 updates, with the 14,000-step checkpoint deployed. Coding-agent runs use a four-hour wall-clock budget.","trainingHardware":"One NVIDIA B200 for the reported VLA training; one RTX 4090 simulator allocation per coding-agent run.","controlRate":"Not reported","trainingMethod":"Frozen-model program development plus learned-policy fine-tuning on diversified simulated demonstrations. Reward-hack auditing is separate from program success.","evaluation":"28 long-horizon simulated tasks, including dexterous / contact / deformable settings, and a selected real-transfer policy."},"release":{"code":"Available","weights":"Not verified","data":"Partial — task assets and generation tooling","license":"Apache-2.0 code, verified from LICENSE; the task-asset card declares Apache-2.0 with NOTICE/provenance qualifications. External assets and models retain separate terms.","notes":"The current tree contains benchmark/task execution, coding-agent evaluation, RL, trajectory-diversification and VLA conversion/training tools. EmbodiedSWE/robobench-assets contains actual task/robot/room tar bundles, not a released corpus of every generated demonstration or the reported sim-to-real policy checkpoint."},"evidence":[{"url":"https://arxiv.org/abs/2609.27308","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/EmbodiedSWE/EmbodiedSWE","label":"Author-linked repository","fields":["links.code"],"note":"Repository identity; code and checkpoint release status are stated separately."},{"url":"https://github.com/EmbodiedSWE/EmbodiedSWE","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/datasets/EmbodiedSWE/robobench-assets","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["The simulator coding agents receive privileged state; their results are not RGB-only policy results.","Successful program generation and successful deployment of a distilled VLA are separate stages."],"verified":"2026-10-19"},{"id":"membodied","name":"MemBodied","title":"MemBodied: Recurrent Associative Memory for Vision-Language-Action Models","date":"2026-09-23","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A fixed-size associative state and initial-scene anchor retain episode context without growing a VLA’s observation history.","institution":"Nanyang Technological University","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.28256","project":"https://github.com/declare-lab/MemBodied","code":"https://github.com/declare-lab/MemBodied"},"policy":{"family":"VLA","baseModel":"π0 policy with recurrent associative memory and a compact episode-anchor pathway.","modelSize":"40M added memory parameters, reported as 1.26% of π0; not a complete policy parameter count.","inputs":"Current camera observations, robot state, instruction and fixed-size associative/anchor memory.","outputs":"Continuous VLA action chunks.","trainingData":"Five evaluated RMBench tasks plus LIBERO-Long; real data collection uses 50 demonstrations per task before cleaning for three tasks.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"A100-80GB BF16 profiling measures model-side latency only; real data recording at 30 Hz is not claimed as policy rate.","trainingMethod":"Sequence training of recurrent read/write memory and episode anchor jointly with action conditioning.","evaluation":"Five memory-requiring RMBench tasks, LIBERO-Long and real Put Back Block / Rearrange Blocks / Swap Blocks."},"release":{"code":"Available","weights":"Not released","data":"Not released","license":"Apache-2.0 for repository code; external model, dataset and dependency terms remain separate.","notes":"Official recurrent training/evaluation source is public; README explicitly states benchmark assets, datasets and trained checkpoints are not bundled."},"evidence":[{"url":"https://arxiv.org/abs/2609.28256","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://github.com/declare-lab/MemBodied","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/declare-lab/MemBodied","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."},{"url":"https://github.com/declare-lab/MemBodied/blob/HEAD/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Actual license file inspected; code license does not automatically license data or weights."}],"caveats":["Added-memory parameters are not total policy parameters.","Real demonstrations retained after cleaning differ from the original collected count."],"verified":"2026-10-17"},{"id":"occ4m","name":"OCC4M","title":"OCC4M: Object-Centric 4D Memory for Spatiotemporal Reasoning in Long-Horizon Manipulation","date":"2026-09-23","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Tracks object identity and historical locations in a shared frame, then reprojects remembered targets into the current view for a frozen executor.","institution":"Harvard University; ETH Zürich","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.28798","project":"https://occ4m-sup.github.io/occ4m-supplementary/"},"policy":{"family":"Hybrid","baseModel":"SAM3 perception, explicit object-centric 4D memory, Gemini 2.5 Flash planner and a frozen VLA / skill executor.","modelSize":"Not reported","inputs":"RGB observations, camera / base calibration and known support geometry; memory stores tracks, times, movement and containment relations.","outputs":"A selected identity / historical location and skill sequence; live tracking updates the target cue at execution chunk boundaries.","trainingData":"OCC4M itself requires no target-task memory-model training. A separate learned memory-policy baseline uses 80 / 250 / 400 demonstrations per task.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Physical tracking updates at 10.6 Hz; target updates occur at 0.8-second action-chunk boundaries.","trainingMethod":"Frozen planner / executor with explicit online memory updates; no deployment-time weight changes. Separate oracle variants diagnose memory, planning and action errors.","evaluation":"Seven simulated memory conditions, viewpoint-transfer controls and twenty fixed-camera Franka compositional-memory episodes."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The original paper is public; code, model and dataset release scopes are verified separately where established. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms."},"evidence":[{"url":"https://arxiv.org/abs/2609.28798","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."}],"caveats":["Current geometry uses known support surfaces and calibration; measured depth is proposed as an alternative, not the reported input.","The raw-history baseline uses Gemini 3.7 Flash, whereas OCC4M uses Gemini 2.5 Flash; backbone and representation are not matched.","Physical evaluation uses a fixed camera and does not validate the simulation viewpoint-invariance claim."],"verified":"2026-10-17"},{"id":"me-u0","name":"MachEmbodied-U0 (ME-U0)","title":"MachEmbodied-U0: Unified Understanding and Generation Model for Embodied Intelligence","date":"2026-09-22","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Unifies subtask/affordance understanding with generation of future visual modalities and executable continuous actions.","institution":"Li Auto","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.25627","project":"https://machembodied.com/ME-U/ME-U0.html","code":"https://github.com/MachEmbodied/ME-U0","weights":"https://huggingface.co/LiAuto-Foundation-Model/ME-U0-RoboDojo"},"policy":{"family":"World-action model","baseModel":"Lance-initialized understanding and generation experts coupled through MoT attention.","modelSize":"Not reported. Qwen3.6-35B-A3B is a data-annotation model, not the policy parameter count.","inputs":"RGB, task instruction and robot state; current observations without explicit persistent visual history.","outputs":"Future RGB/depth/normals/flow and continuous actions. LIBERO: 24 end-effector actions; RoboDojo: 48 delta joint actions.","trainingData":"Approximately 4,200 curated hours and 120M pretraining samples. Raw pools of 5,700 robot hours and 3,920 egocentric hours are not all consumed.","trainingTime":"Wall-clock duration not reported.","trainingHardware":"LIBERO/RoboDojo posttraining: 368 PPU810E accelerators, batch two per device.","controlRate":"Not reported","trainingMethod":"Autoregressive subtask/affordance supervision, joint visual-action flow matching, multi-rate temporal positions and masked canonical actions.","evaluation":"RoboDojo, LIBERO, LIBERO-Plus and physical manipulation; zero-shot understanding and visual-dynamics generation."},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 code; weights/data/backbone terms separate","notes":"Availability concerns author-linked artifacts; no training or installation reproduced."},"evidence":[{"url":"https://arxiv.org/abs/2609.25627","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/MachEmbodied/ME-U0","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://machembodied.com/ME-U/ME-U0.html","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."}],"caveats":["Predicted depth/normals are outputs or supervision, not measured deployment inputs.","The paper identifies memory-task limitations associated with missing explicit observation history."],"verified":"2026-10-17"},{"id":"patchwam","name":"PatchWAM / PatchWAM-Lite","title":"An Action Is Worth One Patch: Unified World-Action Modeling with PatchWAM","date":"2026-09-22","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Maps action vectors into fixed visual latent patches so one generative transformer predicts future images and executable action chunks.","institution":"Westlake University / Awomo","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.25961","code":"https://github.com/TianhengW/PatchWAM","weights":"https://huggingface.co/QZWang/patchwam-robotwin-c2r-step140000"},"policy":{"family":"World-action model","baseModel":"FLUX.2 [klein] 4B Base and fixed Action-as-Patch codec. Qwen3-4B text encoder; RoboDojo uses Qwen3-VL-4B with LoRA.","modelSize":"4B shared DiT; RoboDojo adds 132.1M trainable VL LoRA parameters. Text encoder and VAE counts are separate.","inputs":"RGB, language and state. RoboDojo additionally uses up to 20 head-camera frames at one-second intervals.","outputs":"16 actions per chunk: 7D on LIBERO or 14D on dual-arm tasks; joint future-frame prediction.","trainingData":"Distinct full-data, C2R, matched-window and augmented protocols. Full RoboTwin has approximately 5.37M training windows; matched control retains approximately 268k.","trainingTime":"Five epochs for full RoboTwin; ten for matched/LIBERO runs. Released C2R Lite snapshot at step 140k. Wall-clock duration not reported.","trainingHardware":"32 GPUs for full RoboTwin/RoboDojo, eight for matched control and 16 for C2R/LIBERO. GPU type not reported here.","controlRate":"Measured 20-step sampling takes 0.446 s per 16-action call. Benchmark ten-step replanning was not timed in the paper.","trainingMethod":"Joint image/action flow matching through a fixed codec; auxiliary subtask supervision in RoboDojo.","evaluation":"RoboTwin matched architecture, C2R and full-data protocols; LIBERO, LIBERO-Plus and RoboDojo."},"release":{"code":"Available","weights":"Partial","data":"Not verified","license":"Apache-2.0 new code, verified from LICENSE. Adapted ImageWAM components retain MIT notices; model and data terms are separate.","notes":"The author repository provides training, data processing, and closed-loop RoboCasa/RoboTwin/LIBERO entry points. It reports passed CPU integration checks while full-size GPU training and simulator reproduction still require verification. A separate contributor Lite adapter releases a C2R step-140k checkpoint; artifacts for every paper protocol are not verified."},"evidence":[{"url":"https://arxiv.org/abs/2609.25961","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/TianhengW/PatchWAM","label":"Author implementation","fields":["links.code","release"],"note":"Public source includes training and evaluation entry points. README distinguishes CPU integration verification from pending full-size GPU/simulator verification."},{"url":"https://github.com/TianhengW/PatchWAM/blob/main/LICENSE","label":"Code license","fields":["release.license"]}],"caveats":["The adapter still describes a forthcoming report, but arXiv 2609.25961 is public.","Augmented headline protocols are separate from the matched architecture comparison.","Fewer trainable parameters did not yield faster inference in the measured comparison."],"verified":"2026-10-17"},{"id":"me-brain-1","name":"ME-Brain-1.0","title":"ME-Brain-1.0: Memory, Cognition and Action for Evolving Embodied Intelligence","date":"2026-09-21","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Combines hierarchical multimodal memory, a skill-using Cognitive Core and a world-aware action model to retain experience across deployment.","institution":"Mach Embodied","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.24271","code":"https://github.com/MachEmbodied/Focus-VLWA","project":"https://github.com/MachEmbodied/ME-Brain-1.0","weights":"https://huggingface.co/zhengjingxie/focus-vlwa"},"policy":{"family":"Hybrid","baseModel":"Cognitive Core and Evolvable Memory; Focus-VLWA action model initialized from π0.5, with a visual world-model expert and EventCell prediction.","modelSize":"Not reported","inputs":"Current multimodal observations and robot state, task goals, selectively retrieved event / task / skill memories and execution outcomes.","outputs":"Subgoals and skill calls from Cognitive Core; Focus-VLWA produces action chunks jointly with local future-world predictions.","trainingData":"Focus-VLWA has learned policy / world-model training; deployment experience is consolidated into working, task and procedural memory.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"A learned action model is combined with deployment-time memory evolution. Experience-driven system evolution is not equivalent to a claim that all component weights remain untrained.","evaluation":"Embodied manipulation benchmarks and real-world experience-transfer / adaptation studies, with separate Cognitive Core and action-model ablations."},"release":{"code":"Partial — Focus-VLWA action component","weights":"Partial — Focus-VLWA action checkpoint","data":"Not verified","license":"Apache-2.0 for Focus-VLWA and umbrella repository code, verified from their LICENSE files; model checkpoint license not stated in the inspected HF card. External backbones retain separate terms.","notes":"The ME-Brain umbrella is primarily a component index. Its linked Focus-VLWA repository actually contains post-training, data conversion and inference implementation; the official checkpoint inventory includes model.safetensors, model_config.json and ARX-X5 normalization statistics. This is an action-component release, not a verified complete Cognitive Core/Evolvable Memory stack or full-system checkpoint."},"evidence":[{"url":"https://arxiv.org/abs/2609.24271","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/MachEmbodied/ME-Brain-1.0","label":"Author-linked repository","fields":["links.code"],"note":"Repository identity; code and checkpoint release status are stated separately."},{"url":"https://github.com/MachEmbodied/Focus-VLWA","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/zhengjingxie/focus-vlwa","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["π0.5 initialization describes the action component; the memory–cognition–action system is broader than a single π0.5 checkpoint."],"verified":"2026-10-19"},{"id":"roboprobe","name":"RoboProbe / GPT-6 Astra Direct-EEF","title":"An Unexpected Robot Policy: Early Evaluations of GPT-6 Astra on RoboDojo and Beyond","date":"2026-09-21","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A frozen frontier model selects end-effector targets from RGB and robot state. A non-learned action-conversion module validates requests, plans joint paths and executes the motion.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Bimanual","Parallel-jaw gripper","Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2609.24170","project":"https://github.com/RoboProbe/RoboProbe","code":"https://github.com/RoboProbe/RoboProbe"},"policy":{"family":"Robot agent","baseModel":"GPT-6 Astra; GPT-5.5 and DeepSeek-Flash are separately evaluated under the same model-facing interface.","modelSize":"Not reported","inputs":"Three RGB camera views, read-only robot proprioception, task instructions and wiki recipes, recent images and retained textual interaction history; no metric-depth image.","outputs":"move_eef world-frame grasp-point targets or give_up; an analytic adapter converts grasp targets to flange poses, joint paths, timing and gripper phases.","trainingData":"No robot-specific fine-tuning in the direct-policy study. Demonstration-context variants are separate ablations.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen model; repeated observation–tool-action feedback. The low-level adapter is analytic rather than a learned motor policy.","evaluation":"2,100 RoboDojo episodes across 42 tasks; complementary real-robot manipulation and dexterous-piano case studies."},"release":{"code":"Available","weights":"Not applicable","data":"Partial","license":"Apache-2.0 for the repository; external assets and APIs retain separate terms.","notes":"Public Direct-EEF / RoboDojo evaluation code, protocol and run artifacts; simulator setup and hosted-model access are separate."},"evidence":[{"url":"https://arxiv.org/abs/2609.24170","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/RoboProbe/RoboProbe","label":"Author-linked repository","fields":["links.code"],"note":"Repository identity; code and checkpoint release status are stated separately."},{"url":"https://github.com/RoboProbe/RoboProbe","label":"Official repository README and LICENSE","fields":["release","caveats"],"note":"Implementation scope and repository license are verified separately from model access and full benchmark reproducibility."}],"caveats":["GPT-5.5 and DeepSeek-Flash leaderboard rows are model variants of this robot interface, not separate robot-policy training papers.","The piano case study uses a different finger / wrist interface and does not establish a large-scale dexterous benchmark result.","One-shot demonstrations reduced aggregate performance in this particular controller protocol; this is not a universal claim about in-context learning."],"verified":"2026-10-17"},{"id":"tacit","name":"TACIT","title":"TACIT: Tactile Contact Supervision for Spatial Attention in Dexterous Manipulation","date":"2026-09-21","year":2026,"collections":["policy"],"categories":["rgbd-3d","tactile-il"],"resourceType":"policy","summary":"Uses measured demonstration contacts to supervise spatial attention over earlier camera point clouds; tactile observations also remain policy inputs at deployment.","institution":"Tohoku University","venue":"Preprint","modalities":["Depth","Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.24507"},"policy":{"family":"Diffusion","baseModel":"DP3/3D visuotactile diffusion backbone with point-wise attention MLP and tactile-derived Gaussian spatial targets","modelSize":"Not reported","inputs":"Observed depth-derived XYZ cloud, tactile force/contact points and robot state; six-channel points encode XYZ, force and modality masks","outputs":"20 predicted actions, execute ten; 23D single-arm or 46D handover state/action interfaces","trainingData":"Ten physical demonstrations per ball/peg task; simulation 50 fruit/cube and 18 peg demos across placement configurations","trainingTime":"3,000 epochs of 500 minibatches, batch 16; wall-clock Not reported","trainingHardware":"Not reported","controlRate":"Physical control 20 Hz; Isaac Lab simulation 30 Hz; ten DDIM inference steps","trainingMethod":"Diffusion imitation plus contact-derived attention supervision; contact targets are training-only, tactile observations remain online inputs","evaluation":"Physical FR3+LEAP+three 9DTact fingertips/D455 ball placement and peg insertion; simulated fruit, peg, cube and separate bimanual handover extension"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified; pretrained backbones and data retain separate terms","notes":"Availability concerns author-linked artifacts; no training or installation reproduced."},"evidence":[{"url":"https://arxiv.org/abs/2609.24507","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."}],"caveats":["Contact supervision targets earlier visual frames; it is not privileged online contact-location input.","Reported improvement primarily concerns approach localization; remaining failures occur after arrival.","Held-out placement diagnostics are inside the demonstrated convex hull, not workspace extrapolation."],"verified":"2026-10-17"},{"id":"tactile-jepa","name":"Tactile-JEPA","title":"Tactile-JEPA: Topology-Aware Self-Supervised Representation Learning for Distributed Tactile Sensors","date":"2026-09-21","year":2026,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Predicts masked electronic-skin embeddings using local/global masks sampled on the sensor graph, with topology-aware representations across hand skins.","institution":"Sber AI; Skoltech; HSE University","venue":"Preprint","modalities":["Tactile"],"environments":["Real world"],"embodiments":["Allegro hand","Bimanual Inspire hands"],"links":{"paper":"https://arxiv.org/abs/2609.24385","code":"https://github.com/E-Kovtun/tactile","weights":"https://huggingface.co/Tactile-JEPA"},"model":{"role":"Reusable representation or perception model; downstream controllers are separate systems","family":"Tactile encoder","baseModel":"ViT-Tiny-style encoder (12 blocks, width 192, 3 heads) and 4-block predictor; taxel graph controls masking","modelSize":"Not reported","inputs":"Short multichannel taxel time windows, taxel connectivity/coordinates where available","outputs":"Per-taxel latent tactile features for classification, force/pose estimation or a separate visuotactile action decoder","trainingMethod":"EMA teacher/student joint-embedding prediction; local and global graph masks; frozen-feature downstream probes","trainingData":"Sparsh-skin/Xela/Allegro (1.7M frames,4.6h), tactile socks (298K,5.9h), and DECO-50 Inspire-hand data; policy experiment is Task 4 Assembly subset","trainingTime":"Pretraining: Xela 6.1h; socks action 0.7h / pose 2.0h; DECO 5.5h; task-specific reported timings","trainingHardware":"4 NVIDIA A100 80GB for pretraining; downstream heads on 1 GPU","inference":"Not reported","evaluation":"Force, object identity and in-hand pose probes; human socks activity/pose and DECO assembly action prediction are distinct evaluation settings."},"release":{"code":"Available","weights":"Available — official download link","data":"Not verified","license":"CC BY-NC 4.0 repository LICENSE.md and official model cards; third-party datasets keep original licenses","notes":"Code, pretrained encoders and downstream task data are separate artifacts; no policy checkpoint is implied by an encoder release."},"evidence":[{"url":"https://arxiv.org/abs/2609.24385","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Method, experiments and implementation sections; specifications retain component and evaluation scope."},{"url":"https://github.com/E-Kovtun/tactile","label":"Official implementation and current release instructions","fields":["release","links"],"note":"README and repository files inspected on 2026-10-17."},{"url":"https://github.com/E-Kovtun/tactile/blob/main/LICENSE.md","label":"Repository license file","fields":["release.license"],"note":"Applies to repository scope; model/data/base-model terms can differ."},{"url":"https://huggingface.co/Tactile-JEPA","label":"Official organization with four released encoder checkpoints","fields":["release","model"],"note":"Official artifact metadata inspected separately from code."}],"caveats":["Repository README still lists pretrained-model links as TBD, but the official Tactile-JEPA Hugging Face organization exposes four model releases.","Socks experiments concern human feet; they are not additional robot manipulation tasks.","DECO policy experiment learns an action decoder on top of features; the representation model is not counted as a standalone executable policy."],"relatedIds":["sparsh-skin","htt"],"verified":"2026-10-17"},{"id":"touch2robot","name":"Touch2Robot","title":"Touch2Robot: Robot Touch in the Human Demonstration Loop","date":"2026-09-21","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Human tactile preferences shape simulated robot contacts and live demonstration feedback, improving downstream robot imitation.","institution":"BIGAI / ShanghaiTech University / Shanghai Jiao Tong University / Bei hang University","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2609.24660","project":"https://Touch2Robot.github.io/"},"policy":{"family":"Hybrid","baseModel":"Object-specific RL teachers, unified retargeter and downstream Le Robot Diffusion Policy.","modelSize":"Not reported","inputs":"Downstream policy:two RGB views, robot joint states and tactile observations.","outputs":"Robot joint-target chunks; live collection retargeter also predicts robot contacts.","trainingData":"Four tasks;30 teacher trajectories/task augmented to 400 training rollouts; separate equal-size collected datasets for downstream policies.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Live VR feedback 30 Hz/122 ms; downstream executes 16 actions before replanning.","trainingMethod":"Tactile-preference RL and guided-to-free dynamics curriculum, retargeter distillation, then robot demonstration diffusion imitation.","evaluation":"LEAP Hand pick-and-place, rotation, board wiping, drawer opening; real replay and autonomous policy completion."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2609.24660","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://Touch2Robot.github.io/","label":"Official author project","fields":["links","release"],"note":"Live project artifact links inspected on2026-10-17."}],"caveats":["Object-specific teachers use object geometry/references; the unified retargeter is a different deployment stage.","Predicted contact feedback during human collection is not direct real-robot touch measurement."],"verified":"2026-10-17"},{"id":"zeva-ego","name":"Zeva-Ego","title":"Zeva-Ego: Egocentric Mid-Training with In-Context Causal Learning for Robot Manipulation","date":"2026-09-21","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Egocentric-video mid-training and a causal interaction-context encoder improve VLA manipulation and fixed-weight deployment adaptation.","institution":"Institute for AI Industry Research, Tsinghua University","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.24411","project":"https://air-embodied-brain.github.io/Zeva-Ego","code":"https://github.com/air-embodied-brain/Zeva","weights":"https://huggingface.co/collections/BEN12324/zeva-ego-models-and-checkpoints"},"policy":{"family":"VLA","baseModel":"π0.5 / PaliGemma action policy with Action-Centric Encoder (ACE) and In-Context Causal Learning (ICCL).","modelSize":"Not reported","inputs":"Robot RGB views, proprioception, instruction and previous action-effect interaction context; human videos supply mid-training supervision.","outputs":"16-D dual-end-effector action chunks; mid-training horizon 50 and later robot policy horizon 30 in the reported configurations.","trainingData":"Egocentric scaling up to 10k video-hours, with reported task-stratified human datasets and a fixed 1:1 robot-data mix during specified training stages.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"ACE-based action-centered video mid-training, then policy learning with context fed directly to the action expert; deployment context accumulates without weight updates.","evaluation":"RoboTwin data scaling and real manipulation with multi-attempt causal-context adaptation."},"release":{"code":"Available","weights":"Available","data":"Partial","license":"OpenMDW-1.1 for the linked Zeva code/materials (actual repository LICENSE); the released Zeva-Ego π0.5 JAX checkpoint retains actual Gemma Terms of Use in LICENSE_GEMMA.txt. Upstream human-video data have separate terms.","notes":"Official Zeva repository and Zeva-Ego model/checkpoint collection; upstream human-video datasets have separate terms. The pi05-jax-55k checkpoint inventory contains real sharded parameter arrays and an actual Gemma license; its terms are not inferred from the host collection."},"evidence":[{"url":"https://arxiv.org/abs/2609.24411","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://air-embodied-brain.github.io/Zeva-Ego","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/air-embodied-brain/Zeva","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."},{"url":"https://huggingface.co/BEN12324/zeva-ego-pi05-jax-55k/blob/main/LICENSE_GEMMA.txt","label":"Official current source / artifact inventory — 21 October 2026","fields":["release","links","policy"],"note":"Actual checkpoint-specific Gemma Terms file and sharded JAX parameter inventory reviewed; repository root is separately OpenMDW 1.1."}],"caveats":["Ten-thousand-hour human-video scale is not robot demonstration time.","ICCL modifies deployment context; mid-training and policy training still update model weights."],"verified":"2026-10-21"},{"id":"hapticwam","name":"HapticWAM","title":"HapticWAM: Distilling Imagined Touch into a World-Action Model without Inference-Time Tactile Sensing","date":"2026-09-20","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"A tactile-teacher world-action model distills generated contact futures into a student without fingertip observations.","institution":"Skolkovo Institute of Science and Technology","venue":"Preprint","modalities":["RGB","Force/Torque","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.23888","project":"https://advanced-robotic-manipulation.github.io/websites/hapticwam/","code":"https://github.com/Advanced-Robotic-Manipulation/HapticWAM","data":"https://huggingface.co/datasets/armteam/hapticwam-teleop-dataset","weights":"https://huggingface.co/armteam/hapticwam-student"},"policy":{"family":"World-action model","baseModel":"Frozen Cosmos-Predict 2.5-2 B backbone with rank 16 LoRA, anticipated-contact conditioning and haptic imagination distillation.","modelSize":"2 B frozen backbone;27.9 M trainable teacher and 26.4 M student parameters.","inputs":"Deployed student: current RGB, language, proprioception and arm-side wrench history estimated from motor currents; no fingertip tactile frames.","outputs":"16-step end-effector delta-pose/absolute-gripper chunks plus generated video and typed contact futures.","trainingData":"855 collected demonstration episodes=285/task, split 761 training 94 validation; student 860 episodes=761 train+99 earlier teacher/student rollouts; baseline export 876 excludes deliberate failures, so training corpora are not identical.","trainingTime":"Teacher 20,000 +student 1,000 updates; wall-clock Not reported.","trainingHardware":"Teacher one 24 GB GPU, model Not reported; student one NVIDIA H100.","controlRate":"Action targets 10 Hz, 16-step/1.6 s horizon; collection robot 125 Hz, touch 8 Hz, RGB15 Hz.","trainingMethod":"Teacher 20 k updates; student 1 k offline distillation updates with generated contact/event targets; failed examples do not supervise ground-truth action reproduction.","evaluation":"UR3/Robotiq 2 F-85: waffles, carton, egg pick-and-place; contact-representation clamping intervention and matched-start robot evaluation."},"release":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0","notes":"Author-linked repository and actual LICENSE inspected; code, checkpoints anddatasets have independent availability."},"evidence":[{"url":"https://arxiv.org/abs/2609.23888","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://advanced-robotic-manipulation.github.io/websites/hapticwam/","label":"Official author project","fields":["links","release"],"note":"Live artifact status reviewed separately from paper claims."},{"url":"https://github.com/Advanced-Robotic-Manipulation/HapticWAM","label":"Official released implementation and LICENSE","fields":["links","release"],"note":"Repository metadata,README and actualLICENSE inspected; availability scoped to the listed component."},{"url":"https://huggingface.co/armteam/hapticwam-student","label":"Author-linked released model","fields":["links","release"],"note":"Public repository file inventory inspected; component/data scope follows model/dataset card."},{"url":"https://huggingface.co/datasets/armteam/hapticwam-teleop-dataset","label":"Author-linked released dataset","fields":["links","release"],"note":"Public repository file inventory inspected; component/data scope follows model/dataset card."}],"caveats":["No fingertip sensing does not mean vision-only: motor-current-derived arm wrench remains a runtime input.","Wrench is estimated, not measured by a dedicated wrist F/T sensor.","Contact-clamping evaluates dependence on generated contact content, not an isolated demonstration that tactile distillation outperforms action-only distillation."],"verified":"2026-10-17"},{"id":"ropeformer","name":"RopeFormer","title":"RopeFormer: Cross-Trial Adaptation from Interaction History for Dynamic Rope Manipulation","date":"2026-09-20","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A Transformer-XL policy retains cross-trial action-response context to manipulate previously unseen rope dynamics.","institution":"UC Berkeley","venue":"Preprint","modalities":["RGB","Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.23432","project":"https://ropeformer.github.io/"},"policy":{"family":"RL","baseModel":"Transformer-XL history-conditioned controller trained across randomized rope/actuator dynamics.","modelSize":"Not reported","inputs":"Robot state, previous actions, task/target information and observed rope marker positions; no explicit rope physical parameters at deployment.","outputs":"Humanoid arm controls for sustained rope rotation and transient whipping.","trainingData":"Simulated repeated interaction trials with shared randomized dynamics; real transfer uses unseen physical ropes.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Rotation evaluation uses 30 Hz control. Real marker positions are triangulated from two calibrated ZED 2i RGB cameras running 720p/60 Hz.","trainingMethod":"Train across multiple attempts with physical-state reset but retained policy context; fixed weights during real cross-trial adaptation.","evaluation":"Single-arm rotation, bimanual rotation and whipping in simulation; Unitree H1-2 Rope Swing / Twirl / Whip real transfer."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/2609.23432","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://ropeformer.github.io/","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."}],"caveats":["3D rope markers are obtained by calibrated triangulation, not a dense skin tactile stream.","Reported matched evaluations use one training seed per configuration."],"verified":"2026-10-17"},{"id":"robodawn","name":"Robo Dawn","title":"Transferring the Intelligence of VLMs to Robotic Control","date":"2026-09-19","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A frozen VLM uses a compact semantic motion vocabulary and in-context examples, grounded at the gripper interaction point.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.22966"},"policy":{"family":"Robot agent","baseModel":"GPT-6 Astra in the main result; Gemini 3.8 Flash, GPT-5.6 Sol / Luna and Seed 2.1 Pro variants are separately evaluated.","modelSize":"Not reported","inputs":"Task, visual feedback, gripper-interaction-point state and zero or one clean-scene demonstration for the main comparison.","outputs":"Parameterized move, rotate, point, gripper, home, wait and done commands, interpreted by a non-learned backend.","trainingData":"No target-task weight training. RoboTwin C2R uses clean-scene demonstrations only as in-context examples; learned-policy comparisons use their own post-training.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen VLM; semantically grounded motion commands and in-context demonstration conditioning.","evaluation":"All 50 RoboTwin 2.0 C2R tasks, RoboDojo and three real Franka tasks; demonstration-count, model and harness ablations."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2609.22966","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["Translation / rotation commands are clipped to 20 cm / 90 degrees per call; these are action limits, not positioning accuracy.","The C2R demonstration access and trained-policy baseline protocols differ."],"verified":"2026-10-17"},{"id":"me-dex-1","name":"ME-Dex 1.0","title":"ME-Dex 1.0: Bringing Heterogeneous Tactile Sensing into World Action Modeling","date":"2026-09-18","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Jointly models future video, heterogeneous tactile states and actions with separate flow-matching experts and a canonical hand representation.","institution":"Li Auto","venue":"Preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.21449","project":"https://machembodied.com/ME-Dex/ME-Dex1.0.html","code":"https://github.com/MachEmbodied/ME-Dex-1.0","data":"https://huggingface.co/datasets/liuxuetao/MachEmbodied-Dex1.0-RoboTwin-Clean50-Tactile","weights":"https://huggingface.co/liuxuetao/ME-Dex-1.0-RoboTwin-Clean2Random-Leaderboard"},"policy":{"family":"World-action model","baseModel":"Wan2.2-5B video expert; video/action initialization from Motus","modelSize":"5B video backbone component; action/tactile experts have 30 layers at widths 1024/512; total not reported","inputs":"Paper interface: RGB, language, robot state and heterogeneous tactile measurements. Current released RoboTwin checkpoint instead receives RGB/state/language with zero observed tactile force; its future tactile states are predictions, and Clean50 tactile replay is training supervision.","outputs":"Future visual/tactile observations and robot action chunks","trainingData":"Agentic Tactile Data Engine augments RoboTwin and DexJoCo through simulation replay; total not reported; released Clean50 simulation tactile-replay subset contains 2,500 HDF5 episodes across 50 task directories, augmenting existing demonstrations rather than adding a new demonstration corpus.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Canonical Hand Model + Unified Tactile Autoencoder; H-Bridge intermediate shared attention","evaluation":"RoboTwin, DexJoCo, ManiFeel and real gripper/dexterous-hand experiments"},"release":{"code":"Available","weights":"Available","data":"Partial","license":"Official source Apache-2.0, verified from LICENSE. Checkpoint card declares Apache-2.0; dataset terms are separate and not verified.","notes":"Official runtime, frozen tactile encoders, reference AE/VAE Clean50 training recipes and tactile replay code are public. Two RoboTwin checkpoint variants and a public Clean50 tactile-replay dataset are retrievable. This does not establish release of all paper-scale data or all physical experiments."},"evidence":[{"url":"https://arxiv.org/abs/2609.21449","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://arxiv.org/html/2609.21449","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."},{"url":"https://github.com/MachEmbodied/ME-Dex-1.0/tree/a2db63b979e748bcea692966b1c57ab2984155a9","label":"Official current implementation, source LICENSE and release instructions","fields":["links","release","policy.inputs","policy.evaluation"],"note":"Author library supplies runtime/training/replay and links the actual HF artifacts. Current RoboTwin deployment zeroes observed tactile force; predicted future tactile and training replay labels are not measured runtime tactile input."},{"url":"https://huggingface.co/datasets/liuxuetao/MachEmbodied-Dex1.0-RoboTwin-Clean50-Tactile/tree/4680b177c93be8e07fba8bd4c4c65b97ca94d1df","label":"Actual released Clean50 tactile replay dataset inventory","fields":["release.data","policy.trainingData"],"note":"Public ungated repository has task-specific HDF5 episodes; this is simulation replay augmentation, not real sensor data. Dataset license is not supplied in the inspected metadata."},{"url":"https://github.com/XPolicyLab/XPolicyLab/tree/94dbf9505f2f87cf59c3ee8ba5912cd236781771/policy/ME_Dex_1_0","label":"Current XPolicyLab alias and interface","fields":["policy.inputs","links.code"],"note":"ME_Dex_1_0 explicitly cites the same ME-Dex manuscript and official repository; it is an adapter alias, not a new policy."}],"caveats":["Simulation tactile augmentation is not measured real-sensor supervision.","The released RoboTwin interface has no measured tactile observations: it zeros observed tactile frames and preserves the sensor-support mask. Tactile replay supervision and predicted tactile states must be separated from runtime observations."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Partial","license":"Official source Apache-2.0, verified from LICENSE. Checkpoint card declares Apache-2.0; dataset terms are separate and not verified.","notes":"Official runtime, frozen tactile encoders, reference AE/VAE Clean50 training recipes and tactile replay code are public. Two RoboTwin checkpoint variants and a public Clean50 tactile-replay dataset are retrievable. This does not establish release of all paper-scale data or all physical experiments."}},"relatedIds":[]},{"id":"sandwich-residuals","name":"Sandwich-Residuals","title":"Sandwich-Residuals: Parameter-Efficient Test-time Adaptation of World Models","date":"2026-09-18","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Small residual modules around frozen world-model dynamics adapt online from transition prediction errors.","institution":"Montanuniversität Leoben","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.21740"},"policy":{"family":"Model-based planning","baseModel":"Parameter-efficient online world-model adaptation. AdaJEPA and DINO-WM with input/action/output residual adapters; CEM planning.","modelSize":"OGBench-Cube adapts 99,310 parameters versus 3.23M in compared AdaJEPA variants; backbone size varies.","inputs":"Current and goal RGB plus recent transitions; OGBench-Cube explicitly has no proprioceptive input. Other benchmark observations differ.","outputs":"CEM-planned actions; Cube action is 5D end-effector displacement, yaw and gripper.","trainingData":"DINO-WM Cube model trained on 2,000 LeWorldModel episodes; separate pretrained AdaJEPA task models.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Freeze pretrained encoders/dynamics and fit residual parameters to online self-supervised transition error.","evaluation":"21 AdaJEPA primary and seven compound-shift conditions; additional four-condition OGBench UR5e cube manipulation test."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No author-linked implementation was verified in the inspected primary manuscript/project. This is an evidence gap, not proof that no implementation exists."},"evidence":[{"url":"https://arxiv.org/abs/2609.21740","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."}],"caveats":["Includes maze navigation as well as PushT/PushObj and 3D manipulation; the reduced Cube setting shows modest gains.","The algorithm updates small parameters online. Fewer trainable parameters do not imply proportional runtime savings."],"verified":"2026-10-17"},{"id":"closed-loop-robot-software","name":"Closed-Loop Robot Software","title":"Learning and Transferring Closed-Loop Robot Software","date":"2026-09-17","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Optimizes per-task executable controllers from demonstrations and reuses optimized software as references for related tasks.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.19906"},"policy":{"family":"Robot agent","baseModel":"Coding-agent controller optimization with an archive of validated task programs.","modelSize":"Not reported","inputs":"RoboCasa Full observations: three RGB views, metric depth / calibrated point clouds, ground-truth MuJoCo geometry-ID masks and labels, binary robot / gripper / finger contacts, and robot state.","outputs":"Frozen executable robot-control source code with observation-dependent feedback and recovery.","trainingData":"Demonstration-initialized software for four source tasks; optimized references transfer to nine target tasks.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen coding model revises code through rollout / validation feedback; selected source programs execute without further corrective model calls at test.","evaluation":"RoboCasa PandaOmron: four source tasks and nine target tasks involving faucet, drawer, transport and appliance behaviors."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2609.19906","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["The method receives Full observations while public VLA references receive RGB and robot state.","Programs are optimized separately per task, unlike multi-task learned-policy baselines; reported numerical comparisons are references rather than fully matched evaluations.","Binary contact indicators have neither contact-force values nor object IDs; they are simulator state, not a released physical tactile sensor. Physical effectiveness remains untested."],"verified":"2026-10-17"},{"id":"dextouch-wm","name":"DexTouch-WM","title":"DexTouch-WM: Learning Action-Conditioned Tactile World Models from Human Touch for Dexterous Robot Manipulation","date":"2026-09-17","year":2026,"collections":["policy"],"categories":["tactile-foundation","tactile-representation"],"resourceType":"model","summary":"An action-conditioned visual/tactile world model learns shared human/robot contact dynamics for policy evaluation and synthetic demonstration generation.","institution":"Xspark AI / HKUST (Guangzhou) / Tsinghua University / University of Hong Kong / Peking University","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.20649"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2609.20649","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","model","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."}],"caveats":["67D action sequence is an input to the world model. Predicted observations condition separate policies.","Human/robot adaptation and downstream policy-training splits are distinct; synthetic-data utility varies by policy and does not equal all-real training."],"verified":"2026-10-17","model":{"family":"World model","baseModel":"Pretrained video expert plus anatomy-aware tactile expert with aligned 67D pose conditioning.","modelSize":"Not reported","inputs":"Initial RGB, bilateral tactile maps, language and a proposed future 67D retargeted wrist/hand/head pose sequence.","outputs":"Future RGB and bilateral tactile predictions; downstream policies supply actions, so this is not itself an executable policy.","trainingData":"100 h human interaction over 50 tasks +5 h robot world-model pretraining over 6 tasks. Four disjoint downstream tasks have separately partitioned robot/human adaptation sets.","trainingTime":"Not reported","trainingHardware":"Not reported","trainingMethod":"Human-to-robot dynamics pretraining; task-specific world-model adaptation; closed-loop imagined evaluation and synthetic-demonstration generation.","evaluation":"Held-out real-robot prediction; FTP-1/π0.5/X-VLA evaluated in model/real worlds and trained with generated demos on Place Shoes, Stack Bowls, Place Phone, Stand Bottle.","role":"Action-conditioned visual/tactile predictive model; surrogate evaluator and synthetic-data generator, not standalone action policy.","inference":"Human glove recording 60 Hz; model/policy-evaluation throughput Not reported."}},{"id":"foretac-vla","name":"ForeTac-VLA","title":"ForeTac-VLA: A Forecasting-Based Tactile-Vision-Language-Action Model for Contact-Rich Robotic Manipulation","date":"2026-09-17","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Uses observed tactile history and a multi-step contact forecast to condition π0.5 action generation.","institution":"Texas A&M University","venue":"Preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.20980","project":"https://foretac-vla.github.io/"},"policy":{"family":"VLA","baseModel":"π0.5 / SigLIP–PaliGemma plus tactile forecasting Transformer","modelSize":"Not reported","inputs":"RGB, tactile history, instruction and proprioception","outputs":"Robot action chunks conditioned on predicted tactile states","trainingData":"280 demonstrations: 70 per task across four real tasks, collected at 60 Hz","trainingTime":"60k optimization steps; wall-clock not reported","trainingHardware":"One NVIDIA RTX PRO 6000 Blackwell 96GB","controlRate":"Data sampling 60 Hz; forecast offsets 5/10/15/20 frames; deployment query rate not reported","trainingMethod":"Bidirectional cross-attention and ground-truth-to-prediction curriculum","evaluation":"Peg Insertion, Chip Handling, Cap Unscrewing, Board Wiping; low-light and clutter robustness"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."},"evidence":[{"url":"https://arxiv.org/abs/2609.20980","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://foretac-vla.github.io/","label":"Author project page","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2609.20980","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["60 Hz is recording frequency, not a verified policy feedback rate."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."}},"relatedIds":[]},{"id":"hil-umi","name":"HIL-UMI","title":"HIL-UMI: Bringing Human-in-the-Loop Post-Training of Vision-Language-Action Models to Universal Manipulation Interface","date":"2026-09-17","year":2026,"collections":["policy"],"categories":["visual-precision-rl","generalist-foundation"],"resourceType":"policy","summary":"Handheld UMI data collection uses policy-disagreement and advantage feedback to target useful VLA post-training demonstrations without robot execution during collection.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.20659","project":"https://hil-umi.github.io"},"policy":{"family":"VLA","baseModel":"π0.5 with an Energy Score OOD detector and learned progress-based advantage estimator.","modelSize":"Not reported","inputs":"RGB observations, robot state and language at deployment; UMI collection uses calibrated handheld observations/human actions.","outputs":"Robot action chunks; collection interface additionally requests informative human data segments.","trainingData":"Long-horizon task bases: 50 demonstrations and 12k-frame round budgets; other tasks: 80 demonstrations and 2,500-frame round budgets.","trainingTime":"Not reported","trainingHardware":"RTX 4090D local workstation is explicitly for online policy/advantage inference during collection.","controlRate":"UMI collection records at 30 Hz; policy inference rate not reported as the same quantity.","trainingMethod":"Policy-guided robot-free human data collection, advantage-model refinement and advantage-conditioned behavior cloning.","evaluation":"Real manipulation including Fold Towel and Clean Up Table, with controlled per-round data budgets."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/2609.20659","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://hil-umi.github.io","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."}],"caveats":["Robot-free refers to human data collection, not absence of robot deployment evaluation.","Hardware used for live collection inference is not a full policy-training compute budget."],"verified":"2026-10-17"},{"id":"lifd","name":"LIFD","title":"LIFD: Anchored Diffusion for 3D-Aware Scene Memory in Robotic Manipulation","date":"2026-09-17","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Recurrent flow-completed scene tokens retain 3D-aware visual memory for partially observed manipulation from one RGB camera.","institution":"South China University of Technology","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.19796"},"policy":{"family":"Hybrid","baseModel":"Geometry-aware image encoder, anchored rectified-flow scene completion, recurrent memory slots and a visuomotor policy head.","modelSize":"Not reported","inputs":"One RGB observation, proprioception, instruction and recurrent scene memory; multi-view/geometric supervision is training-only.","outputs":"Continuous robot actions conditioned on completed scene tokens.","trainingData":"LIBERO and 50 Meta-World tasks with 50 demonstrations per task; real policy-head adaptation uses ten demonstrations per task family.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Real UR5e Cartesian RTDE commands at 125 Hz; this is independent of policy inference. RGB capture at 30 Hz.","trainingMethod":"Multi-view-agreement representation training, anchored scene completion and staged policy learning.","evaluation":"LIBERO, Meta-World and four UR5e task families under partial observability."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/2609.19796","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."}],"caveats":["Runtime requires RGB only; RealSense depth is used for annotation/visualization, not as a policy input.","Scene completion predicts geometry-aware latent content rather than receiving privileged hidden geometry at deployment."],"verified":"2026-10-17"},{"id":"metapusher","name":"MetaPusher","title":"MetaPusher: Meta Learning and Planning for Nonprehensile Manipulation of Unseen Objects with Rapid Online Adaption","date":"2026-09-17","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Meta-learned geometry-conditioned pushing dynamics adapt after each contact while a kinodynamic search tree is repaired and reused.","institution":"Not reported in accessible affiliation markup","venue":"Preprint","modalities":["RGB","Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Pushing end effector"],"links":{"paper":"https://arxiv.org/abs/2609.21122"},"policy":{"family":"Model-based planning","baseModel":"Meta-learned dynamics and adaptive nonprehensile planning. Geometry-encoded forward dynamics with MAML-style initialization; AORRT/OMPL kinodynamic planner.","modelSize":"Not reported","inputs":"Object contour point cloud, object SE(2) pose, push contacts and observed transitions; physical FoundationPose estimates pose from wrist-mounted RealSense D435.","outputs":"Planar contact-point/normal-direction push actions and UR16e execution.","trainingData":"238 YCB/Google Scanned Objects, 2,000 simulated push transitions per object. Physical adaptation evaluations use separately recorded pushes.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Offline meta-training; each deployed push yields a gradient update, after which the existing planning tree is reconstructed/refined.","evaluation":"Simulation and UR16e physical pushing of unseen objects; separate dynamics adaptation and manipulation/planning evaluations."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No author-linked implementation was verified in the inspected primary manuscript/project. This is an evidence gap, not proof that no implementation exists."},"evidence":[{"url":"https://arxiv.org/abs/2609.21122","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."}],"caveats":["Assumes accurate contour geometry and pose estimates; the demonstrated control is planar SE(2).","Online adaptation changes dynamics weights and is not context-only imitation."],"verified":"2026-10-17"},{"id":"skipvla","name":"SkipVLA","title":"SkipVLA: Skipping VLA Steps with Classical Planning for Fast Robot Manipulation","date":"2026-09-17","year":2026,"collections":["policy"],"categories":["visual-precision-rl","generalist-foundation"],"resourceType":"policy","summary":"A hybrid controller delegates free-space motion to a classical planner and reserves learned VLA calls for contact-rich skills.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.20648"},"policy":{"family":"Hybrid","baseModel":"Frozen VLA visual/language backbone with a learned target-pose/indicator module and VAMP or cuRoboV2 motion planning.","modelSize":"Not reported","inputs":"VLA RGB/state/instruction observations plus robot/planner geometry required by the execution system.","outputs":"Planned collision-free robot motions and VLA-generated contact-rich action chunks.","trainingData":"13 selected LIBERO tasks with 50 demonstrations each, 650 total; no additional expert demonstration collection for the target predictor.","trainingTime":"Not reported","trainingHardware":"NVIDIA Jetson Thor is used for deployment energy measurements, not asserted as training hardware.","controlRate":"Not reported","trainingMethod":"Learn pose targets/policy switching from existing VLA knowledge and combine with classical planning.","evaluation":"π0.5, SmolVLA and MolmoAct2 variants; 13 simulated LIBERO tasks and three real YAM-arm pick-and-place tasks."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/2609.20648","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."}],"caveats":["Planner-assisted execution differs from direct learned-policy control throughout the task.","Model sizes and speedups remain base-policy-specific."],"verified":"2026-10-17"},{"id":"stageguard","name":"StageGuard","title":"StageGuard: Learning Stage Transitions for Long-Horizon Robot Tasks via Agentic Distillation","date":"2026-09-17","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Distills a teacher model’s structured explanations into a compact stage-transition monitor that switches learned robot skills during long-horizon execution.","institution":"Huawei Noah’s Ark Lab; University of British Columbia; University of Toronto; McGill University; Huawei 2012 Labs","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.20791"},"policy":{"family":"Hybrid","baseModel":"Qwen3.5-397B-A17B teacher, Qwen3.5-0.8B student monitor and π0.5 low-level manipulation policies.","modelSize":"0.8B monitor; teacher has 397B total / 17B active parameters. Low-level policy parameters are separate.","inputs":"Visual observations, current subtask and semantic execution history; demonstrations provide teacher reasoning / transition supervision.","outputs":"Continue / complete / interrupt decisions, next-subtask specifications and updated semantic history, controlling a local VLA executor.","trainingData":"Ten training trajectories per task for LIBERO-Logic / BEHAVIOR-1K monitoring. Physical π0.5 deployment uses fifty demonstrations per subtask.","trainingTime":"10,000 fine-tuning steps on LIBERO-Logic and 35,000 on BEHAVIOR-1K; elapsed hours are not reported.","trainingHardware":"Not reported","controlRate":"Monitor throughput: 1.03 Hz on LIBERO, 1.74 Hz on BEHAVIOR-1K and 2.2 Hz in the reported physical setting; these are not VLA servo rates.","trainingMethod":"Offline agentic teacher explanation generation and supervised student distillation; integrated robot policies are separately fine-tuned.","evaluation":"LIBERO-Logic / BEHAVIOR-1K transition monitoring and twenty closed-loop trials per physical UR5e / Piper setting."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The original paper is public; code, model and dataset release scopes are verified separately where established."},"evidence":[{"url":"https://arxiv.org/abs/2609.20791","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."}],"caveats":["StageGuard is a supervisory module within an executable hierarchy; it does not directly predict continuous robot actions."],"verified":"2026-10-17"},{"id":"traceflow","name":"TraceFlow","title":"TraceFlow: Guiding Frozen Flow-Matching Robot Policies with Success and Failure Traces","date":"2026-09-17","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Progress-aligned success and failure memories guide a frozen flow-matching action field using terminal rollout labels.","institution":"The University of Hong Kong","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.20646","project":"https://zhangjiaxuan-xuan.github.io/TraceFlow/","code":"https://github.com/zhangjiaxuan-Xuan/TraceFlow","data":"https://www.modelscope.ai/datasets/JoeyXuan/traceflow-realworld"},"policy":{"family":"Hybrid","baseModel":"Frozen π0.5 or PrediMem flow action expert, retrieval head and bounded signed TraceBank guidance.","modelSize":"Not reported","inputs":"Current visual/state context and retrieved time-ordered state/action traces with terminal success/failure bits.","outputs":"Action chunks corrected toward successful and away from failed aligned actions during flow integration.","trainingData":"Target-task training traces initialize TraceBank; deployed rollouts may be appended with one terminal outcome label each.","trainingTime":"Reported T-Dense5 retrieval head uses 30 epochs in the matched real configuration; action policy remains frozen during stacking rounds.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Task-specific retrieval setup followed by parameter-fixed signed density guidance; memory can grow from deployment experience.","evaluation":"LIBERO, paired OOD task variants, RoboMemArena and real ordered packing / other manipulation tasks."},"release":{"code":"Available","weights":"Not applicable","data":"Partial","license":"Apache-2.0 for repository code; external model, dataset and dependency terms remain separate.","notes":"Official guidance code and real-world data are released. Base Pi0.5/PrediMem checkpoints are third-party resources; retrieval setup and frozen rollout-stacking are distinct stages."},"evidence":[{"url":"https://arxiv.org/abs/2609.20646","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://zhangjiaxuan-xuan.github.io/TraceFlow/","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/zhangjiaxuan-Xuan/TraceFlow","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."},{"url":"https://github.com/zhangjiaxuan-Xuan/TraceFlow/blob/HEAD/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Actual license file inspected; code license does not automatically license data or weights."},{"url":"https://github.com/zhangjiaxuan-Xuan/TraceFlow","label":"Verified official code artifact","fields":["links.code","release.code"],"note":"Author project / repository cross-link verified; current release distinguished from original paper experiments."},{"url":"https://www.modelscope.ai/datasets/JoeyXuan/traceflow-realworld","label":"Verified official data artifact","fields":["links.data","release.data"],"note":"Author project / repository cross-link verified; current release distinguished from original paper experiments."}],"caveats":["Not entirely training-free: retrieval head setup is trained, while subsequent policy/stacking comparisons keep weights fixed.","Per-suite selected settings and negative effects on some conditions are distinct from one universal gain."],"verified":"2026-10-17"},{"id":"va-bench","name":"VA-Bench Shared Agent Harness","title":"VABench: Measuring Embodied Spatial Intelligence through Visual Demonstrations, Active Perception, and Metric Control","date":"2026-09-17","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Connects general multimodal models to active camera control and metric Cartesian actions, using RGB demonstrations as procedural context.","institution":"Dalian University of Technology; Nanyang Technological University","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.19554","code":"https://github.com/zhangzhongbo2213/VABench"},"policy":{"family":"Robot agent","baseModel":"Twelve evaluated MLLM conditions, including Qwen3.8-max, Opus-5 and GPT-5.6-sol, share one fixed executor and agent harness.","modelSize":"Not reported","inputs":"Task, sampled RGB demonstration frames, current RGB observations, interaction history and tool / execution feedback.","outputs":"Camera viewpoint changes, chosen grasp regions, arm roles, metric Cartesian targets / waypoints and corrective actions.","trainingData":"No task-specific policy training. RGB-only demonstrations are summarized into reusable procedural context before execution.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen MLLM tool use with model-agnostic validation, inverse kinematics and trajectory execution; no learned action head.","evaluation":"Fourteen RoboTwin task families: eleven single-arm and three dual-arm; seven held-out variants and a five-object composition track. Three runs use twenty verified seeds per base task."},"release":{"code":"Available","weights":"Not applicable","data":"Not verified","license":"MIT (code); model, dependency and dataset terms are separate.","notes":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms."},"evidence":[{"url":"https://arxiv.org/abs/2609.19554","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/zhangzhongbo2213/VABench/main/README.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."},{"url":"https://raw.githubusercontent.com/zhangzhongbo2213/VABench/main/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Code-license text checked directly; this license is not automatically assigned to model weights or third-party data."}],"caveats":["Object poses, reward / checker state, camera calibration and seed-verification probes are evaluator-only.","A learned RGB-D pre-grasp module appears only in separately named ablations, not the primary direct-control condition.","This policy record describes the shared benchmark harness; the VABench benchmark role is flagged for separate full metadata closure."],"verified":"2026-10-17"},{"id":"workspace-models","name":"Workspace Models","title":"Workspace Models: Lightweight Robotic Memory via Saliency-Driven Supervision","date":"2026-09-17","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"VLM-derived saliency supervision trains a lightweight recurrent workspace representation for memory-dependent robot policies.","institution":"MIT","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.20820"},"policy":{"family":"Hybrid","baseModel":"DINOv3 tokenizer, workspace encoder / latent slot and set-reconstruction decoder, with a reactive action policy.","modelSize":"Not reported","inputs":"RGB/proprioceptive observations and recurrent workspace token; teacher VLM queries are training-only.","outputs":"Robot action chunks conditioned on workspace memory.","trainingData":"Scripted memory-task trajectories in ManiSkill3 and hardware data; CubeDrop uses 272 training trajectories.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Simulation physics at 100 Hz; data collection and policy control at 20 Hz with seven-dimensional end-effector/gripper actions.","trainingMethod":"Use a VLM to label task-salient current/past information, distill a Hungarian set-reconstruction loss into memory, then train reactive control.","evaluation":"Memory-intensive simulation and physical manipulation."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/2609.20820","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."}],"caveats":["Teacher reasoning and privileged scripted collection state are not runtime policy inputs.","No repeated large-VLM call is required in the deployment action loop."],"verified":"2026-10-17"},{"id":"fare","name":"FaRe","title":"Causal-History Test-Time Scaling for Failure Recovery in Autoregressive World-Action Models","date":"2026-09-16","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Failure recovery selects among continuing, rebuilding a reliable history prefix and resetting the causal cache of a frozen world-action policy.","institution":"Hong Kong University of Science and Technology and collaborators","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.18016"},"policy":{"family":"World-action model","baseModel":"Inference-time causal-history recovery for WAMs. Frozen LingBot-VA and Robo-Dopamine GRM-2.0-8B-Preview progress evaluator.","modelSize":"8B progress-model variant; complete combined system count not reported.","inputs":"Latest robot RGB observations/state, language task, retained causal history and measured progress; RoboTwin progress/history uses top/left/right views.","outputs":"Selected action chunk and committed causal KV state after comparing Continue, Prefix and Reset hypotheses.","trainingData":"Released RoboTwin-posttrained LingBot-VA checkpoint; no extra recovery demonstrations or parameter updates.","trainingTime":"No additional training for recovery.","trainingHardware":"Experiments use NVIDIA H20 GPUs; count not reported. These are evaluation resources.","controlRate":"Not reported","trainingMethod":"Frozen-model progress detection, compatible historical anchor retrieval and hypothesis rollout/verification; memory cache changes, weights do not.","evaluation":"RoboTwin 2.0 Easy/Hard and dual SO-101 Fold Towel, Stack C-shaped Blocks and Organize Vegetables, 20 trials each."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No author-linked implementation was verified in the inspected primary manuscript/project. This is an evidence gap, not proof that no implementation exists."},"evidence":[{"url":"https://arxiv.org/abs/2609.18016","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."}],"caveats":["Reported baseline tables mix author reimplementations with results taken from prior work; comparisons need that scope.","History revision is inference-time state modification, not recursive weight self-improvement."],"verified":"2026-10-17"},{"id":"gavel","name":"GAVEL","title":"GAVEL: Graph World Models for Verified and Efficient Long-Horizon LLM Task Planning","date":"2026-09-16","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"An explicit graph world model verifies and repairs LLM symbolic plans and updates beliefs about unseen object locations.","institution":"Duke University and collaborators","venue":"Preprint","modalities":["Language"],"environments":["Simulation"],"embodiments":["Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2609.19315"},"policy":{"family":"Model-based planning","baseModel":"Symbolic world-model verification and belief-aware planning. Qwen3-4B/8B and other LLM planners; Qwen3-1.7B LoRA task-understanding adapters and Relational Semantic Network room-location prior.","modelSize":"Planner variants 4B and 8B; 1.7B understanding adapter base. No single complete-system count.","inputs":"Language instruction and symbolic believed scene graph with object properties, relations and partial object-location observations; action preconditions/effects are explicitly modeled.","outputs":"Verified sequences of nine symbolic navigation/manipulation primitives, with online task reordering.","trainingData":"LoRA training corpus size and complete compute budget not reported.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Fine-tuned task-understanding adapters plus inference-time symbolic rollout/repair and belief updates; planner-weight adaptation is not the recovery mechanism.","evaluation":"100 BEHAVIOR-1K single tasks and 500 multi-task instructions, mainly in a lightweight 2D symbolic executor; 100 single-task outcomes cross-checked in OmniGibson."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No author-linked implementation was verified in the inspected primary manuscript/project. This is an evidence gap, not proof that no implementation exists."},"evidence":[{"url":"https://arxiv.org/abs/2609.19315","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."}],"caveats":["This is high-level symbolic manipulation planning, not measured low-level visuomotor control.","The graph validates logical consequences and does not establish geometric reachability or contact feasibility."],"verified":"2026-10-17"},{"id":"gpt-policy","name":"GPT-Policy","title":"In-Context Robot Learning with VLM Agents","date":"2026-09-16","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A frozen VLM combines task references, current observations and persistent interaction history to select constrained Cartesian robot tools.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.19138","project":"https://cheng-haha.github.io/GPT-Policy","code":"https://github.com/cheng-haha/GPT-Policy","data":"https://huggingface.co/datasets/SII-CDZ/gpt-policy-robodojo-contexts"},"policy":{"family":"Robot agent","baseModel":"GPT-6 Astra in the main experiments; Fable 5.1 and Kimi K3 are separately compared.","modelSize":"Not reported","inputs":"Task instruction, RGB images, robot state, human / robot video, goal images or interaction references, and prior execution history.","outputs":"move_to absolute TCP targets, move_eef_chunk waypoint sequences and set_gripper requests; an analytic adapter validates IK, samples paths and assigns timing.","trainingData":"No target-task model fine-tuning; human video, robot video / action records and self-interaction are in-context conditions.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen VLM; multimodal context and returned execution feedback guide constrained tool use.","evaluation":"Ten real-robot tasks with three trials per task / context condition; reference and model ablations."},"release":{"code":"Available — source for review/evaluation; redistribution license pending","weights":"Not applicable","data":"Partial — ten-task RoboDojo demonstration contexts","license":"License pending: actual repository LICENSE grants no redistribution or commercial-use license. Context dataset has separate LICENSE.txt/THIRD_PARTY.md terms; no blanket open-source claim.","notes":"Actual ARX/YAM harness, hardware adapters and RoboDojo backend are public. The dataset contains ten-task context packages, selected frames and numeric action references, reproduction tools and integrity checks. It does not include all physical experiment prompts/recordings. The actual LICENSE restricts this preview to review/evaluation rather than granting redistribution or commercial use."},"evidence":[{"url":"https://arxiv.org/abs/2609.19138","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/cheng-haha/GPT-Policy","label":"Author-linked repository","fields":["links.code"],"note":"Repository identity; code and checkpoint release status are stated separately."},{"url":"https://github.com/cheng-haha/GPT-Policy","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/datasets/SII-CDZ/gpt-policy-robodojo-contexts","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["The sample size is small, and progress scores and binary task success are different metrics.","Absolute Cartesian waypoint tools differ from RoboICL's fixed-rate incremental action-matrix interface."],"verified":"2026-10-19"},{"id":"unidex-vitac","name":"UniDex-ViTac","title":"UniDex-ViTac: Learning Unified Visuo-Tactile Dexterous Manipulation Policy from Human Video Data","date":"2026-09-15","year":2026,"collections":["policy"],"categories":["tactile-il","rgbd-3d"],"resourceType":"policy","summary":"Converts annotated human videos into simulation specialists and contact-labeled rollouts, then distills an ACT generalist.","institution":"Not reported","venue":"Preprint","modalities":["Depth","Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2609.16504","project":"https://unidex-vitac.github.io/"},"policy":{"family":"ACT","baseModel":"ACT with point-cloud and fingertip contact tokens","modelSize":"Not reported","inputs":"Depth-derived point clouds, proprioception and four binary fingertip contact signals","outputs":"Arm/hand target-action chunks","trainingData":"50 human demonstrations across ten objects → 10k simulated robot trajectories","trainingTime":"≈2 days for ten specialists and rollout collection; generalist training excluded","trainingHardware":"One NVIDIA RTX 5090 for specialists/data collection","controlRate":"Not reported","trainingMethod":"Object-specific residual RL followed by pooled ACT imitation learning","evaluation":"Simulation ten-object suite; 110 physical trials across six seen/five unseen objects without real policy fine-tuning"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."},"evidence":[{"url":"https://arxiv.org/abs/2609.16504","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://unidex-vitac.github.io/","label":"Author project page","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2609.16504","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Touch consists of four contact bits, not dense images or calibrated forces.","Human references and object ground truth are training-only."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."}},"relatedIds":[]},{"id":"bench2dex","name":"Bench2Dex","title":"Bench2Dex: Benchmarking Visuo-Tactile Bimanual Dexterous Manipulation Across Dexterous Hands","date":"2026-09-14","year":2026,"collections":["benchmark"],"categories":["tactile-benchmarks","dexterous-benchmarks"],"resourceType":"benchmark","summary":"A bimanual dexterous suite across 12 arm-hand pairings, using a shared geometry-based tactile representation and controlled generalization channels.","institution":"East China University of Science and Technology / Shanghai Jiao Tong University / Fudan University","venue":"Preprint","modalities":["RGB","Depth","Tactile","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.15726","project":"https://bench2dex.github.io/","code":"https://github.com/Bench2Dex/Bench2Dex","data":"https://huggingface.co/Bench2Dex"},"benchmark":{"simulator":"Isaac Sim / Isaac Lab","simulatorVersion":"Released setup: Isaac Sim 5.1.0 / Isaac Lab 2.3.2.","taskCount":"26 long-horizon task scenes spanning 12 arm-hand embodiments; not a full 26×12 crossed task matrix","tasks":["Canned Food Tray Arrangement","Baking Tray Prep","Jigsaw Puzzle Assembly","Gaming Desk Setup","Tool Box Loading","Medicine Shoebox Pack","Sports Ball Cup Sort","Stationery Category Sorting","Faucet Cup Water Fill","Wine Glass Plate Balance","Bimanual Piano Melody","Cleaner Box Loading","Toilet Lid Cleaner Pour","Shoebox Accessory Pack","Citrus Plate Loading","Fridge Wine Interhand Pour","Breadbasket Fast-Food Loading","Fruit Bowl Loading","Screwdriver Box & Hammer","Height-Sorted Trash Disposal","Fridge Fruit Shelf Sorting","Soup Serving","Frypan Stand & Pour","Microwave Bowl Loading","Condiment Box Loading","Ball Box Loading"],"taskListScope":"Complete 26-task released scene catalogue from official documentation.","capabilities":["Bimanual dexterity","Tool/device use","Articulated objects","Multi-stage execution","Invariance and equivariance robustness"],"metrics":"Stable task success (terminal predicate with default 0.5 s dwell); latched stage completion; efficiency; SafeSR and violation/drop diagnostics; bootstrap intervals.","robots":"IIWA7+Sharpa; JAKA ZU7+DexHand021; Panda+Orca/Allegro; RM65+Revo2; xArm7+Ability/LEAP; UR5+RH5DG2/RH56DFX/Shadow/Schunk/Wuji.","sensors":"RGB / depth cameras and shared visuotactile images generated by ray-sampling contact surfaces; proprioception and privileged object-state annotations. Geometry-based contact response is not an identified physical optical sensor model.","dataCollection":"Glove-and-wrist-tracking teleoperation: Manus hand skeletons, ARKit wrist tracking, DexPilot retargeting and arm inverse kinematics. Offline replay adds the full sensor suite.","data":"Approximately 1,300 human-teleoperated demonstrations across the task catalogue.","protocol":"4 policies × 26 tasks × 4 channels × 50 rollouts = 20,800 evaluation episodes. Channels None, Equivariance, Invariance and All control seven perturbation factors; task-macro averages.","baselines":"ACT, Diffusion Policy, π0.5, GR00T N1.5."},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"MIT code; dataset/asset terms separate","notes":"Training, inference, teleoperation code and data links public; inspect individual dataset/asset licenses."},"evidence":[{"url":"https://arxiv.org/abs/2609.15726","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://bench2dex.github.io/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/Bench2Dex/Bench2Dex","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://github.com/Bench2Dex/Bench2Dex/blob/main/LICENSE","label":"Official source","fields":["release.license"],"note":"License text read directly; code license does not automatically cover models, data or simulator."},{"url":"https://arxiv.org/abs/2609.15726","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Section3.1 online teleoperation and3.2 offline multimodal replay."}],"caveats":["Tasks are allocated to embodiments rather than every hand performing every task.","Shared simulated contact images are not equivalent to measured tactile hardware signals."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not verified","data":"Available","license":"MIT code; dataset/asset terms separate","notes":"Training, inference, teleoperation code and data links public; inspect individual dataset/asset licenses."}},"relatedIds":[]},{"id":"stereopatch","name":"StereoPatch","title":"StereoPatch: Patch-Aligned RGB-Depth Fusion for Spatial Perception in Robot Manipulation","date":"2026-09-14","year":2026,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Patch-aligned RGB-depth fusion binds measured metric geometry to the visual tokens used for action decoding.","institution":"University of Sydney, School of Computer Science / Australian Centre for Robotics","venue":"arXiv preprint","modalities":["RGB","Depth","Point cloud","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.15509","project":"https://aus.bot/stereopatch/"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2609.15509","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://aus.bot/stereopatch/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."}],"caveats":["Requires registered RGB/depth; geometry-only and late-fusion baselines have distinct representation assumptions."],"verified":"2026-10-17","policy":{"family":"Transformer / BC","baseModel":"Asymmetric cross-attention on a shared RGB/depth patch grid; policy-backbone-compatible representation.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Registered RGB and metric depth plus policy-specific proprioception.","outputs":"Continuous manipulation actions from the conditioned downstream policy.","trainingMethod":"Imitation learning with patch-aligned cross-modal features; policy objective unchanged.","trainingData":"Task demonstrations across physical and three simulation suites.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Six physical tasks plus simulation architecture/generalization/operating-limit studies.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"touch2trace","name":"Touch2Trace","title":"Touch2Trace: Tactile-Driven Imitation Learning for Dexterous Cable Tracing","date":"2026-09-14","year":2026,"collections":["policy"],"categories":["tactile-il","tactile-foundation"],"resourceType":"policy","summary":"A tactile-only behavior-cloning transformer performs dexterous cable tracing without explicit cable-state estimation.","institution":"Not reported","venue":"Preprint","modalities":["Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2609.15921"},"policy":{"family":"Transformer","baseModel":"Frozen ViT-MAE tactile encoder; three-layer causal transformer with five-component Gaussian-mixture action head.","modelSize":"4.1 M pipeline parameters:1.6 M frozen tactile encoder+2.5 M policy backbone.","inputs":"Two 32×32 normal-pressure fingertip images and eight controlled joint positions;15-observation history.","outputs":"Eight finger-joint position targets.","trainingData":"16 teleoperated demos/~14 min total, 12 training/10.1 min and 4 validation/~4 min; all policy training uses USB-0 cable in ring routing.","trainingTime":"Policy 50 epochs; wall-clock Not reported.","trainingHardware":"Not reported","controlRate":"60 Hz deployed control;3.4 ms end-to-end measured on RTX4500Ada (inference hardware, not training).","trainingMethod":"Self-supervised tactile-MAE pretraining then frozen-encoder behavior cloning for 50 epochs.","evaluation":"Tesol lo DG-5 F cable tracing; unseen Ethernet/USB variants and straight/ring routing."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2609.15921","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."}],"caveats":["TacV5 measures normal pressure, not direct shear. Shear dynamics are inferred from temporal pressure sequences.","Acc el ero meter, audio and external video are recorded but unused by the deployed policy."],"verified":"2026-10-17"},{"id":"revolve","name":"REVOLVE","title":"REVOLVE: An Automated Closed-Loop Framework for Evolving Robot Manipulation with Minimal Human Intervention","date":"2026-09-13","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Pairs automated geometric reset / correction with external judgment memory and repeated policy updates to improve real manipulation with less manual intervention.","institution":"Jiangnan University","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.14633"},"policy":{"family":"Hybrid","baseModel":"VLA-Adapter manipulation policies with a visual judgment agent, CLIP-indexed mismatch memory and classical correction / reset controllers.","modelSize":"Not reported","inputs":"Robot camera observations and proprioception; calibrated RGB-D point clouds support reset / corrective geometry. Human verification labels judgment mismatches.","outputs":"Robot-policy actions, switch-to-correction decisions, corrected trajectories, updated policy checkpoints and revised external judgment memory.","trainingData":"One hundred initial demonstrations per task; one hundred real-world rollouts per task in each subsequent iteration, adding successful and failure–correction trajectories.","trainingTime":"Five policy-improvement iterations in the reported comparison; elapsed training hours are not reported.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Corrective Evolution Loop updates learned policy weights; Judgement Evolution Loop updates external retrieval memory rather than the judgment model weights.","evaluation":"Four real tasks: Sort Workpiece, Throw Paper, Pour Water and Ring Bell, plus human-effort / judgment comparisons."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The original paper is public; code, model and dataset release scopes are verified separately where established."},"evidence":[{"url":"https://arxiv.org/abs/2609.14633","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."}],"caveats":["The current system retains human-led data choices / verification and task-specific classical correction controllers; it is not fully autonomous general self-improvement.","The initial / improved policy success rates and judgment accuracy refer to different components."],"verified":"2026-10-17"},{"id":"wam-control-survey","name":"World-Action Models — Learning and Control","title":"World-Action Models for Robot Learning and Control: A Survey","date":"2026-09-13","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","generalist-foundation"],"resourceType":"survey","summary":"A robotics-oriented review of how predictive world representations connect to executable actions and closed-loop control.","institution":"MBZUAI; Caltech; Amazon FAR; University of Virginia; Georgia Tech; NYU; UC Berkeley","venue":"Preprint · Survey","modalities":[],"environments":["Literature review"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2609.16074","project":"https://rcl-robotics.github.io/Awesome-World-Action-Models/","code":"https://github.com/RCL-Robotics/Awesome-World-Action-Models"},"survey":{"scope":"World-action models for manipulation, navigation and autonomous driving.","taxonomy":["Shared backbone versus separate world/action experts","Joint future-action prediction versus inverse-dynamics factorization","State representations and action interfaces","Pretraining, adaptation and scaling"],"adaptationRegimes":["Policy fine-tuning","Data augmentation","Reinforcement learning","Predictive inference and planning"],"contextSources":["Language and observations","Robot trajectories","Action-free video priors","Optional additional sensors"],"evaluationAxes":["World prediction","Action grounding","Closed-loop task success","Temporal and spatial consistency","Inference cost"],"corpus":"19-page survey; application and resource tables summarize selected methods. No exhaustive screening denominator is claimed here.","reviewMethod":"Technical synthesis of model components, transition objectives, training pipelines, applications and evaluation protocols.","coverageLimits":"Includes non-manipulation domains and predictive models that are not standalone action policies. An architecture taxonomy does not establish in-context adaptation for every WAM."},"release":{"code":"Available — companion literature website and collection","weights":"Not applicable — survey","data":"Literature library; no new robot training corpus","license":"MIT for companion repository; paper and third-party figures retain their own rights","notes":"No new executable policy or shared leaderboard result is introduced by this review."},"evidence":[{"url":"https://arxiv.org/abs/2609.16074","label":"Primary WAM survey","fields":["title","date","institution","survey"],"note":"PDF Sections III–V and Figure 5 distinguish two independent architecture axes."},{"url":"https://rcl-robotics.github.io/Awesome-World-Action-Models/","label":"Official project and literature library","fields":["survey","links","release"],"note":""},{"url":"https://github.com/RCL-Robotics/Awesome-World-Action-Models/blob/main/LICENSE","label":"Companion repository MIT license","fields":["release.license"],"note":""}],"caveats":["The PDF template header is not evidence of journal publication.","Listed sensing modalities describe reviewed systems, not deployment inputs of a survey."],"relatedIds":[],"verified":"2026-10-17"},{"id":"gift-force-transfer","name":"GIFT","title":"GIFT: Glove-Inferred Force Transfer: Force-Aware Human-to-Robot Skill Transfer from a Wearable Sensing Glove to a Robot Hand Without Tactile Sensors","date":"2026-09-12","year":2026,"collections":["policy"],"categories":["tactile-il","tactile-foundation"],"resourceType":"policy","summary":"Calibrated human glove force and motor-current-derived robot force provide a common physical interface for skill transfer.","institution":"Independent author; institutional affiliation Not reported","venue":"Preprint","modalities":["RGB","Force/Torque","Proprioception"],"environments":["Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2609.14173","project":"https://tzahsarusi.github.io"},"policy":{"family":"ACT","baseModel":"ACT/LeRobot with 13 Dglove-state conditioning andforce-aware hand actions.","modelSize":"Not reported","inputs":"Egocentric RGB, finger flexion, wrist orientation and five fingertip force estimates; robot forces derive from actuator current residuals.","outputs":"Five hand joint targets; arm follows a preprogrammed wrist trajectory.","trainingData":"Human-only glove demonstrations; same corpus and 20 k training steps for with/without force ladder.","trainingTime":"20,000 steps; wall-clock Not reported.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Behavior cloning with calibrated newton-scale force interface and deployment current baseline.","evaluation":"Aero Hand onSO101:grasp/lift cup under force-aware control."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2609.14173","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://tzahsarusi.github.io","label":"Official author project","fields":["links","release"],"note":"Live project artifact links inspected on2026-10-17."}],"caveats":["Robot has no tactile sensors; runtime contact force is estimated from motor currents, not directly measured glove pressure.","Policy control shand only; programmed arm lift should not be claimed as learned full-arm policy."],"verified":"2026-10-17"},{"id":"visible-touch","name":"Visible Touch","title":"Visible Touch: Rendering Contact for Visuomotor Policies","date":"2026-09-12","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Renders contact vectors or binary contacts onto camera images so existing image-conditioned policies can use touch without architecture changes.","institution":"UCLA","venue":"CoRL 2026 Spotlight","modalities":["RGB","Tactile","Force/Torque","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.14156","project":"https://visibletouch.github.io/","code":"https://github.com/qaz1678249/visibletouch"},"policy":{"family":"VLA","baseModel":"BC-Transformer, miniVLA and π0.5-DROID","modelSize":"miniVLA: Qwen2.5-0.5B component; π0.5 paper reports ≈2.3B plus 311M action expert","inputs":"RGB with projected contact overlays; proprioception; language for VLA variants","outputs":"Original backbone action space and chunks","trainingData":"LIBERO demonstrations; 100 recorded demos per real task (397 accepted total)","trainingTime":"miniVLA: 200k steps; real LoRA runs up to 20k steps, usually early-stop at 5.1k–7.9k","trainingHardware":"miniVLA: 4 L40S; real π0.5 LoRA: 4 H200 per run","controlRate":"Real data/control 10 Hz; inference query rate not separately reported","trainingMethod":"Image-space contact rendering; BC or LoRA adaptation with unchanged backbone","evaluation":"LIBERO suites; Lift, Transfer Tube, Put Mug in Dishwasher, Plug Charger on xArm7"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Author project links implementation and sensor materials; weights/data download scope not verified."},"evidence":[{"url":"https://arxiv.org/abs/2609.14156","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/qaz1678249/visibletouch","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2609.14156","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["LIBERO overlays use simulator contact forces; real observations use custom magnetic taxels.","The state-wrench baseline and image overlay do not have identical information content."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Author project links implementation and sensor materials; weights/data download scope not verified."}},"relatedIds":[]},{"id":"agent-as-policy","name":"Agent as Policy (AGP)","title":"Agent as Policy for Robotic Manipulation","date":"2026-09-11","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"General coding agents write and select robot programs from multimodal feedback on physical manipulation tasks, while experience files support repeated-run refinement and transfer.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.12541","project":"https://agent-as-policy-2026.github.io/","code":"https://github.com/agent-as-policy-2026/agent-as-policy","data":"https://huggingface.co/datasets/Agent-as-Policy/agent-as-policy","data-robodojo":"https://huggingface.co/datasets/Agent-as-Policy/agent-as-policy-robodojo","results-robodojo":"https://github.com/agent-as-policy-2026/agent-as-policy/blob/main/robodojo/results/seed0/_summary.md"},"policy":{"family":"Robot agent","baseModel":"Frozen Codex agents using GPT-6 Astra, GPT-5.6 Sol, Terra or Luna; Claude Code variants include Opus 5 and Fable 5.1.","modelSize":"Not reported","inputs":"Task definitions, demonstrations / reference images where specified, robot camera observations, state, execution feedback and reusable experience. The wrist RealSense D405 supplies aligned metric RGB-D, and calibration / camera poses let programs map measurements into robot coordinates.","outputs":"Executable programs that interpret observations, compute joint or end-effector references, and control single or bimanual YAM arms.","trainingData":"No robot-specific base-model fine-tuning; preparation and repeated-run experience refine task definitions and reusable artifacts.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen models; a preparation agent builds reusable task context and a runtime execution agent writes / selects control programs. Experience can be frozen for model-to-model transfer.","evaluation":"Physical video-guided assembly, image-guided construction, dice flipping, targeted throwing and bimanual towel folding. The released RoboDojo adapter now reports one complete seed: 54 configurations / 2,100 episodes; Score 63.63 and success 56.13%, scored by the benchmark summarizer. This author-published seed is not an official live-leaderboard result."},"release":{"code":"Available","weights":"Not applicable","data":"Available — published session/trajectory bundles","license":"Apache-2.0 original code, verified from LICENSE; published dataset card declares CC BY 4.0. Vendored SDK/connectors retain MIT/Apache notices.","notes":"Current source contains real-arm/camera bridge, calibration, experiment harness, task-result artifacts and a separate RoboDojo adapter. The official HF dataset inventory includes captures/ tar bundles, videos/, traces/ and trials/ files for observations, video, joints and agent traces; coverage is the published sessions, not an inferred complete reproduction of every trial. A separate public RoboDojo dataset includes recordings, received images and tool traces for the released 2,100-episode seed; its coverage does not establish all physical-paper rollouts."},"evidence":[{"url":"https://arxiv.org/abs/2609.12541","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/agent-as-policy-2026/agent-as-policy","label":"Author-linked repository","fields":["links.code"],"note":"Repository identity; code and checkpoint release status are stated separately."},{"url":"https://github.com/agent-as-policy-2026/agent-as-policy","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/datasets/Agent-as-Policy/agent-as-policy","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://github.com/agent-as-policy-2026/agent-as-policy/blob/main/robodojo/results/seed0/_summary.md","label":"Official current source / artifact inventory — 21 October 2026","fields":["release","links","policy"],"note":"New source summary reports native54configurations and2,100 episodes; official-board membership remains distinct."},{"url":"https://huggingface.co/datasets/Agent-as-Policy/agent-as-policy-robodojo","label":"Official current source / artifact inventory — 21 October 2026","fields":["release","links","policy"],"note":"Actual dataset API/file inventory inspected for recordings and traces; published CC BY 4.0 dataset terms are separate from Apache code."}],"caveats":["Throwing time / token statistics and successful-trial-only summaries have different denominators.","The task preparation and experience-refinement budget must be retained when comparing against one-shot execution."],"verified":"2026-10-21"},{"id":"star-vtla","name":"STAR — sparse tactile VTLA","title":"STAR: Sparse Tactile Representation Learning in Vision-Tactile-Language-Action Models for Dexterous Manipulation","date":"2026-09-11","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Sparse-global tactile tokens and sparse future-touch supervision adapt a VLA to bimanual dexterous manipulation.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.12549"},"policy":{"family":"VLA","baseModel":"π0.5: PaliGemma-2 B VLM, Gemma-300 M action expert; MAE tactile encoder initialized by visual-tactile joint pretraining.","modelSize":"Gemma-2 B VLM + Gemma-300 M action expert and tactile/vision encoders; total Not reported.","inputs":"Three RGB views, language/discretized proprioception, bilateral 268-dimensional piezoresistive tactile measurements rendered as tactile images.","outputs":"50-step 64D padded action chunks;34 active dimensions: two 7D end-effector poses and two 10D hand joint vectors.","trainingData":"200 h /10,576 trajectories /65 collected tasks; four evaluated tasks receive 100 task-specific post-training trajectories each.","trainingTime":"6 multitask epochs +100 post-training epochs; wall-clock Not reported.","trainingHardware":"Not reported","controlRate":"30 Hz control grid; tactile prediction budget distributed over 1.67 s.","trainingMethod":"15 k tactile-MAE iterations;6 epochs multitask fine-tuning;100 epochs task-specific post-training, with sparse future-touch prediction.","evaluation":"Four real tasks: earbud flipping, stacked-book retrieval, postcard retrieval and multi-object grasping;20 trials/task with shared initial configurations."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2609.12549","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."}],"caveats":["65 data-collection tasks are not 65 evaluated tasks. Evaluation objects are held out from pretraining but included in 100 post-training demos/task.","This STAR is the sparse-tactile method, distinct from StarVLA."],"verified":"2026-10-17"},{"id":"2am","name":"2AM","title":"2AM: Grounding Agent-Side Memory as Guidance for Steerable Action Models in Long-Horizon Manipulation","date":"2026-09-10","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Keeps persistent episode memory in a reasoning agent and steers one episodically stateless learned action model with text and optional image-space hints.","institution":"KU Leuven; Meituan; Flanders Make","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.11308"},"policy":{"family":"Hybrid","baseModel":"Qwen3.8-27B Agent Model; Qwen3-VL-4B Action Model with a flow-matching Action Expert.","modelSize":"27B agent and 4B action backbone; the action expert adds separate parameters not quantified here.","inputs":"Task, agent-view / wrist-view RGB and robot state; agent memory is translated into subtask text and optional grasp / place / move points.","outputs":"Grounded steering commands and sixteen-step continuous action chunks; no separate grasp, place or move controller.","trainingData":"Offline demonstrations supply stages, object boxes / masks, gripper contact and object motion for deterministic steering-label construction.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Action-model training uses optional-condition dropout, spatial noise and temporal jitter; deployment agent updates external history while the action model carries no persistent episode memory.","evaluation":"LIBERO-Mem with a language-only ablation and an aligned in-house π0 reproduction; cross-benchmark and physical validation remain future work."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The original paper is public; code, model and dataset release scopes are verified separately where established."},"evidence":[{"url":"https://arxiv.org/abs/2609.11308","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."}],"caveats":["Privileged annotations construct training targets only; deployment explicitly removes depth, segmentation, object identifiers / poses, oracle subgoals and rewards.","No real-robot experiments are claimed by the current paper."],"verified":"2026-10-17"},{"id":"attention-dp3","name":"Attention-DP3","title":"Attention-DP3: Spatially Object-aware 3D Diffusion Policy via Geometry-aligned Attentional Conditioning","date":"2026-09-10","year":2026,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Lifts open-vocabulary object masks from RGB into target, saliency and background fields that condition a 3D diffusion policy.","institution":"Dalian University of Technology","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2609.13318","project":"https://github.com/zhangzhongbo2213/Attention-DP3","code":"https://github.com/zhangzhongbo2213/Attention-DP3"},"policy":{"family":"Diffusion","baseModel":"DP3 backbone with Grounded-SAM-2 perception and tri-field attentional conditioning","modelSize":"Adds 3.8M policy parameters relative to DP3; full count and frozen perception components are separate","inputs":"Synchronized RGB, observed point clouds, proprioception and target text for open-vocabulary segmentation; calibrated 2D-to-3D lifting","outputs":"16 predicted actions; eight executed before replanning","trainingData":"10 demonstrations per Adroit/MetaWorld/SO101 task; 100 per DexArt task","trainingTime":"1,000 MetaWorld epochs; 3,000 epochs elsewhere; wall-clock Not reported","trainingHardware":"One RTX 3090 in reported implementation/evaluation setup","controlRate":"Reported effective executed-step latency 0.198 s; physical execution window eight actions/0.8 s at 10 Hz. This is not raw model query latency.","trainingMethod":"Diffusion imitation learning with frozen object perception and learned geometry-aligned attention fields","evaluation":"Adroit, DexArt, MetaWorld and physical SO101; clutter/OOD distractor studies"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"The bundled 3D-Diffusion-Policy implementation has an MIT LICENSE. A separate top-level license for all additions is Not verified; Grounded-SAM-2 and other components retain upstream terms.","notes":"Availability concerns author-linked artifacts; no training or installation reproduced."},"evidence":[{"url":"https://arxiv.org/abs/2609.13318","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/zhangzhongbo2213/Attention-DP3","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://github.com/zhangzhongbo2213/Attention-DP3","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."},{"url":"https://github.com/zhangzhongbo2213/Attention-DP3/blob/main/3D-Diffusion-Policy/LICENSE","label":"Bundled policy implementation license","fields":["release.license"]}],"caveats":["The perception branch contributes substantial end-to-end latency; the 0.001 s incremental policy overhead excludes that branch.","Text identifies target objects; this is not a general language foundation policy."],"verified":"2026-10-17"},{"id":"map-wam","name":"MaP-WAM","title":"Memory as Plans: World-Action Modeling with Memory-Grounded Planning","date":"2026-09-10","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Sparse segment memories generate language and visual plans that guide a world-action-progress executor.","institution":"Harbin Institute of Technology / Nanyang Technological University / Shandong University","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.11561","project":"https://sizhezhao.github.io/projects/MaP-WAM/","code":"https://github.com/aipixel/MaP-WAM"},"policy":{"family":"World-action model","baseModel":"Memory-grounded hierarchical world-action planning. Qwen3.5-4B language planner; Wan2.2-5B causal visual planner and WAP video expert, plus action/progress experts.","modelSize":"WAP action expert 1.02B and progress expert 207M; 4B VLM and 5B video components. Whole-system count not reported.","inputs":"Global language instruction, current RGB/proprioception and sparse visual/language memory of completed segments; eight frames summarize each completed segment.","outputs":"Next language/visual plan and 32-step robot action/progress chunks, executing eight actions before the next call.","trainingData":"RMBench with supplied segment annotations and physical task demonstrations; exact combined training trajectory count not reported.","trainingTime":"VLM eight epochs; causal world model 100; WAP 40–100 by task. Wall-clock duration not reported.","trainingHardware":"8 NVIDIA A800 80 GB GPUs.","controlRate":"Ten flow-matching steps; eight executed actions per 32-step prediction. Complete deployed Hz not reported.","trainingMethod":"LoRA language-planner adaptation plus causal visual/world-action-progress training; episodic memories update without deployment-time policy retraining.","evaluation":"RMBench memory manipulation and real tasks, 50 physical trials per task."},"release":{"code":"Not released","weights":"Not released","data":"Not verified","license":"No license found in README-only repository","notes":"Official repository contains a README and unchecked release plan; implementation and checkpoints are marked coming soon."},"evidence":[{"url":"https://arxiv.org/abs/2609.11561","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://sizhezhao.github.io/projects/MaP-WAM/","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://github.com/aipixel/MaP-WAM","label":"Official code","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."}],"caveats":["Segment boundaries are provided by benchmark annotations during training; automatic unsegmented skill discovery is future work.","Reported latency excludes prompt/VAE encoding, loading and transfers and is not an end-to-end control measurement."],"verified":"2026-10-17"},{"id":"mopa","name":"MoPA","title":"MoPA: Coordinated Mobile Manipulation via Subsystem-Specific Perception Alignment","date":"2026-09-10","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Separates manipulation and mobility perception queries while coupling the generated whole-body actions through a structured MoT decoder.","institution":"Tsinghua University / Xspark AI / HKUST(GZ)","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2609.12081","project":"https://mopa-policy.github.io","code":"https://github.com/XPolicyLab/XPolicyLab/tree/main/policy/MoPA"},"policy":{"family":"VLA","baseModel":"Qwen3-VL context encoder; subsystem query banks and flow-matching MoT action experts","modelSize":"Backbone/expert total not reported","inputs":"Head/wrist RGB, language and proprioception","outputs":"ManiSkill-HAB: 4-step × 11D whole-body chunks; first2 actions executed before replanning","trainingData":"ManiSkill-HAB1,000 successful demos/skill; separate real mobile-manipulation demonstrations","trainingTime":"100k optimization steps, batch 64 for simulation; wall-clock not reported","trainingHardware":"Not reported","controlRate":"Simulation executes 2 actions at 20 Hz before replanning; four Euler sampling steps","trainingMethod":"Subsystem-specific perceptual alignment and coordinated conditional flow matching for mobility/manipulation","evaluation":"ManiSkill-HAB three suites and four real whole-body mobile-manipulation tasks"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Apache-2.0 for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2609.12081","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/XPolicyLab/XPolicyLab/tree/main/policy/MoPA","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."},{"url":"https://github.com/XPolicyLab/XPolicyLab/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["The 11D/H4 recipe is simulation-specific; do not transfer it to the XPolicyLab mobile-robot adapter without checking its action schema."],"verified":"2026-10-19"},{"id":"manipulation-icl-survey","name":"Demonstration-driven Manipulation ICL","title":"In-Context Learning from Demonstrations for Robotic Manipulation: A Survey","date":"2026-09-09","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"survey","summary":"A targeted review comparing what demonstrations supply, what methods infer and what manipulation-transfer experiments establish.","institution":"Not reported in inspected manuscript page","venue":"Preprint · Survey","modalities":[],"environments":["Literature review"],"embodiments":[],"links":{"paper":"https://www.preprints.org/manuscript/202609.0780"},"survey":{"scope":"Demonstration-driven in-context learning for robotic manipulation.","taxonomy":["Context information","Inference target","Adaptation mechanism","Evaluated transfer"],"adaptationRegimes":["Fixed-parameter contextual inference","Geometric correspondence","Retrieval and memory","Parameter adaptation as a boundary case"],"contextSources":["Human video","Robot sensorimotor trajectories","Geometry","Text examples","Outcome history"],"evaluationAxes":["Task versus execution versus dynamics inference","Same-checkpoint context interventions","Transfer scope","Preparation and sensing budgets"],"corpus":"Literature cutoff 2026-09-06. Tables 2–3 compare 21 representative methods; an inventory attachment is described by the author.","reviewMethod":"Targeted narrative review using arXiv searches and citation tracing, with core, adjacent and background distinctions.","coverageLimits":"Demonstration count is not a common information budget. Unchanged replay is treated as adjacent under this review's narrower working definition."},"release":{"code":"Not applicable — survey","weights":"Not applicable — survey","data":"Author describes literature_inventory.csv; direct attachment not independently verified","license":"CC BY 4.0 for the manuscript as stated by publisher; cited methods retain their own terms","notes":"Preprints.org v1 is a preprint; peer-reviewed publication is not claimed."},"evidence":[{"url":"https://www.preprints.org/manuscript/202609.0780","label":"Primary manuscript v1","fields":["title","date","survey","release"],"note":"Posted September 9, not submission September 8. Sections 1.2–2.3 describe selection, scope and evidence budgets."}],"caveats":["A distinct review from Huang et al.'s arXiv:2609.36012.","An official concept figure is visible on the manuscript page, but a local source copy has not been verified."],"relatedIds":["robot-icl-survey","icrt","instant-policy","keypoint-action-tokens","ricl"],"verified":"2026-10-17"},{"id":"gta2","name":"GTA-2","title":"GTA-2: A Multi-VLM Framework for Synthesizing Robot Manipulation Skills via Grounded Task Axes","date":"2026-09-09","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Four VLM agents turn manipulation instructions into grounded task axes, parameterized skills and executable compositions of robot controllers.","institution":"Carnegie Mellon University","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.09808","project":"https://gta2-project.github.io/","code":"https://github.com/gta2-project/GTA-2"},"release":{"code":"Available — reference ROS2 hardware integration","weights":"Not applicable — external hosted VLM","data":"Not verified","license":"PolyForm Noncommercial License 1.0.0 for the repository; external model/tool licenses remain separate.","notes":"Actual task-decomposition, skill-generation, parameter-setting and vision agents plus UR5e/Robotiq/ZED integration are public. The image-only tutorial is not a physics simulator; deployment requires model API access and the hardware stack."},"evidence":[{"url":"https://arxiv.org/html/2609.09808","label":"Original full paper, methods and evaluation","fields":["title","date","institution","summary","modalities","policy","model","benchmark","benchmark.dataCollection","caveats"],"note":"Publication date is arXiv v1; implementation and deployment observations are distinguished from training supervision, generated geometry and base-model identity."},{"url":"https://github.com/gta2-project/GTA-2","label":"Official artifact: actual source files, inventory and license","fields":["links","release"],"note":"232 actual files including four agent implementations and ROS2 integrations; actual root LICENSE is PolyForm Noncommercial 1.0.0, not MIT/Apache."}],"caveats":["Four cooperating VLM agents do not imply online model-weight training; the target-task protocol uses no policy fine-tuning or demonstrations.","Force-controller output commands do not establish measured force/torque or skin-tactile observation input.","One-Success human refinement and zero-shot evaluation have distinct protocols."],"verified":"2026-10-21","policy":{"family":"Robot agent","baseModel":"Gemini 3.1 Pro across task-decomposer, skill-generator, parameter-setter and vision agents.","modelSize":"Not reported","inputs":"Task and current RGB-D; calibrated ZED2i stereo depth lifts semantic keypoints/axes into metric 3D, alongside robot state.","outputs":"Priority-composed position, orientation, force and gripper controllers with grounded task axes and executable parameters.","trainingMethod":"Frozen VLM reasoning and controller synthesis; targeted human correction is an additional evaluation setting, not policy fine-tuning.","trainingData":"No target-task robot demonstration or policy-weight training in the zero-shot setting.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","evaluation":"UR5e with Robotiq2F85 and fixed ZED2i:14 real tasks,73.9% zero-shot mean success;90.7% after targeted One-Success human refinement."}},{"id":"show-harness","name":"Show-Harness","title":"Show-Harness: Just a VLM Agent Can Play Robots","date":"2026-09-09","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A shared semantic action interface connects VLM perception and reasoning to robot motion across embodiments, with frozen frontier-model and adapted compact-model variants.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.10522","project":"https://showlab.github.io/Show-Harness","data":"https://huggingface.co/datasets/showlab/Show-Harness-Data","code":"https://github.com/showlab/Show-Harness","weights":"https://huggingface.co/showlab/Show-Harness-VLMs"},"policy":{"family":"Robot agent","baseModel":"Gemini 3.1 Pro is the default hosted VLM; a separate compact 2B open-model adaptation is also evaluated.","modelSize":"2B for the compact adapted variant; hosted frontier-model size is not disclosed.","inputs":"Task, RGB camera views, robot state, short action history and perceive–reason–act plugins; sensor hardware differs across real setups.","outputs":"Discrete semantic translation, rotation and gripper commands, interpreted by the local robot-control interface.","trainingData":"Frontier variant: no fine-tuning. Compact variant: collected semantic-control demonstrations, released separately through Show-Harness-Data.","trainingTime":"The compact-model adaptation is described as a few GPU-hours; no exact duration is asserted here.","trainingHardware":"RTX 5090 is reported for local compact-model inference; this must not be treated as verified training hardware.","controlRate":"Not reported","trainingMethod":"Two distinct variants: frozen-model zero-shot control and lightweight supervised adaptation of a small VLM.","evaluation":"Simulated and real manipulation with Franka FR3 and AgileX bimanual setups; cross-scene / cross-embodiment studies."},"release":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 code; released adapters/data have separate model-card terms","notes":"Official repository includes core execution, interpreters, plugins and training. Model and dataset inventories were verified separately."},"evidence":[{"url":"https://arxiv.org/abs/2609.10522","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/showlab/Show-Harness","label":"Official implementation, README and file/license inspection on 19 October 2026","fields":["links","release"],"note":"Official repository includes core execution, interpreters, plugins and training. Model and dataset inventories were verified separately. Apache-2.0 code; released adapters/data have separate model-card terms."},{"url":"https://huggingface.co/showlab/Show-Harness-VLMs","label":"Official released artifact inventory and model/dataset card","fields":["release.weights","links"],"note":"File inventory and artifact-specific terms checked independently; no upstream artifact is counted as this work’s trained release."},{"url":"https://huggingface.co/datasets/showlab/Show-Harness-Data","label":"Official released artifact inventory and model/dataset card","fields":["release.data","links"],"note":"File inventory and artifact-specific terms checked independently; no upstream artifact is counted as this work’s trained release."}],"caveats":["Zero-shot frontier-model results and the trained 2B model are different training conditions.","Depth-capable camera hardware alone does not establish a metric-depth input to every VLM configuration."],"verified":"2026-10-19"},{"id":"bideta","name":"BIDETA / BIFTA","title":"BIDETA: Brain-Inspired Data-Efficient Tactile Adaptation for Unseen Sensors","date":"2026-09-08","year":2026,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Adapts frozen tactile representations to unseen sensors through support memory, feature graphs and reliability-gated recurrence.","institution":"Beijing Institute of Technology; Imperial College London","venue":"Preprint","modalities":["Tactile"],"environments":["Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2609.08673"},"model":{"role":"Reusable representation or perception model; downstream controllers are separate systems","family":"Tactile encoder","baseModel":"Frozen Sparsh or DINOv2 features with dual-view statistical memory, a support-conditioned spectral graph and recurrent inference","modelSize":"Depends on the frozen encoder; added adaptation parameter count not reported","inputs":"Unseen-sensor tactile queries plus a small labeled target support set and pretrained source features","outputs":"Adapted class probabilities and closed-set identity rankings","trainingMethod":"Support-conditioned statistical and graph adaptation while the tactile backbone stays frozen; label-budget protocols and inner-validation tuning","trainingData":"SITR, TacVerse Shape and TacQuad unseen-sensor splits. Required support labels are separate from the query samples.","trainingTime":"Not reported","trainingHardware":"NVIDIA H800 80GB for reported experiments","inference":"Not reported","evaluation":"Unseen-sensor classification and closed-set identity ranking under controlled label budgets, pretrained backbones and random seeds."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Paper CC BY-SA 4.0; official code/model release not verified.","notes":"Code, pretrained encoders and downstream task data are separate artifacts; no policy checkpoint is implied by an encoder release."},"evidence":[{"url":"https://arxiv.org/abs/2609.08673","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Method, experiments and implementation sections; specifications retain component and evaluation scope."}],"caveats":["Version 1 was BIFTA: Brain-Inspired Few-Shot Tactile Adaptation for Unknown Sensors. Version 2 (2026-09-23) renames and revises the work as BIDETA; the first-public date stays 2026-09-08.","Target support labels enable adaptation; this is not zero-shot policy transfer or an actuator controller."],"relatedIds":["sparsh","sitr"],"verified":"2026-10-17"},{"id":"ici-vla","name":"ICI-VLA","title":"ICI-VLA: In-Context Imitation with Spatiotemporally Aligned Demonstrations for Vision-Language-Action Models","date":"2026-09-07","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Retrieves phase-aligned micro-demonstrations to condition a text-action VLM, using offline alignment and retrieval training while keeping evaluation parameters fixed.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.07581"},"policy":{"family":"VLA","baseModel":"Qwen3-VL-4B policy with numerical text actions; Qwen3-VL-Embedding-2B retriever. No added action-specific head.","modelSize":"4B policy backbone and 2B retriever, reported as separate components.","inputs":"Current observations / task state and three retrieved fixed-horizon micro-demonstrations aligned by semantic filtering and dynamic time warping.","outputs":"Numerical text strings describing 25-step robot-action chunks.","trainingData":"Approximately 11,200 long-horizon LIBERO, RoboTwin 2.0 and physical trajectories, including about 1,000 ALOHA teleoperated trajectories.","trainingTime":"Not reported","trainingHardware":"Server with eight NVIDIA A100 80 GB GPUs.","controlRate":"Not reported","trainingMethod":"Offline full-parameter policy fine-tuning, contrastive retriever training and target-action masking. Five retrieval-mining cycles are used; evaluation refreshes context at confidence below 0.65 without weight updates.","evaluation":"LIBERO and RoboTwin 2.0; four physical ALOHA manipulation tasks, with ten varied rollouts per simulated task."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2609.07581","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["Some baseline table entries follow their original papers' protocols; the paper identifies the table as a benchmark-level comparison rather than a fully matched study.","VLA-0 motivates the architecture, but the actual reported backbone is Qwen3-VL-4B."],"verified":"2026-10-17"},{"id":"openwam","name":"OpenWAM / OpenWAM-α","title":"OpenWAM: An Open, Modular Exploration Towards Systematic World-Action Model Pretraining","date":"2026-09-07","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Modular WAM infrastructure and controlled architecture study produce a released video-action policy pretrained on curated heterogeneous interaction data.","institution":"OpenWAM contributors","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.07398","project":"https://openwam-official.github.io/","code":"https://github.com/OpenWAM-Official/OpenWAM","weights":"https://huggingface.co/OpenWAM"},"policy":{"family":"World-action model","baseModel":"OpenWAM-α: Wan2.2-TI2V-5B plus ActionDiT with 30 bridge layers. Infrastructure also supports other interchangeable backbones.","modelSize":"α uses a 5B video backbone plus action expert; full aggregate not reported here. Infrastructure options of 1.3B–14B are separate models.","inputs":"RGB observations, language and proprioception; masked common 80D state/action interface.","outputs":"Native-embodiment action chunks jointly denoised with future visual latents.","trainingData":"518.5M curated frames, approximately 6,369 hours, from egocentric, AgiBotWorld, RoboCOIN, DROID and InternData-A1. Raw pool: 14,348 hours.","trainingTime":"Not reported in inspected source; pretraining and downstream fine-tuning are separate stages.","trainingHardware":"α training hardware not reported here; inference timing on one RTX 5090.","controlRate":"Approximately 170 ms per chunk with ten joint denoising steps. Reported benchmark execution pauses when the action buffer empties until a new prediction arrives.","trainingMethod":"Independent training noise times and synchronized joint inference; unavailable human-video action labels are masked; native action mapping.","evaluation":"LIBERO, LIBERO-Plus, VLABench, RoboTwin 2.0, RoboDojo, RoboCasa365, RoboCasa-GR1 and EBench; physical validation."},"release":{"code":"Available","weights":"Available","data":"Partial","license":"Apache-2.0 code; weights/data/backbone terms separate","notes":"Availability concerns author-linked artifacts; no training or installation reproduced."},"evidence":[{"url":"https://arxiv.org/abs/2609.07398","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/OpenWAM-Official/OpenWAM","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://openwam-official.github.io/","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."}],"caveats":["OpenWAM-Infra design options and the final OpenWAM-α policy have different scopes.","Benchmark support does not imply identical observations or action spaces."],"verified":"2026-10-17"},{"id":"wm-craftnet","name":"WM-Craftnet","title":"WM-Craftnet: World Synesthesia Model for Generalizable and Robust Dexterous In-Hand Manipulation","date":"2026-09-07","year":2026,"collections":["policy"],"categories":["tactile-il","rgbd-3d"],"resourceType":"policy","summary":"Uses a recurrent multimodal world-state estimator to supply a PPO controller for robust in-hand rotation.","institution":"Sharpa","venue":"CoRL 2026 Spotlight","modalities":["Depth","Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2609.07002","project":"https://wmcraftnet.github.io/","code":"https://github.com/sharpa-robotics/WM-Craftnet","weights":"https://huggingface.co/SharpaIT/WM-Craftnet"},"policy":{"family":"RL","baseModel":"Dreamer-style RSSM World Synesthesia Model + PPO MLP","modelSize":"Not reported","inputs":"Wrist depth, tactile signals, proprioception and observation history","outputs":"Relative targets for all 22 hand joints","trainingData":"Simulation RL; nine-object z-axis pretraining and downstream 49-object transfer","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Policy/depth/tactile 10 Hz; physics 60 Hz","trainingMethod":"Multimodal state estimation with auxiliary depth reconstruction, PPO control and domain randomization","evaluation":"Sim-to-real Sharpa Wave multi-axis object rotation"},"release":{"code":"Available","weights":"Available","data":"Not applicable","license":"Apache-2.0 for repository","notes":"Training, deployment and checkpoint paths are public."},"evidence":[{"url":"https://arxiv.org/abs/2609.07002","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/sharpa-robotics/WM-Craftnet","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2609.07002","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["The world model is a state estimator; PPO does not optimize through imagined rollouts."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Not applicable","license":"Apache-2.0 for repository","notes":"Training, deployment and checkpoint paths are public."}},"relatedIds":[]},{"id":"contextflow","name":"ContextFlow","title":"ContextFlow: In-Context Flow Matching for Robot Manipulation","date":"2026-09-06","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A flow-matching policy compresses multimodal demonstrations into context tokens for single-arm and bimanual imitation without task-specific fine-tuning.","institution":"KAUST","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.06852v1","project":"https://dingjiansw101.github.io/contextflow-page/","code":"https://github.com/dingjiansw101/ContextFlow","data":"https://huggingface.co/datasets/vo2yager/aloha_incontext","weights":"https://drive.google.com/drive/folders/1Bf5j90lifJ9kPy2YSQG1bp5FKWZwzTES"},"policy":{"family":"VLA","baseModel":"π0-initialized SigLIP So400m/14 and Gemma-style action expert; randomly initialized context expert and Perceiver-style compressors.","modelSize":"Not reported","inputs":"RGB observations, proprioception, optional task text and in-context demonstration image/state/action sequences.","outputs":"Continuous 50-step action horizon generated by conditional flow matching.","trainingData":"Seen LIBERO task configurations and real single-arm / ALOHA data; four extra bimanual training tasks contribute 254 demonstrations.","trainingTime":"Table S6 reports 40 A100 GPU-hours for ContextFlow.","trainingHardware":"NVIDIA A100; compute reported as GPU-hours.","controlRate":"Table S6: 130.6 Hz action throughput on a single A100 under the OpenVLA-OFT accounting convention, not a robot servo rate.","trainingMethod":"Flow matching with compressed demonstration context; full context/vision training and rank-32 LoRA on the action expert.","evaluation":"Held-out LIBERO configurations and real single-arm / bimanual manipulation, including pen uncapping."},"release":{"code":"Available","weights":"Available","data":"Partial","license":"Apache-2.0 for repository code; external model, dataset and dependency terms remain separate.","notes":"Official training/evaluation code, ContextFlow_Data checkpoint and ALOHA in-context dataset are linked."},"evidence":[{"url":"https://arxiv.org/abs/2609.06852v1","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://dingjiansw101.github.io/contextflow-page/","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/dingjiansw101/ContextFlow","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."},{"url":"https://github.com/dingjiansw101/ContextFlow/blob/HEAD/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Actual license file inspected; code license does not automatically license data or weights."}],"caveats":["Uses RGB, not measured depth or tactile inputs.","Reported throughput counts predicted actions and must not be compared directly with chunk-call or low-level control frequency."],"verified":"2026-10-17"},{"id":"cr-vla-force","name":"CR-VLA-Force / CC-VLA","title":"CR-VLA-Force: Learning Control-aware Compliance VLA Model for Robust Contact-rich Robotic Manipulation","date":"2026-09-05","year":2026,"collections":["policy"],"categories":["tactile-foundation","visual-precision-rl"],"resourceType":"policy","summary":"A force-sequence VLA is coupled to adaptive compliance control for contact-rich precision tasks.","institution":"Not reported","venue":"RA-L 2026","modalities":["RGB","Force/Torque","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.05832v1","project":"https://www.huaweicloud.com/product/cloudrobo.html"},"policy":{"family":"VLA","baseModel":"π0 / PaliGemma, flow action expert and four-expert sparse multimodal force/vision-language MoE.","modelSize":"Not reported","inputs":"RGB views, language, proprioception and six-axis wrist force/torque sequences.","outputs":"40-step action chunks including reference pose / force information for adaptive compliance execution.","trainingData":"Contact-rich teleoperation demonstrations; plug insertion is expanded to 100 demonstrations.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Actual experiments: VLA inference once per second; force-history samples every 0.1 s; compliance control at 500 Hz. Overview-diagram rate ranges are not substituted for this setting.","trainingMethod":"Multi-stage visual-semantic / force learning and force-aware shared teleoperation; VLA-guided adaptive compliance control.","evaluation":"Four real UR5e contact-rich tasks, including board wiping and plug insertion, with partial-OOD pose shifts."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/2609.05832v1","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://www.huaweicloud.com/product/cloudrobo.html","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."}],"caveats":["CR-VLA-Force is the title; the manuscript calls its method CC-VLA.","Wrist F/T is not skin tactile. RealSense RGB-D hardware does not establish a runtime depth input.","Diagram lists broad design rates; reported experimental frequencies are slower and explicitly scoped above."],"verified":"2026-10-17"},{"id":"tacpac","name":"TacPAC","title":"TacPAC: Tactile Prediction and Real-Time Action Correction in World-Action Models for Contact-Rich Manipulation","date":"2026-09-04","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Predicts future contact with a WAM, then corrects unexecuted actions from fresh touch using cached action/tactile context.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.05266","project":"https://logosroboticsgroup.github.io/TacPAC/","code":"https://github.com/LogosRoboticsGroup/TacPAC"},"policy":{"family":"World-action model","baseModel":"Wan2.2 TI2V backbone with video/action/tactile experts","modelSize":"Not reported","inputs":"RGB, language, robot state and incoming tactile images","outputs":"48-step joint/gripper chunks plus asynchronous residual corrections to unexecuted suffix","trainingData":"Five real contact-rich tasks; training data size not reported","trainingTime":"Both training stages: 10 epochs; wall-clock not reported","trainingHardware":"8 NVIDIA H100 GPUs","controlRate":"Chunk generation 1.59 Hz; tactile corrector 32.90 Hz (30.4 ms); commands 30 Hz","trainingMethod":"Joint predictive training then cache-conditioned tactile correction training","evaluation":"Five real tasks including chip and fruit transfer"},"release":{"code":"Available","weights":"Not released","data":"Not released","license":"MIT for repository; third-party terms separate","notes":"Model/training/deployment code present; author README says datasets and checkpoints are being prepared."},"evidence":[{"url":"https://arxiv.org/abs/2609.05266","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/LogosRoboticsGroup/TacPAC","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2609.05266","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Corrector rate and chunk generation rate are distinct; cache is fixed within a chunk."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Not released","data":"Not released","license":"MIT for repository; third-party terms separate","notes":"Model/training/deployment code present; author README says datasets and checkpoints are being prepared."}},"relatedIds":[]},{"id":"xr2","name":"XR-2","title":"Scaling Bimanual Household Manipulation from 1,500 hours of Demonstrations to On-Policy Corrections","date":"2026-09-03","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"A bimanual household VLA trains on a large demonstration corpus and improves through online human corrections.","institution":"PrimeBot Research Institute / Peking University","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.03591","data":"https://huggingface.co/datasets/challenge-2026/challenge_data"},"policy":{"family":"VLA","baseModel":"Qwen3-VL-4B-Instruct and a flow-matching action expert in a Mixture-of-Transformer policy.","modelSize":"5B total reported parameters.","inputs":"Synchronized multi-view RGB observations, robot joint/state measurements and language instruction.","outputs":"50×25 whole-body joint action chunk, temporally ensembled for execution.","trainingData":"1,500 hours of bimanual household demonstrations; DAgger interventions provide additional corrective data.","trainingTime":"Each DAgger round uses 2.5 post-training epochs; full pretraining wall-clock duration not reported.","trainingHardware":"Not reported","controlRate":"RTX 4090 asynchronous policy inference at 10 Hz; joint commands at 30 Hz; whole-body controller at 1,000 Hz.","trainingMethod":"Multi-stage flow-matching pretraining followed by streamed human-intervention DAgger corrections.","evaluation":"Real bimanual household tasks and data/correction scaling experiments."},"release":{"code":"Not verified","weights":"Not verified","data":"Available","license":"Not verified","notes":"The paper explicitly releases the 1,500-hour demonstration corpus. Dataset release alone does not establish XR-2 policy weights."},"evidence":[{"url":"https://arxiv.org/abs/2609.03591","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."}],"caveats":["The 1,500-hour quantity is data duration, not training compute.","Policy, command and whole-body servo rates are distinct."],"verified":"2026-10-17"},{"id":"sa-wam","name":"SA-WAM","title":"Spatially Aware World Action Model via Geometric Latent Diffusion","date":"2026-09-02","year":2026,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Encodes measured metric depth as video-tokenizer-compatible frames for joint action, RGB anddepth prediction.","institution":"Inria / ENS / PSL Research University","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.02531","project":"https://jlopetegui98.github.io/projects/sa_wam.html","code":"https://github.com/jlopetegui98/sa-wam"},"policy":{"family":"World-action model","baseModel":"Cosmos-Predict2 DiT; frozen Wan2.1 videoVAE; log-depth latent-frame injection","modelSize":"2B DiT backbone; full tokenizer-inclusive total not reported","inputs":"Multi-viewRGB, metric depth, currentEE/gripper state andlanguage; realOrbbecFemtoMegaRGB-D camera","outputs":"Simulation 32-action chunks, execute 16; real 3semanticEEwaypoints, execute 2; futureRGB/depth latents","trainingData":"RoboCasa50 demos/task across 24 tasks; LIBERO demonstrations; real 20/task across 10 tasks","trainingTime":"RoboCasa45k updates; LIBERO40k; real 2,400; wall-clock not reported","trainingHardware":"Simulation 40 H100(batch 800 RoboCasa/960 LIBERO); real 8 H100(batch 192)","controlRate":"Not reported; real actions are sparse semantic waypoints, not dense servo commands","trainingMethod":"EDMdenoising jointly supervisesaction andfutureRGB-D; no dedicated3Dencoder","evaluation":"RoboCasa, LIBERO, LIBERO-Plus; UR5/RG6 clean andrandomized composite tasks"},"release":{"code":"Not released","weights":"Not released","data":"Not verified","license":"MIT for placeholder repository; future checkpoint/data terms not verified","notes":"Official repository currently containsREADME/LICENSE only and explicitly sayscode/checkpointscomingsoon."},"evidence":[{"url":"https://arxiv.org/abs/2609.02531","label":"Canonical paper and first submission","fields":["date","title","institution"],"note":"First andonly inspectedversionv1dated2026-09-02."},{"url":"https://arxiv.org/html/2609.02531v1","label":"Full paper:SectionsIII,IV-A andV","fields":["policy","modalities","environments","embodiments"],"note":"Simulation denseactionchunks andreal semanticwaypoint evaluation have differentscopes; reportedrealcompletion is partial-credit scoring."},{"url":"https://github.com/jlopetegui98/sa-wam","label":"Official release status","fields":["release","links.code"],"note":"Comingsoonplaceholder isnot runnableimplementation."},{"url":"https://raw.githubusercontent.com/jlopetegui98/sa-wam/main/LICENSE","label":"Repository license","fields":["release.license"],"note":"MITlicense applies to currentrepository only."},{"url":"https://jlopetegui98.github.io/projects/sa_wam.html","label":"Author method diagram","fields":["imageCandidates"],"note":"ActualarchitectureFig2, not task-success chart."}],"caveats":["RealUR5 evaluationuses3–11semantic key steps andpartialcreditscores; do not compare as densecontrolsuccessrate.","Depthis anactualmetricRGB-Dstream; futurepredicteddepth is a separateoutput.","Observedfutureprediction/successcorrelationdoesnot establish failure-detectionguarantees."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not released","weights":"Not released","data":"Not verified","license":"MIT for placeholder repository; future checkpoint/data terms not verified","notes":"Official repository currently containsREADME/LICENSE only and explicitly sayscode/checkpointscomingsoon."}},"relatedIds":[]},{"id":"simplememvla","name":"SimpleMemVLA","title":"SimpleMemVLA: A Simple but Effective Native-Video Memory for Vision-Language-Action Models","date":"2026-09-02","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Feeds timestamped video history through a native-video VLM and routes historical evidence to actions through predicted subtask representations.","institution":"HUST / Tsinghua University / ModelBest","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2609.05533","code":"https://github.com/OpenBMB/SimpleMemVLA","data":"https://huggingface.co/datasets/yinchenghust/rmbench_lerobot","weights":"https://huggingface.co/yinchenghust/simplememvla_rmbench"},"policy":{"family":"VLA","baseModel":"Qwen3.5-4B native-video backbone and flow-matching action head","modelSize":"4B backbone; head-inclusive aggregate not reported","inputs":"Timestamped head-camera video history, current wrist RGB, language and proprioception","outputs":"Current subtask text plus continuous action chunks conditioned on subtask hidden states","trainingData":"RMBench, RoboMME, MIKASA, RoboMemArena, LIBERO and real-robot demonstrations; task datasets/checkpoints publicly linked","trainingTime":"Not reported","trainingHardware":"Full training hardware not reported; latency measurements use one H100","controlRate":"60s history: 0.68s streaming decision latency vs1.02s full recompute on one H100, bf16, batch 1","trainingMethod":"Supervised VLA learning with native video/subtask bottleneck; prefix-prefill cache reuse for streaming inference","evaluation":"Four memory suites, LIBERO and real memory-dependent manipulation; history intervention/latency studies"},"release":{"code":"Available","weights":"Available","data":"Available","license":"MIT","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2609.05533","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/OpenBMB/SimpleMemVLA","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."},{"url":"https://raw.githubusercontent.com/OpenBMB/SimpleMemVLA/main/LICENSE","label":"Repository license file","fields":["release.license"],"note":"Code scope; model, data and third-party component terms can differ."}],"caveats":["Artificial45-minute inputs are a stress test; they are not equivalent to validated45-minute robot task execution."],"verified":"2026-10-17"},{"id":"peg-in-bench","name":"Peg-in-Bench","title":"Peg-in-Bench: A Modular Benchmark for High-Precision Robotic Insertion","date":"2026-09-01","year":2026,"collections":["benchmark"],"categories":["precision-assembly"],"resourceType":"benchmark","summary":"Reconfigurable physical insertion kit with five peg geometries, three nominal clearances and machine-readable scenario generation.","institution":"AIST","venue":"Preprint","modalities":[],"environments":["Real world"],"embodiments":["Embodiment-agnostic"],"links":{"paper":"https://arxiv.org/abs/2609.00906","code":"https://github.com/aistairc/peg-in-bench"},"benchmark":{"simulator":"Not applicable — 3D-printable physical benchmark and scenario generator","simulatorVersion":"Not applicable","taskCount":"Open-ended scenarios from 5 peg shapes × 3 tolerance levels; not a fixed task-count suite","tasks":["Circular peg insertion","Rectangular peg insertion","Hexagonal peg insertion","Triangular peg insertion","L-shaped peg insertion"],"taskListScope":"Complete five peg-geometry families; generated layouts, orientations and assemblies are combinatorial variants.","capabilities":["Spatial generalization","Geometric generalization","Task-compositional generalization","Tolerance-sensitive insertion"],"metrics":"Scenario-conditional insertion/assembly success; exact scoring procedure specified in manuscript.","robots":"Embodiment-agnostic physical kit.","sensors":"Not prescribed; no tactile sensor is required by the benchmark.","dataCollection":"Not applicable — scenario/component generator and evaluation protocol; no fixed robot-demonstration corpus or prescribed collector.","data":"STL components and JSON scenario generator; not a robot demonstration dataset.","protocol":"Hold physical/contact conditions approximately fixed while varying target pose, geometry, nominal tolerance (0.1 / 1 / 3 mm) and assembly structure.","baselines":"No standardized learned-policy comparison established in the source overview."},"release":{"code":"Available","weights":"Not applicable","data":"Available","license":"Not verified","notes":"Scenario-generation software and printable STL assets public; materials/printing tolerance must be measured separately."},"evidence":[{"url":"https://arxiv.org/abs/2609.00906","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://github.com/aistairc/peg-in-bench","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://arxiv.org/abs/2609.00906","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Nominal CAD clearance is not the same as measured manufactured clearance.","No turnkey simulated training environment is claimed."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not applicable","data":"Available","license":"Not verified","notes":"Scenario-generation software and printable STL assets public; materials/printing tolerance must be measured separately."}},"relatedIds":[]},{"id":"flux3action","name":"FLUX 3 Action","title":"FLUX 3 Action: A 7B World Action Model for Robot Control","date":"2026-09","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Open-weight world-action model jointly predicts robot action chunks and visual outcomes with guidance/step-distilled variants.","institution":"Black Forest Labs","venue":"Technical report","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://bfl.ai/models/flux-3-action","project":"https://bfl.ai/models/flux-3-action","code":"https://github.com/black-forest-labs/flux-action","weights":"https://huggingface.co/black-forest-labs/flux-3-action-droid"},"policy":{"family":"World-action model","baseModel":"FLUX3 multimodal video-heavy pretrained backbone with action heads","modelSize":"7B total","inputs":"Camera RGB, robot joint state and instruction","outputs":"32-action/2.13 s joint-position horizon on DROID; general EE50 representation in midtraining","trainingData":"Video/audio pretraining; action midtraining from gaming, human hands, handheld grippers and 14 teleop embodiments; DROID fine-tuning","trainingTime":"DROID recipe20–30k steps; total elapsed time not reported","trainingHardware":"Inference benchmark spansB200/H200/RTX Pro6000/RTX5090; training compute not reported","controlRate":"DROID horizon 32 actions/2.13 s (15 Hz action representation); real-time factor is not query latency","trainingMethod":"Joint video-action flow model; DROID warmup/adaptation; guidance and one-step distillation","evaluation":"RoboLab120, independent Franka30 real attempts over 10 DROID tasks, SO101 adaptation"},"release":{"code":"Available","weights":"Gated — automatic Hugging Face access terms","data":"Not verified","license":"Apache-2.0 (official code); model/data terms not verified","notes":"Official standalone training/inference code and base/DROID/SO101 weights released. The current official artifact inventory is gated (automatic access terms); files are listed but anonymous unrestricted download is not claimed."},"evidence":[{"url":"https://bfl.ai/models/flux-3-action","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://bfl.ai/models/flux-3-action","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://github.com/black-forest-labs/flux-action","label":"Official released code","fields":["policy.controlRate"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://github.com/black-forest-labs/flux-action","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/black-forest-labs/flux-3-action-droid","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["Real evaluation is30 attempts over 10 tasks; strong results do not establish broad precision benchmark coverage.","Throughput speedups use seconds of generated motion per compute second and should not be read as feedback Hz."],"verified":"2026-10-19","releaseByCollection":{"policy":{"code":"Available","weights":"Gated — automatic Hugging Face access terms","data":"Not verified","license":"Apache-2.0 (official code); model/data terms not verified","notes":"Official standalone training/inference code and base/DROID/SO101 weights released. The current official artifact inventory is gated (automatic access terms); files are listed but anonymous unrestricted download is not claimed."}},"relatedIds":[]},{"id":"knowin-glow","name":"GLOW","title":"GLOW: A Generative Learning Framework for General-Purpose Embodied Intelligence","date":"2026-09","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","generalist-foundation"],"resourceType":"policy","summary":"An author-reported embodied system combines multimodal autoregressive actions, physical-experience generation and contextual robot execution.","institution":"Knowin","venue":"Official technical report","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"project":"https://knowinai.com/tech.html"},"policy":{"family":"Hybrid","baseModel":"KnowinGLOW unified autoregressive BrainSkill/ActSkill model, KnowinAgent harness, KnowinDream experience generator and KnowinWorld action-conditioned rollouts.","modelSize":"Not reported","inputs":"Robot images/video, language, proprioceptive state, execution history and demonstrations. Local simulated geometry interfaces additionally use RGB-D observations.","outputs":"End-effector actions and gripper controls through a robot motion interface, plus task decisions and memory updates.","trainingData":"Real first-person and synthetic physical interaction experiences; corpus totals not disclosed.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Multimodal sequence pretraining, spatial/action post-training and closed-loop feedback; author demonstrates one-shot task teaching without parameter updates.","evaluation":"Author reports 18 RoboDojo tasks, LIBERO-Pro and real one-shot teaching demonstrations."},"release":{"code":"Not released","weights":"Not released","data":"Not released","license":"Not applicable; proprietary technical report.","notes":"Official September 2026 web report and architecture diagrams; no public executable package or checkpoint established."},"evidence":[{"url":"https://knowinai.com/tech.html","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."}],"caveats":["Author technical report, not an independently reproduced public model.","Observed RGB-D is specific to the documented local simulated interaction interface; core general autoregressive inputs are described more broadly."],"verified":"2026-10-17"},{"id":"motus2","name":"Motus2","title":"Motus2: A Self-Evolving General World Model for Dexterous Manipulation","date":"2026-08-31","year":2026,"collections":["policy"],"categories":["tactile-foundation","generalist-foundation","agent-rsi-icl"],"resourceType":"policy","summary":"Shared policy, simulator and evaluator interfaces support value-guided planning and model-based policy improvement for bimanual dexterous manipulation, with tactile refinement.","institution":"GensPI / Tsinghua University","venue":"Preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2608.30237","project":"https://motus-robotics.github.io/motus2/","code":"https://github.com/shengshu-ai/Motus2"},"policy":{"family":"World-action model","baseModel":"Wan2.2-TI2V-5B initialization with shared video/action/value model; small tactile expert reads cached backbone features.","modelSize":"5B video initialization; complete model count not reported. The tactile expert has 30 layers of width 128.","inputs":"Stereo RGB, language, robot state and working memory; contact-aware variants add fingertip force/deformation observations.","outputs":"48-action chunks at 30 Hz; tactile expert refines eight six-action subchunks from preceding tactile windows.","trainingData":"Approximately 130k raw source hours before filtering. Stereo scaling uses 2k/4k/10k/20k-hour subsets; robot midtraining exceeds 100 hours, supplemented by glove alignment.","trainingTime":"Video pretraining: 500k plus 340k steps; stereo joint pretraining: 450k steps. Wall-clock duration not reported.","trainingHardware":"Full training resources not reported.","controlRate":"Actions 30 Hz; tactile force 90 Hz; six-action refinement windows span 0.2 s. These are not full WAM query frequencies.","trainingMethod":"Action-first modeling, progress-value learning, Best-of-N planning and DiffusionNFT MBRL; video/value frozen during action updates; working memory and tactile refinement.","evaluation":"Bimanual physical platforms with Wuji/Sharpa Wave hands; precision, long-horizon, memory, tactile and MBRL evaluations."},"release":{"code":"Partial","weights":"Not verified","data":"Not verified","license":"Not verified; pretrained backbones and data retain separate terms","notes":"Author repository currently provides project/release overview; a linked Hugging Face organization alone does not prove complete checkpoint availability."},"evidence":[{"url":"https://arxiv.org/abs/2608.30237","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/shengshu-ai/Motus2","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://motus-robotics.github.io/motus2/","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."}],"caveats":["130k hours describes raw source data before curation, not hours necessarily consumed in training.","Stereo cues and auxiliary depth recording do not establish a measured depth stream in the deployed policy.","MBRL updates action parameters while the video backbone and evaluator remain frozen."],"verified":"2026-10-17"},{"id":"sun-kuafu","name":"SUN Programs / Kuafu","title":"SUN: Agentic Robot Policy Learning with Persistent Task Programs","date":"2026-08-31","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Maintains typed task programs whose grounded relations define aligned control objectives, satisfaction tests and learning rewards throughout policy development.","institution":"University of California, Los Angeles; Carnegie Mellon University; Amazon; Robotics and AI Institute","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2608.31167","project":"https://kuafu-robo.github.io/"},"policy":{"family":"Hybrid","baseModel":"GPT-5.5 task-level agent with MPC verification, Stage-BC, bounded residual RL and downstream visual policies including DP3.","modelSize":"Not reported","inputs":"Language, videos / reconstructed scene assets, typed operator library and privileged simulation feedback during development; downstream visual policies receive recorded observations / actions.","outputs":"Persistent SUN programs, privileged simulation controllers, demonstration trajectories and learned visual deployment policies.","trainingData":"Nine multi-stage task settings; physical DP3 transfer uses 1,000 generated trajectories per task.","trainingTime":"Program formation / validation averages 0.172 hours per task; controller learning and data-generation time are separately measured and are not included in that figure.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Agent-driven program repair / reward calibration, MPC verification and learned-controller training precede visual-policy distillation and zero-shot real deployment.","evaluation":"Nine multi-stage simulation tasks and real Franka / Kinova transfer of visual policies."},"release":{"code":"Not released","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The author project page marks the implementation as coming soon or links a placeholder rather than a released implementation."},"evidence":[{"url":"https://arxiv.org/abs/2608.31167","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://kuafu-robo.github.io/","label":"Official project release status","fields":["release.code","release.notes"],"note":"The author project page marks the implementation as coming soon or links a placeholder rather than a released implementation."}],"caveats":["The agent develops a privileged simulation policy; the physical visual policy has a different observation interface.","The current primary title is SUN: Agentic Robot Policy Learning with Persistent Task Programs; the survey cites an earlier title."],"verified":"2026-10-17"},{"id":"wm-manipulation-survey","name":"World Models for Manipulation — Survey","title":"World Models for Robotic Manipulation: A Survey","date":"2026-08-31","year":2026,"collections":["policy"],"categories":["geometry-models","rgbd-3d","generalist-foundation"],"resourceType":"survey","summary":"A manipulation-focused review organizes world models by predicted representation, action interface and learning lifecycle.","institution":"Hong Kong Polytechnic University; Great Bay University; HIT Shenzhen; HKU; KTH and collaborators","venue":"SmartBot · Review article","modalities":[],"environments":["Literature review"],"embodiments":[],"links":{"paper":"https://onlinelibrary.wiley.com/doi/10.1002/smb2.70053"},"release":{"code":"Not applicable — review article; no new controller","weights":"Not applicable — survey","data":"Not applicable — publisher states no new datasets generated or analyzed","license":"Open-access publisher article; exact reuse license not independently verified.","notes":"Publisher full text reviewed. No guessed project repository or executable release."},"evidence":[{"url":"https://onlinelibrary.wiley.com/doi/10.1002/smb2.70053","label":"Publisher full text, metadata and bibliography","fields":["title","date","institution","venue","survey","release"],"note":"Sections1–9; author-defined taxonomy and original publication date. Geometry citations are discovery leads, not evidence for the methods themselves."}],"caveats":["No direct robot policy, control frequency or training-resource claim belongs to this review."],"verified":"2026-10-17","survey":{"scope":"Predictive systems for robotic manipulation, including spatial, contact and physical dynamics.","taxonomy":["Image/video","Latent dynamics","Flow/scene motion","3D/4D geometry","Physics-informed dynamics"],"adaptationRegimes":"Integrated prediction–action, predictive planning, policy augmentation and learned-environment improvement across pretraining, post-training and inference.","contextSources":"World-grounded, intervention-aware predictions; observations and robot actions constrain future scene/contact evolution.","evaluationAxes":["Prediction quality","Task performance","Contact plausibility","Action alignment","Simulator reliability"],"corpus":"Publisher bibliography has 162 references; review describes 38 manipulation datasets. Published online August 31, 2026.","reviewMethod":"Functional taxonomy linking representations, predictive cores, action interfaces, infrastructure roles and lifecycle stages.","coverageLimits":"Pure perception, inverse models, policies without prediction and standalone utility functions are outside its operational world-model definition. Dataset references are not independent policy results."}},{"id":"zeva","name":"Zeva","title":"Zeva: In-Context Causal Learning for Generalizable Embodied Manipulation","date":"2026-08-31","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Uses a learned causal transition encoder and two-timescale interaction memory to condition a frozen robot policy on observed action effects.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2608.30880","project":"https://air-embodied-brain.github.io/Zeva","code":"https://github.com/air-embodied-brain/Zeva","weights":"https://huggingface.co/chen123fu/zeva-robocasa"},"policy":{"family":"Hybrid","baseModel":"Causal Transition Encoder with recurrent transition modeling; frozen embodied policy conditioned on causal interaction / phase tokens.","modelSize":"Not reported","inputs":"Visual changes, executed controls, current robot state and short / longer-term interaction memory.","outputs":"Policy actions conditioned on learned causal transition tokens and retrieved interaction evidence.","trainingData":"Offline transition-encoder training and episode experience; deployment adaptation updates context rather than the main policy weights.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"A learned causal encoder maps action–visual transitions into compact tokens; memory changes at deployment without policy fine-tuning.","evaluation":"RoboCasa365 and real ChemLab-Evo, with randomized episodes and repeated-attempt fixed-initialization studies."},"release":{"code":"Partial — RoboCasa overlay and case-study implementation","weights":"Partial — RoboCasa checkpoint and memory adapters","data":"Not verified","license":"OpenMDW-1.1 in actual code LICENSE and released RoboCasa model LICENSE; external Cosmos/GR00T models and datasets retain separate terms.","notes":"Official code is an overlay for Cosmos-RL with a locked five-task RoboCasa reproduction protocol and cross-attempt case studies. The public zeva-robocasa inventory contains stage1/stage2 weights, PIM adapter and stage3 case-study checkpoints. The generic chen123fu/zeva endpoint returned 401, so availability is scoped to the independently verified RoboCasa release."},"evidence":[{"url":"https://arxiv.org/abs/2608.30880","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/air-embodied-brain/Zeva","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/chen123fu/zeva-robocasa","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["An episode is one initialized task instance; multiple attempts can restore the same initialization. These are different evaluation units.","The transition encoder is trained offline even though the deployment policy is described as frozen."],"verified":"2026-10-19"},{"id":"agm","name":"Achievement-Grounded Memory (AGM)","title":"AGM: Achievement-Grounded Memory for Closed-Loop Agents with Frozen VLA Policies","date":"2026-08-30","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Advances a task-memory pointer only after verifying physical achievement, preventing attempted actions from being stored as completed progress.","institution":"Harbin Institute of Technology; University of Sydney; Wuhan University","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2608.29537"},"policy":{"family":"Hybrid","baseModel":"Frozen subgoal-conditioned π0 / π0.5, frozen point tracker and SigLIP-base evidence encoder, plus a learned placement-verification head.","modelSize":"2.43M trainable verification-head parameters plus 228.6M frozen external parameters; the VLA backbone is additional.","inputs":"Front / wrist RGB, robot state and gripper-load cues, typed subgoal sequence and pre / post execution evidence.","outputs":"Continue / retry / advance decisions and subgoal-conditioned VLA action chunks.","trainingData":"Verification head learns from 526 placement events (238 target, 288 bin); real policy preparation uses about 2.2 hours of teleoperated data.","trainingTime":"Real-policy preparation uses 40,000 updates at batch size 128; this is separate from verification-head training. Elapsed duration is not reported.","trainingHardware":"Not reported","controlRate":"Real robot control and cameras run at 30 Hz; policy chunk length is twenty frames, replanning every ten frames or at a subgoal transition.","trainingMethod":"The deployed VLA, tracker and visual encoder remain frozen; only the small verification head is trained for the main AGM mechanism. Base-policy adaptation occurs separately.","evaluation":"RoboMME counting / repeated manipulation and real repetition / bin-filling tasks, with memory, grasp and placement verification ablations."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The original paper is public; code, model and dataset release scopes are verified separately where established."},"evidence":[{"url":"https://arxiv.org/abs/2608.29537","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."}],"caveats":["Attempt-based and achievement-based memory are compared with the same executor; repetition counts and ordinary final success are distinct metrics.","Only the verification head is trained in the main memory method, which does not erase the prior training of the underlying policies."],"verified":"2026-10-17"},{"id":"correctvla","name":"CorrectVLA","title":"Training-Free Action Correction for VLA Model Failures via Language Feedback","date":"2026-08-30","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Converts one task-level language correction into temporally scoped additive action adjustments while preserving the underlying VLA weights.","institution":"Carnegie Mellon University","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2608.29967"},"policy":{"family":"Hybrid","baseModel":"Frozen OpenVLA-OFT / π0.5 policies; Claude Sonnet 4.5 parses the reported human correction format.","modelSize":"Not reported","inputs":"Task-level human feedback, a VLA action stream and its temporal window; the base policy retains its RGB / robot-state interface.","outputs":"Additive magnitude / direction corrections to selected action dimensions during a specified interval.","trainingData":"No correction-module weight training. Underlying VLA checkpoints are separately fine-tuned before shifted-setting evaluation.","trainingTime":"Not reported","trainingHardware":"Experiments use an RTX 4090 workstation; isolated correction training does not occur.","controlRate":"Not reported","trainingMethod":"Frozen policy weights; language-to-parameter extraction and grounded action adjustment are reused across rollouts without per-episode human intervention.","evaluation":"LIBERO-90 execution-misalignment failures and a real UFactory xArm7 under environment shift."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The original paper is public; code, model and dataset release scopes are verified separately where established. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms."},"evidence":[{"url":"https://arxiv.org/abs/2608.29967","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."}],"caveats":["A single human correction is provided per task and reused across rollouts; the method is not correction-free zero-shot deployment.","The method targets execution misalignment and does not claim to repair every semantic / perception failure."],"verified":"2026-10-17"},{"id":"n0-foundation","name":"N₀-Foundation / NeoForce","title":"𝒩₀-Foundation: Towards the Age of Tactile Intelligence","date":"2026-08-30","year":2026,"collections":["benchmark","policy"],"categories":["tactile-representation","tactile-benchmarks","precision-assembly"],"resourceType":"model","summary":"A force-grounded visual–tactile encoder, a large interaction corpus and paired physical/simulated evaluation suites share a contact representation.","institution":"NeoteAI; Fudan University TEAI","venue":"Preprint · N₀-Foundation report","modalities":["RGB","Tactile"],"environments":["Real world","Simulation"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2608.29601","project":"https://research.neoteai.com/n0-foundation/","code":"https://github.com/neoteai/N0-Foundation","data":"https://huggingface.co/datasets/NeoteAIEmbodied/OpenNeoData","weights":"https://huggingface.co/NeoteAI/NeoForce"},"release":{"code":"Available","weights":"Available — NeoForce and tactile converter checkpoints","data":"Gated — OpenNeoData requires Hugging Face access terms","license":"NeoForce and NeoSim code subdirectories MIT; root documentation and OpenNeoData CC BY-NC-SA 4.0. NeoForce checkpoint model card specifically declares MIT; other artifacts retain their individual terms.","notes":"Code and checkpoint files were inspected. The complete 30,000-hour NeoData corpus is distinct from the 5,000-hour OpenNeoData subset; hardware access and live training were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2608.29601","label":"Original report and arXiv history","fields":["title","date","institution","summary"],"note":"First arXiv manuscript 30 August; revised 20 September 2026."},{"url":"https://arxiv.org/html/2608.29601v2#S5","label":"NeoForce method and Appendix D","fields":["model","modalities"],"note":"Encoder training subset, converter, supervision and downstream use are distinct."},{"url":"https://arxiv.org/html/2608.29601v2#S6","label":"NeoReal / NeoSim with Appendices E–F","fields":["benchmark","environments","embodiments"],"note":"Full paper task names, protocols, hardware and evaluated baseline subsets."},{"url":"https://github.com/neoteai/N0-Foundation/tree/main/neoforce","label":"Released NeoForce implementation","fields":["model.baseModel","model.inputs","model.outputs","model.inference","release.code"]},{"url":"https://github.com/neoteai/N0-Foundation/blob/main/neosim/docs/EnvironmentLock.md","label":"Released NeoSim environment lock","fields":["benchmark.simulator","benchmark.simulatorVersion","release.code"]},{"url":"https://huggingface.co/NeoteAI/NeoForce","label":"NeoForce model release","fields":["release.weights"],"note":"Public API lists neoforce.pt and the tactile converter checkpoint."},{"url":"https://huggingface.co/datasets/NeoteAIEmbodied/OpenNeoData","label":"OpenNeoData card and access terms","fields":["release.data","benchmark.data","release.license"]},{"url":"https://github.com/neoteai/N0-Foundation/blob/main/neoforce/LICENSE","label":"NeoForce MIT license","fields":["release.license"]},{"url":"https://github.com/neoteai/N0-Foundation/blob/main/neosim/LICENSE","label":"NeoSim MIT license","fields":["release.license"]},{"url":"https://github.com/neoteai/N0-Foundation/blob/main/LICENSE","label":"Root documentation license","fields":["release.license"]},{"url":"https://arxiv.org/abs/2608.29601","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"NeoSim appendix describes scripted cuRobo collection; NeoData pretraining capture is not treated as the NeoReal task collector."},{"url":"https://huggingface.co/NeoteAI/NeoForce","label":"Official current source / artifact inventory — 21 October 2026","fields":["release","links","policy"],"note":"Current actual neoforce/neoforce.pt and visuo_tactile_conversion/visuo_tactile_conversion.pt inventory and MIT model card reviewed; gated OpenNeoData and benchmark code terms remain separate."}],"caveats":["NeoForce is a supporting encoder, not a standalone action policy or a new 30,000-hour encoder-training run.","NeoSim gel-depth maps describe local tactile deformation, not an observed external depth camera stream.","The project page is dated July 25; this catalog uses the first arXiv manuscript date and preserves that difference.","The full NeoData corpus, the released OpenNeoData subset and per-benchmark task demonstrations must not be summed."],"verified":"2026-10-21","benchmarkName":"NeoReal / NeoSim","policyName":"NeoForce","benchmarkSummary":"Ten physical NeoReal tasks and twelve NeoSim task settings test contact regulation, assembly, fragile-object handling and bimanual coordination.","policySummary":"NeoForce learns visual–tactile contact features with force reconstruction and masked teacher-latent prediction; downstream policies consume the representation.","model":{"role":"Contact representation for downstream manipulation; the encoder itself does not emit robot actions.","family":"Multimodal representation","baseModel":"DINOv2-initialized ViT-B shares visual/tactile tokens, with temporal aggregation, force/mask reconstruction and an EMA-teacher latent predictor. A separate GoogLeNet-based converter estimates force fields from tactile imagery.","modelSize":"ViT-B backbone; released encoder lists 12 layers and 768-dimensional tokens. Full encoder/converter parameter totals are not reported.","inputs":"Synchronized four-frame RGB and dual-finger force-field chunks. Raw tactile images are converted into estimated three-axis fields; the wrist F/T sensor used for calibration is not an online encoder input. Proprioception belongs to downstream policies/benchmark streams, not this encoder.","outputs":"Contact features, reconstructed dense force fields and contact masks; released code also exposes global force estimates. No action vector.","trainingMethod":"Force/mask reconstruction plus masked latent prediction, cross-modal alignment and contrastive learning; EMA teacher targets.","trainingData":"NeoForce experiment: 20,000 demonstrations across 30 tasks, 2,500 held-out episodes. This subset is distinct from 30,000+ hours of NeoData and the 5,000-hour OpenNeoData release.","trainingTime":"100,000 NeoForce steps; wall-clock time not reported. The separate force converter uses 50 epochs.","trainingHardware":"8 NVIDIA A100 GPUs for NeoForce (Appendix D.4).","inference":"Released interface uses four frames at 30-fps sampling; measured encoder latency and downstream feedback frequency are not reported.","evaluation":"Force reconstruction/segmentation and a controlled π0.5 tactile-conditioning study on NeoReal. Calibration converter and downstream policy results retain separate scopes."},"benchmark":{"simulator":"NeoSim: UniVTAC on NVIDIA Isaac Sim / Isaac Lab with TacEx; NeoReal: physical robots.","simulatorVersion":"Paper does not pin versions. Released NeoSim EnvironmentLock lists Isaac Sim 4.5.0.0, Isaac Lab v2.1.1, TacEx 0.1.0 and pyuipc 0.9.0; scope is the released known-good node, not an assumed paper setting.","taskCount":"NeoReal: 10 physical tasks; NeoSim: 12 simulated task settings (4 single-arm, 8 dual-arm). These are two suites, not 22 independent skill families.","tasks":["NeoReal — Cardboard Box Folding","NeoReal — Bag Packing","NeoReal — Cable Winding","NeoReal — Board Wiping","NeoReal — Cup Stacking","NeoReal — Socket Plugging","NeoReal — Board Insertion","NeoReal — Bottle Standing","NeoReal — Fruit Collection","NeoReal — Towel Folding","NeoSim — Pour Ball","NeoSim — Unplug and Plug Charger","NeoSim — Plug USB","NeoSim — Grasp Chip","NeoSim — Insert Screw","NeoSim — Place Gears","NeoSim — Unstack Bowl","NeoSim — Cup Handover","NeoSim — Stack Cups","NeoSim — Stack Plates","NeoSim — Stack Bowls","NeoSim — Unstack Cup"],"taskListScope":"Complete named paper suites, with suite prefixes. Extra implementation task files are not added to the paper evaluation count.","capabilities":["Force regulation","Tight-clearance insertion","Fragile-object handling","Deformable-object shaping","Sustained surface contact","Bimanual coordination"],"metrics":"Per-task binary success; NeoReal also reports an ordered milestone-progress score.","robots":"NeoReal single-arm Flexiv Rizon 4s; dual-arm Piper or ARX X5. NeoSim has single- and dual-arm setups; exact paper robot variants are not inferred from unrelated dataset embodiments.","sensors":"External/wrist RGB, tactile fingers and robot states. NeoSim records tactile RGB/marker motion/gel depth and derives contact-force fields. Gel deformation depth is not an external scene-depth camera; not every baseline consumes touch.","dataCollection":"NeoSim: scripted cuRobo experts with planner/success filtering. NeoReal task-demo interface is Not reported; separate NeoData pretraining combines robot teleoperation and TacUMI capture.","data":"NeoReal policies: 400,000+ NeoData pretraining episodes then 300 task-specific demonstrations. NeoSim: 100 successful scripted/cuRobo demonstrations per task. OpenNeoData is a separately gated 5,000-hour corpus, not the task-demo total.","protocol":"NeoReal: 20 randomized trials/task and a 5-minute limit, with milestone and failure rules. NeoSim: 100 fixed evaluation seeds/task disjoint from collection, remote policy inference on RTX 4090. Paper real training uses 8 A100; simulated task-policy training uses 4 A100.","baselines":["ACT","Diffusion Policy","π0.5","Xiaomi-Robotics-0","InternVLA-A1","StarVLA-α","Fast-WAM","GigaWorld Policy","LingBot-VA"]},"releaseByCollection":{"benchmark":{"code":"Available — NeoSim collection/evaluation implementation","weights":"Not applicable — benchmark; baseline checkpoint completeness not verified","data":"Gated — OpenNeoData; separate task demonstrations follow their release documentation","license":"NeoSim code MIT, including NOTICE for UniVTAC/TacEx; data and documentation retain separate terms.","notes":"Published evaluation suites and the larger implementation task registry are scoped separately."}},"relatedIds":["n0-vtla","n0-twam","univtac","sparsh"]},{"id":"temporalflow-vla","name":"TemporalFlow-VLA","title":"TemporalFlow-VLA: Learning Physically Grounded Execution History for Long-Horizon Robot Manipulation","date":"2026-08-27","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Training-only robot-surface temporal-flow supervision teaches compact, execution-aware visual history for action prediction.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2608.26821"},"policy":{"family":"VLA","baseModel":"VLM with continuous action expert plus two execution-aligned temporal queries and asynchronous historical-feature caching.","modelSize":"Not reported","inputs":"Current/history RGB camera views, robot state and instruction. Calibrated robot geometry and temporal-flow targets are training-only.","outputs":"Continuous action chunks conditioned on execution history.","trainingData":"LIBERO suites; 12 RoboTwin tasks with 50 clean / 500 randomized demonstrations per task; real experiments use 280 demonstrations per task.","trainingTime":"30k training steps on LIBERO; 60k on RoboTwin and real tasks. Wall-clock duration not reported.","trainingHardware":"Eight NVIDIA H100 GPUs, per-GPU batch size 32.","controlRate":"Not reported","trainingMethod":"Action learning with geometric temporal-flow auxiliary supervision; remove the geometric-supervision path at deployment.","evaluation":"LIBERO, 12 RoboTwin tasks and real multi-stage manipulation."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/2608.26821","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."}],"caveats":["Robot-surface geometry and calibrated-camera labels do not imply a measured runtime Depth input.","No particular VLM parameter count is inferred when the paper does not unambiguously specify it."],"verified":"2026-10-17"},{"id":"vlact","name":"VLAct","title":"Beyond Data Scaling: Representation-Centric Continued Pre-training for Vision-Language-Action Models","date":"2026-08-27","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Continues VLM pretraining with preserved visual-language priors, multiple continuous action heads and a partially unified action layout for reusable robot representations.","institution":"StarVLA Team","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2608.27550","project":"https://starvla.github.io/VLAct/","code":"https://github.com/starVLA/VLAct","weights":"https://huggingface.co/collections/StarVLA/vlact-6a903c2e0c176179da425c96"},"policy":{"family":"VLA","baseModel":"Qwen3-VL-4B with OFT/PI/GR00T continuous-head co-supervision; task-specific head attached for downstream policy","modelSize":"4B VLM backbone; action-head-inclusive totals depend on chosen downstream head","inputs":"RGB views, language and embodiment-specific proprioceptive state","outputs":"Franka7D delta-EEF/gripper or AgileX14D absolute-joint/gripper action chunks; head-dependent decoding","trainingData":"Open robot mixture including OXE/OXE-AugE, DROID, MolmoAct, InternData-A1, RoboCOIN plus caption/grounding/spatial/language data; aggregate size not reported","trainingTime":"Released backbone checkpoint named100k steps; real adaptation50k steps; wall-clock not reported","trainingHardware":"Continued pretraining16 GPUs, accelerator model not reported; real-policy adaptation8 × H800","controlRate":"Not reported","trainingMethod":"VLM-prior preservation with shallow-layer protection/caption mixing; multi-head action co-supervision; partially unified embodiment layout","evaluation":"LIBERO/Plus, VLA-Arena, RoboTwin, RoboCasa-GR1, DOMINO, RoboDojo and real single-/dual-arm transfer"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"MIT code (official repository badge); foundation/model/data terms separate","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2608.27550","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/starVLA/VLAct","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."}],"caveats":["VLAct is a continued-pretrained backbone evaluated with attached executable policies; do not conflate it with the separate VLAC action-critic model.","RoboDojo August24snapshot from the paper differs from the later live board."],"verified":"2026-10-17"},{"id":"tacforcing","name":"TacForcing","title":"TacForcing: Streaming Action Generation with Execution-Time Tactile Feedback","date":"2026-08-26","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Streams short action blocks with fresh execution-time tactile conditioning instead of using a separate reactive controller.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2608.25798","project":"https://88runaway.github.io/tacforcing/"},"policy":{"family":"VLA","baseModel":"π0.5 simulation; GR00T N1.7 real-world backbone","modelSize":"Not reported","inputs":"RGB, language, state and refreshed fingertip tactile deformation maps","outputs":"Streaming action blocks; execution-aware attention only exposes fresh touch to imminent actions","trainingData":"Simulation: 50 demonstration trajectories/task; real world: 100/task except Close Cap150 and Close Lid300","trainingTime":"Simulation: 15,000 update steps; real world: 15 epochs; global batch 256 for both; wall-clock not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Streaming flow-matching action expert and Execution-Aware Tactile Attention","evaluation":"Six UniVTAC tasks; revised manuscript evaluates six real tasks on Sharpa North and Sharpa–RealMan"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."},"evidence":[{"url":"https://arxiv.org/abs/2608.25798","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://88runaway.github.io/tacforcing/","label":"Author project page","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2608.25798","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."},{"url":"https://arxiv.org/html/2608.25798v2","label":"Independent review: AppendixA.1–A.2 training parameters","fields":["policy.trainingData","policy.trainingTime"],"note":"Uses the revised manuscript: simulation and real-world schedules differ; no wall-clock cost or training hardware is inferred."}],"caveats":["Original v1 had three real tasks; catalog uses revised v2 protocol.","Sampling schedule/feedback timing should be compared at block-level, not action-command rate."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."}},"relatedIds":[]},{"id":"zero-wam","name":"Zero-WAM","title":"Zero-WAM: In-Context World-Action Modeling from Human Videos for Open-Ended Task Generalization","date":"2026-08-26","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","generalist-foundation"],"resourceType":"policy","summary":"A causal video-action policy uses a human video prompt to specify manipulation tasks absent from robot post-training.","institution":"Robbyant / HKUST (Guangzhou) / HKUST","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Bimanual"],"links":{"paper":"https://arxiv.org/abs/2608.26103","project":"https://robbyant-research.github.io/Zero-WAM/","code":"https://github.com/robbyant-research/Zero-WAM","data":"https://huggingface.co/datasets/Robbyant-Research/HumanGen","weights":"https://huggingface.co/Robbyant-Research/zero-wam-posttrain-robotwin"},"policy":{"family":"World-action model","baseModel":"Human-video in-context world-action model. Wan2.2-TI2V-5B, separate causal video/action Transformers coupled through MoT, Wan VAE and T5.","modelSize":"5B video backbone; full instantiated model size not reported.","inputs":"Robot RGB observation and state with either human video context or a language instruction.","outputs":"Joint future robot video and executable action chunks; action decoding attends to predicted future robot video.","trainingData":"Task-diverse VA pretraining corpus and HumanGen: 74,200 human-robot pairs covering approximately 8,600 tasks; synthetic semantically aligned human videos supplement robot trajectories.","trainingTime":"15,360 GPU-hours pretraining; RoboTwin post-training 4,000 steps.","trainingHardware":"64 GPUs for RoboTwin post-training; GPU model not reported.","controlRate":"Not reported","trainingMethod":"Video/action flow matching with multi-horizon intermediate future prediction; all parameters fixed during unseen-task inference.","evaluation":"Seven held-out RoboTwin 2.0 tasks and physical insertion, multi-object and long-horizon manipulation."},"release":{"code":"Available","weights":"Available","data":"Partial","license":"Apache-2.0 code; model/data/backbone terms separate","notes":"Code and pre/post-trained checkpoints released. HumanGen release covers Simulation ICL and External pretraining ICL subsets; not the complete in-house corpus."},"evidence":[{"url":"https://arxiv.org/abs/2608.26103","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://robbyant-research.github.io/Zero-WAM/","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://github.com/robbyant-research/Zero-WAM","label":"Official code","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://raw.githubusercontent.com/robbyant-research/Zero-WAM/main/LICENSE.txt","label":"Actual source license file","fields":["release.license"],"note":"Scope limited to the file/repository subtree carrying these terms, not paper figures or external models/data."}],"caveats":["A large part of the human instruction corpus is generated from robot videos; task-level semantic alignment does not imply physical human demonstrations for every pair."],"verified":"2026-10-17"},{"id":"ponderpounce","name":"PonderPounce","title":"PonderPounce: A Pretrained MLLM as an Episode Context Engine for Robot Control","date":"2026-08-25","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"An MLLM maintains episode context and asynchronously supplies compact cognition to a separate fast action policy.","institution":"MAUM.AI / Seoul National University","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2608.24115","project":"https://worv-ai.github.io/ponderpounce/","code":"https://github.com/worv-ai/PonderPounce","weights":"https://huggingface.co/worv-ai/ponderpounce-9b-robomme"},"policy":{"family":"Hybrid","baseModel":"Qwen3.5-9B Ponder context engine; Pounce initialized from π0.5 on RoboMME or GR00T N1.5 on RoboCasa-DC.","modelSize":"9B main context engine with an additional 3.6B π0.5 or 3B GR00T N1.5 action component as labeled by this paper; a 0.8B Ponder ablation is separate.","inputs":"RGB episode history, demonstration context, task and robot state; latest cognition token and its age condition the action model.","outputs":"20-step action chunks at 20 Hz playback; asynchronous cognition updates.","trainingData":"Not reported","trainingTime":"RoboCasa-DC configuration uses 4,000 episode-based steps; wall-clock training duration not reported.","trainingHardware":"Not reported","controlRate":"Optimized per-call p50: 78 ms cognition-only and 25 ms action invocation on H100. Real asynchronous experiment targets 1 Hz per branch; p50 inverse rates are not concurrent throughput.","trainingMethod":"Joint end-to-end context-engine/action-model training without separate bridge pretraining.","evaluation":"RoboMME, RoboCasa-DC and four real-world memory tasks."},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 for repository code; external model, dataset and dependency terms remain separate.","notes":"Official training/serving code and a 9B-Ponder RoboMME checkpoint are public; other benchmark/backbone variants are not assumed to be included in that checkpoint."},"evidence":[{"url":"https://arxiv.org/abs/2608.24115","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://worv-ai.github.io/ponderpounce/","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/worv-ai/PonderPounce","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."},{"url":"https://github.com/worv-ai/PonderPounce/blob/HEAD/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Actual license file inspected; code license does not automatically license data or weights."},{"url":"https://github.com/worv-ai/PonderPounce","label":"Verified official code artifact","fields":["links.code","release.code"],"note":"Author project / repository cross-link verified; current release distinguished from original paper experiments."},{"url":"https://huggingface.co/worv-ai/ponderpounce-9b-robomme","label":"Verified official weights artifact","fields":["links.weights","release.weights"],"note":"Author project / repository cross-link verified; current release distinguished from original paper experiments."}],"caveats":["Inference profiling hardware is not evidence of training hardware.","Separate action backbones and benchmark data scales must not be mixed into one score or model size."],"verified":"2026-10-17"},{"id":"physcap","name":"PhysCaP","title":"PhysCaP: Grounding Code-as-Policy Agent with Physics-Informed Exploration","date":"2026-08-21","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Uses targeted physical interaction to estimate latent properties such as mass and stiffness before synthesizing a robot-control program.","institution":"National Taiwan University; NVIDIA Research; National Yang Ming Chiao Tung University","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2608.21031","project":"https://physcap.github.io/"},"policy":{"family":"Robot agent","baseModel":"Gemini 3.1 Pro Planner / Prioritizer / coding agents; Molmo2 object grounding; CaP-Agent0-style code-policy execution.","modelSize":"Not reported","inputs":"Task, calibrated RGB-D scene observations, robot proprioception and returned mass / stiffness estimates from physical probes.","outputs":"Exploration-tool calls and executable manipulation code, with a Prioritizer ranking plausible information-gathering actions.","trainingData":"No task-specific model training; task-specific probes estimate physical properties from proprioception without extra property sensors.","trainingTime":"Not reported","trainingHardware":"No new-model training is reported; perception / policy inference uses an RTX 3090 24 GB workstation.","controlRate":"Simulation execution time is computed from a 20 Hz step rate; this is not an LLM query rate.","trainingMethod":"Frozen models; physical property extraction and a multi-agent explore / stop decision loop augment code-as-policy control.","evaluation":"Three real tabletop tasks and Identify Empty Can in LIBERO; model-backbone and exploration-efficiency comparisons."},"release":{"code":"Not released","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The author project page marks the implementation as coming soon or links a placeholder rather than a released implementation. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms."},"evidence":[{"url":"https://arxiv.org/abs/2608.21031","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://physcap.github.io/","label":"Official project release status","fields":["release.code","release.notes"],"note":"The author project page marks the implementation as coming soon or links a placeholder rather than a released implementation."}],"caveats":["Mass / stiffness are inferred from robot interaction and proprioception; no dedicated tactile or wrist force/torque sensor is established.","The property levels are task-calibrated reference bins rather than standardized material grades."],"verified":"2026-10-17"},{"id":"q-planning","name":"Q-Planning","title":"Beyond Imitation: Self-Improving Robot Policies via Off-Policy Q-Planning","date":"2026-08-21","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","visual-precision-rl"],"resourceType":"policy","summary":"A small learned Q-function ranks action samples and absorbs failures while the large behavior-cloned policy stays frozen.","institution":"Georgia Institute of Technology","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2608.21204","project":"https://varungiridhar.github.io/qplanning/"},"policy":{"family":"Hybrid","baseModel":"Frozen large visuomotor behavior policy plus parameter-disjoint DINOv2/T5 Q-function and single-step Q-weighted action aggregation.","modelSize":"Not reported","inputs":"Robot visual/state observations, task text and candidate action chunks from the frozen policy.","outputs":"Q-weighted 32-step action chunk, with benchmark-specific prefixes executed before replanning.","trainingData":"Successful demonstrations initialize the Q-function; self-improvement collects 100 episodes per task/iteration in simulation and 20 on hardware. Real tasks start from 100 demonstrations each.","trainingTime":"Q initialization: 12k steps LIBERO, 45k RoboTwin; each improvement iteration adds 200 Q-only gradient steps.","trainingHardware":"Four H200 GPUs for Q-training; L40S used for planning latency profiles.","controlRate":"Action playback: LIBERO 30 Hz with ten steps executed; RoboTwin 25 Hz with 24 steps executed. L40S timing is planning latency, not training cost.","trainingMethod":"Off-policy value learning, value-guided sampling and Q-only online improvement; no BC-actor weight update.","evaluation":"LIBERO, bimanual RoboTwin and two contact-rich real bimanual tasks."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/2608.21204","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://varungiridhar.github.io/qplanning/","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."}],"caveats":["Online self-improvement updates the critic; frozen actor weights do not imply an entirely training-free method.","Shorter evaluation episode budgets differ from some base-policy reports."],"verified":"2026-10-17"},{"id":"eximo","name":"EXIMO","title":"EXIMO: VLM Guided Exploration of VLA Policies","date":"2026-08-20","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Improves a VLA by collecting VLM-orchestrated experience, imitating successful trajectories and then optimizing a residual controller with off-policy RL.","institution":"Google DeepMind","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2608.19891"},"policy":{"family":"Hybrid","baseModel":"Gemini high-level planner with the 3B Gemini Robotics On-Device (GROD) VLA, based on PaliGemma and a diffusion action head; MPO residual RL.","modelSize":"3B initial GROD policy variant; planner parameter count is not reported.","inputs":"Task goal, camera observations and robot state; a VLM supplies intermediate goals during exploration.","outputs":"Orchestrated subgoal-conditioned VLA actions during collection; an improved goal-conditioned VLA plus learned residual acts after training.","trainingData":"Successful VLM-orchestrated simulation rollouts, filtered with the environment's ground-truth task-success detector.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Three stages: explore with VLM / VLA orchestration, supervised VLA fine-tuning on successful data, then off-policy MPO residual-policy optimization.","evaluation":"Twenty-two simulated ALOHA manipulation tasks, including reasoning and skill chaining; stage ablations and policy learning curves."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The original paper is public; code, model and dataset release scopes are verified separately where established."},"evidence":[{"url":"https://arxiv.org/abs/2608.19891","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."}],"caveats":["All task experiments described here are simulation evaluations; real-data pretraining of GROD does not constitute EXIMO physical deployment.","Ground-truth simulation success is used for data selection; replacing it with a VLM judge is discussed as future work."],"verified":"2026-10-17"},{"id":"gen-1-5","name":"GEN-1.5","title":"GEN-1.5: Embodied Foundation Models are One-Shot Learners","date":"2026-08-19","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A robot foundation model uses sensorimotor demonstrations as physical prompts, with separate one-shot frozen-context and few-gradient-step adaptation conditions.","institution":"Not reported","venue":"Official project report","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"project":"https://generalistai.com/blog/gen-1.5"},"policy":{"family":"In-context policy","baseModel":"GEN-1.5, a proprietary multimodal robot foundation model trained from physical interaction data.","modelSize":"Not reported","inputs":"Thirty seconds of video context plus other sensor, language and proprioceptive inputs; a physical prompt contains 3–12 seconds of demonstration sensorimotor data.","outputs":"100 Hz robot-action trajectories.","trainingData":"Proprietary physical interaction data; the authors state that pretraining contains no simulation data. Data volume is not disclosed.","trainingTime":"The model had been pretraining continuously for over eight months at the report date. Target-task fine-tuning uses 1–10 gradient steps on 1–5 minutes of data (approximately 10–50 demonstrations).","trainingHardware":"Not reported","controlRate":"100 Hz output action trajectories; no separate complete model-query rate is disclosed.","trainingMethod":"Authors report emergent one-shot context learning without a bespoke meta-learning / ICL objective. A separately evaluated few-shot condition updates weights with gradient descent.","evaluation":"Ten short-horizon real manipulation tasks: mean success 59% ± 10% with one-shot context and 83% ± 9% with ten gradient steps on five minutes of data per task."},"release":{"code":"Not released","weights":"Not released","data":"Not released","license":"Proprietary; public code, model and dataset licenses are not provided.","notes":"Official technical blog and demonstrations; public model weights or training data are not released."},"evidence":[{"url":"https://generalistai.com/blog/gen-1.5","label":"Official project / author report","fields":["title","date","summary","policy","release","caveats"],"note":"Project-only evidence; any reported date is an explicit public report or code-release event, not a manuscript submission."}],"caveats":["The official report does not disclose parameter count, training hardware or an internal architecture diagram.","The reported ten tasks are simple, short-horizon tasks; one-shot context and gradient-updated few-shot results are different conditions.","Depth and tactile modalities are not established by the phrase other sensor inputs."],"verified":"2026-10-17"},{"id":"prism","name":"PRISM","title":"PRISM: Precision and contact-rich Real-world Industrial Skill dataset with Multimodal sensing","date":"2026-08-18","year":2026,"collections":["benchmark"],"categories":["supporting-resources","precision-assembly"],"resourceType":"dataset","summary":"Industrial contact-rich demonstrations with synchronized RGB-D, wrist force/torque, robot state and tactile observations for a subset of episodes.","institution":"Peking University / Delta Intelligence","venue":"IROS 2026","modalities":["RGB","Depth","Force/Torque","Tactile","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2608.17962","project":"https://tengbo-yu.github.io/PRISM/","data":"https://modelscope.cn/datasets/Turboyuuuu/PRISM","repository":"https://github.com/Tengbo-Yu/PRISM"},"benchmark":{"simulator":"Not applicable — physical dataset","simulatorVersion":"Not applicable","taskCount":"25+ industrial task types","tasks":["Electronic components plug/unplug","Conveyor Sorting","Plug Tube","Assemble Car","Dexterous Sorting","Packaging"],"taskListScope":"Examples explicitly named in paper/project; complete released task manifest must be consulted per platform.","capabilities":["Industrial precision","Connector insertion/removal","Force regulation","Contact-rich manipulation"],"metrics":"Task-success and baseline modality comparisons on selected representative tasks.","robots":"Franka Panda, Realman and other robot platforms described in paper; gripper / dexterous configurations vary.","sensors":"Multi-view RGB-D, six-axis F/T, robot joint/Cartesian/gripper state; tactile available only for a subset, not every episode.","dataCollection":"Human teleoperation through three interface families: exoskeleton, tracker and VR. Paired human demonstrations are a separate capture stream.","data":"5,000+ robot trajectories, 5,000+ paired human demonstrations, 45+ hours across 25+ tasks; three teleoperation interfaces.","protocol":"Exoskeleton, tracker and VR teleoperation; calibrated/timestamped multisensor records; policy experiments use 200 demonstrations/task and 20 evaluation episodes in reported settings.","baselines":"Multimodal imitation baselines with controlled sensor / teleoperation comparison; exact model table in paper."},"release":{"code":"Not released — inspected repository is the project website","weights":"Not verified","data":"Available","license":"Not verified","notes":"The official GitHub tree contains README/index, site styles, figures and paper PDF, without dataset-processing, collection or policy implementation files and with no release assets. The ModelScope dataset is a separate artifact; website source must not be presented as a released robot/data-processing implementation."},"evidence":[{"url":"https://arxiv.org/abs/2608.17962","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://tengbo-yu.github.io/PRISM/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/Tengbo-Yu/PRISM","label":"Official repository","fields":["benchmark.simulatorVersion"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://tengbo-yu.github.io/","label":"Author publication list","fields":["venue"],"note":"Primary author homepage labels PRISM IROS 2026."},{"url":"https://arxiv.org/abs/2608.17962","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."},{"url":"https://modelscope.cn/datasets/Turboyuuuu/PRISM","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["Tactile coverage is partial, despite RGB-D+F/T+tactile appearing in headline modality list.","Dataset diversity and evaluated baseline subset are separate scopes."],"verified":"2026-10-19","releaseByCollection":{"benchmark":{"code":"Not released — inspected repository is the project website","weights":"Not verified","data":"Available","license":"Not verified","notes":"The official GitHub tree contains README/index, site styles, figures and paper PDF, without dataset-processing, collection or policy implementation files and with no release assets. The ModelScope dataset is a separate artifact; website source must not be presented as a released robot/data-processing implementation."}},"relatedIds":[]},{"id":"skild-s1","name":"Skild S1","title":"Introducing S1: In-Context Learning for Robotics","date":"2026-08-18","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","generalist-foundation"],"resourceType":"policy","summary":"A proprietary in-context robot policy executes new manipulation tasks from one video demonstration without task-specific post-training.","institution":"Skild AI","venue":"Official technical report","modalities":["RGB","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"project":"https://www.skild.ai/blogs/s1"},"policy":{"family":"Other","baseModel":"Skild S1 embodied foundation model; architecture details not disclosed.","modelSize":"Not reported","inputs":"One video demonstration, current robot observations and retained task context.","outputs":"Robot manipulation actions; exact action parameterization / prediction horizon not disclosed.","trainingData":"Large-scale cross-task embodied pretraining; exact corpus not disclosed. The report compares one in-context example against separate task-specific post-training datasets.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"In-context execution with fixed deployment weights; pretraining recipe not disclosed.","evaluation":"Author-reported unseen real tasks and up-to-ten-minute horizons; task-specific post-training scaling comparison."},"release":{"code":"Not released","weights":"Not released","data":"Not released","license":"Not applicable; proprietary author report.","notes":"Official technical blog and videos; early-access sign-up is not a released model checkpoint."},"evidence":[{"url":"https://www.skild.ai/blogs/s1","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."}],"caveats":["The reported 380 demonstrations / 50–100 teleoperation hours describe a post-training comparison, not S1 pretraining compute.","No public executable release or fully specified evaluation suite is established."],"verified":"2026-10-17"},{"id":"tp-flow","name":"TP-Flow","title":"Task-Prototype Guided Flow Matching for Few-Shot Generalization in Vision-Language Robot Manipulation","date":"2026-08-18","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A few-shot flow policy compresses support demonstrations into phase-level task prototypes that guide the action prior and velocity field.","institution":"Henan Institute of Science and Technology","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2609.27780"},"policy":{"family":"Flow matching","baseModel":"ResNet-18 RGB encoder, robot-state MLP, CLIP text transformer and prototype-modulated flow decoder.","modelSize":"Not reported","inputs":"RGB/robot-state observation, language and one/few support demonstrations compressed into fixed-size prototype tokens.","outputs":"Continuous robot action chunks from a task-adaptive flow prior and gated velocity network.","trainingData":"Not reported","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"10 Hz closed-loop control; reported 54.3 ms chunk latency / 3.9 GB peak memory lacks an unambiguous hardware scope and is not generalized here.","trainingMethod":"Episodic support-query flow matching with prototype contrastive regularization and gated adaptive normalization.","evaluation":"LEROBOT-ARM-SO101 few-shot manipulation at one, four and six shots, with held-out object/goal-group generalization."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/2609.27780","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."}],"caveats":["Few-shot support conditioning is an inference context, not an implied task-specific gradient update.","Latency values are not assigned to a guessed GPU."],"verified":"2026-10-17"},{"id":"matchingpolicy","name":"MatchingPolicy","title":"MatchingPolicy: Correspondence-Aware Policy Enables Cross-Object In-Context Learning","date":"2026-08-17","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Dense semantic correspondence conditions a diffusion policy for cross-object in-context manipulation.","institution":"Hong Kong University of Science and Technology","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2608.16715","project":"https://matchingpolicy.github.io"},"policy":{"family":"Diffusion","baseModel":"Vision-foundation-model features, two-stage correspondence matching and a correspondence-conditioned diffusion policy.","modelSize":"Not reported","inputs":"Demonstration observations, current visual scene and robot state, with dense matched semantic correspondences.","outputs":"Continuous manipulation action trajectory conditioned on demonstrated correspondences.","trainingData":"Not reported","trainingTime":"One million optimization steps; seven days in the reported training configuration.","trainingHardware":"Four NVIDIA A6000 GPUs for training; RTX 4080 for evaluation.","controlRate":"Not reported","trainingMethod":"Decouple demonstration-to-scene correspondence from policy learning and train correspondence-aware diffusion actions.","evaluation":"RLBench and real-world few-shot manipulation across unseen instances and object categories."},"release":{"code":"Not released","weights":"Not released","data":"Not verified","license":"Not verified","notes":"Author project verified; its generic google/xxx template link is not a valid method code release."},"evidence":[{"url":"https://arxiv.org/abs/2608.16715","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://matchingpolicy.github.io","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."}],"caveats":["The project’s template repository placeholder is deliberately excluded from Code."],"verified":"2026-10-17"},{"id":"mg-vqa","name":"MG-VQA Agent","title":"MG-VQA: Manipulation Grounded Visual Question Answering with VLMs","date":"2026-08-17","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Uses grasping and pushing as evidence-gathering actions to answer questions about initially occluded tabletop scenes.","institution":"NVIDIA","venue":"Preprint","modalities":["RGB","Depth","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2608.17129","code":"https://github.com/vineet2104/MG-VQA"},"policy":{"family":"Robot agent","baseModel":"Eight tool-using VLMs, including GPT-6 Astra and Gemini 3.8 Flash; a shared Gemini 3.6 Flash perception tool returns scene graphs.","modelSize":"Not reported","inputs":"Question, rendered RGB observations and tool returns. The grasp tool consumes a segmentation mask, top-down depth map and camera intrinsics for GraspGen; raw depth is not a VLM image input.","outputs":"Information-gathering robot actions followed by an answer to Count, Find, Beneath or Compare questions.","trainingData":"No per-model robot-policy training; 600 human-verified questions, with a separate forty-question hard set, evaluate the agent interface.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen VLM tool use; observations and execution feedback determine whether to gather further evidence or answer.","evaluation":"MG-VQA-Bench with a simulated UR5 in cluttered scenes; Direct, Perception and Manipulation access conditions and MG-VQA-Hard."},"release":{"code":"Available","weights":"Not applicable","data":"Not verified","license":"MIT (code); model, dependency and dataset terms are separate.","notes":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms."},"evidence":[{"url":"https://arxiv.org/abs/2608.17129","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/vineet2104/MG-VQA/main/README.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."},{"url":"https://raw.githubusercontent.com/vineet2104/MG-VQA/main/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Code-license text checked directly; this license is not automatically assigned to model weights or third-party data."},{"url":"https://arxiv.org/abs/2608.17129","label":"Primary paper appendix: grasp tool","fields":["modalities","policy.inputs"],"note":"The grasp API lifts a segmentation mask with the top-down depth map and intrinsics before GraspGen. This is tool-mediated depth, separate from VLM RGB observations."}],"caveats":["The survey calls this PROBE; the current primary title and repository identify MG-VQA for the same arXiv ID.","Simulator state constructs / validates ground truth but is not exposed to the agent.","The main metric is answer correctness after manipulation, not a precision-assembly success rate.","The public paper shows benchmark / task illustrations; no dedicated policy architecture figure was identified.","Depth is used behind the grasp-tool interface; simulator ground-truth object state is not exposed to the VLM."],"verified":"2026-10-17"},{"id":"teach-and-grow","name":"Teach-and-Grow Learning (TGL)","title":"Teach and Grow: An Agent-Centered Architecture for General Robot Learning","date":"2026-08-17","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Turns a few successful demonstrations into closed-loop Skill Blocks, then expands a verified skill library and experience memory from execution.","institution":"Shanghai Jiao Tong University","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2608.17209","project":"https://tgl.changnie.top","code":"https://github.com/IRMVLab/TGL"},"policy":{"family":"Robot agent","baseModel":"GPT-6 Astra multimodal reasoning with Codex as the robot-tool interface; earlier analyzed traces use GPT-5.6 Sol.","modelSize":"Not reported","inputs":"Task, successful demonstrations, visual / RGB-D and robot-state tool observations, execution feedback, Skill Library and Experience Memory.","outputs":"Grounded closed-loop Skill Blocks and robot-tool calls with effect checks and recovery; validated blocks become reusable skills.","trainingData":"Few teaching demonstrations and subsequent interaction evidence; no task-acquisition gradient updates or policy fine-tuning.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen model weights; explicitly represented skill and memory state changes through teaching, validation and physical feedback.","evaluation":"LIBERO, LIBERO-Plus and robot demonstration / acquisition studies, with separately identified historical traces."},"release":{"code":"Available","weights":"Not applicable","data":"Not verified","license":"MIT (code); model, dependency and dataset terms are separate.","notes":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms."},"evidence":[{"url":"https://arxiv.org/abs/2608.17209","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/IRMVLab/TGL/main/README.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."},{"url":"https://raw.githubusercontent.com/IRMVLab/TGL/main/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Code-license text checked directly; this license is not automatically assigned to model weights or third-party data."}],"caveats":["Demonstrations improve reliability but the architecture can also run direct agent control.","Conceptual scaling plots are schematic and are not measured success-versus-compute curves."],"verified":"2026-10-17"},{"id":"zetta","name":"Zetta","title":"Zetta ζ: An Efficient Closed-Loop Embodied Harness for Self-Evolving Physical Intelligence","date":"2026-08-17","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Supports a frozen VLA with action-frequency critics, episodic recoveries and validated skill evolution at three time scales.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2608.16590","project":"https://air-embodied-brain.github.io/zetta","code":"https://github.com/air-embodied-brain/Zetta-Embodiment"},"policy":{"family":"Hybrid","baseModel":"Frozen robot foundation policy; the RoboCasa Atomic-Seen comparison uses a GR00T post-trained VLA.","modelSize":"Not reported","inputs":"Task, visual / robot observations, action execution feedback, recovery traces and retained skill memory. When the single RGB view is insufficient, the simulation protocol can consult internal simulator states, sensor trajectories, contact-force vectors and collision intensity.","outputs":"Critic-guided local corrections, recovery actions and validated reusable skills around a frozen VLA executor.","trainingData":"Interaction and recovery traces for system adaptation; no executor-weight fine-tuning in the described harness.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen action model; frequent runtime critique, intermediate recovery and slower validated skill updates.","evaluation":"18 RoboCasa Atomic-Seen tasks, LIBERO-Pro and additional deployment studies."},"release":{"code":"Available","weights":"Not applicable","data":"Not verified","license":"No root repository LICENSE found in the inspected tree; bundled and external dependency/model licenses remain separate.","notes":"Actual LIBERO/RoboCasa/RoboTwin agent/critic implementations, skill evolution and local/Ray rollout infrastructure are public. Simulator dependencies, licensed assets and pretrained policy checkpoints are configured separately; a new Zetta-specific weight or complete rollout-corpus release was not verified."},"evidence":[{"url":"https://arxiv.org/abs/2608.16590","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/air-embodied-brain/Zetta-Embodiment","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["The Ray infrastructure example with eight ranks / A100 GPUs is a configurable illustration, not measured training hardware.","The VLA checkpoint's original training is separate from harness evolution.","The fallback to internal simulator state and contact / collision signals is privileged simulation access; results are not uniformly RGB-only physical-policy execution."],"verified":"2026-10-19"},{"id":"tau0-vla","name":"τ0-VLA","title":"τ0-VLA: a Hierarchical Robot Foundation Model with World-Model-Guided Test-Time Computation","date":"2026-08-17","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","generalist-foundation"],"resourceType":"policy","summary":"Memory-aware high-level proposals are expanded with a world model, value model and reflection before a shared multi-robot action policy executes them.","institution":"Shanghai Innovation Institute / Agibot Finch / Chinese University of Hong Kong","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Bimanual","Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2608.16885","project":"https://tau0-vla.github.io/","code":"https://github.com/sii-research/tau-0-vla","weights":"https://huggingface.co/sii-research/tau-0-vla"},"policy":{"family":"Hybrid","baseModel":"Hierarchical VLA with world-model-guided test-time search. Qwen3.5-2B low-level MoT policy; independent Qwen3.5-9B proposal/value/reflection models and Step1X-Edit world model.","modelSize":"2B low-level VLM and three 9B high-level VLM components; full system count not reported.","inputs":"Multi-view RGB, proprioception, language instruction and updated execution memory. No measured depth channel specified for action inference.","outputs":"High-level next subtask plus 40D masked continuous action/state interface covering arms, grippers, waist and mobile base.","trainingData":"40,115 hours of heterogeneous real robot data and multimodal co-training; annotated/segmented trajectories generate high-level memory and subtask supervision.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Approximately 30 Hz low-level control; asynchronously refreshed high-level command cache about once per second.","trainingMethod":"Knowledge-isolated co-training, end-to-end co-training and embodiment adaptation; independently supervised high-level models. Inference changes memory and search budget without weight updates.","evaluation":"AGIBOT G1, ARX AC One and bimanual Franka FR3 long-horizon tasks, plus isolated annotated next-subtask prediction."},"release":{"code":"Available","weights":"Partial — released low-level VLA checkpoint","data":"Not verified","license":"Apache-2.0 code verified from LICENSE; official low-level checkpoint card also declares Apache-2.0. External high-level/world-model components retain separate terms.","notes":"The official model inventory contains model.safetensors and the low-level VLA configuration. This verifies a low-level checkpoint, not every separate high-level proposal/value/reflection model or the full multi-model system. No complete training corpus was independently verified."},"evidence":[{"url":"https://arxiv.org/abs/2608.16885","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://tau0-vla.github.io/","label":"Official project","fields":["imageCandidates"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://github.com/sii-research/tau-0-vla","label":"Official code","fields":["imageCandidates"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://raw.githubusercontent.com/sii-research/tau-0-vla/main/LICENSE","label":"Actual source license file","fields":["links"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://github.com/sii-research/tau-0-vla","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/sii-research/tau-0-vla","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["Distinct from τ0-WM; the hierarchical system additionally uses a separate low-level VLA and multiple reasoning models.","Open-loop next-subtask accuracy on annotated states and physical episode success are separate evaluations."],"verified":"2026-10-19"},{"id":"gigabrain07","name":"GigaBrain-0.7","title":"GigaBrain-0.7: Scaling Embodied Foundation Models to Emergent Capabilities with a Three-System Architecture","date":"2026-08-16","year":2026,"collections":["policy"],"categories":["generalist-foundation","agent-rsi-icl"],"resourceType":"policy","summary":"Coordinates continuous action control, vision-language planning and world-model prediction/value estimation across multiple robot embodiments.","institution":"GigaAI","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2608.15875","project":"https://gigaai.cc/blog/gigabrain07","code":"https://github.com/open-gigaai/giga-brain-0","data":"https://huggingface.co/datasets/open-gigaai/GigaBrain-0.7-SampleData","weights":"https://huggingface.co/open-gigaai/GigaBrain-0.7-3.5B-Base"},"policy":{"family":"Hybrid","baseModel":"System 1 PaliGemma2-3B plus 0.5B action expert; System 2 semantic planning; System 3 GigaWorld-1 future/value model","modelSize":"Released action checkpoint 3.5B; world/planning system components are additional and should not be collapsed into that count","inputs":"RGB, language, proprioception/history and optional predicted subgoal/value conditioning from System 3","outputs":"Continuous embodiment-specific action chunks plus semantic planning and predicted visual subgoals/progress","trainingData":"37,256.98 hours of cleaned embodied trajectories: 20,535.65 real-robot, 8,251.83 UMI, 2,862.36 egocentric, and 5,607.14 simulation/generated hours. The real-robot portion spans 16 robot types. A separate VLM corpus contains 271,976,674 image–text/QA samples.","trainingTime":"Not reported as full wall-clock duration","trainingHardware":"Not reported for full training","controlRate":"Not reported as a common cross-platform control rate","trainingMethod":"Joint one-stage VLA alignment with soft knowledge insulation; separately trained world/value system; offline/online experience-driven improvement","evaluation":"RoboTwin 2.0, EBench, RoboColiseum and physical Maker H01/mainstream robot platforms"},"release":{"code":"Available","weights":"Available","data":"Partial","license":"Apache-2.0 code, verified from LICENSE. Model and source-data terms are separate.","notes":"Availability concerns author-linked artifacts; no training or installation reproduced."},"evidence":[{"url":"https://arxiv.org/abs/2608.15875","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/open-gigaai/giga-brain-0","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://gigaai.cc/blog/gigabrain07","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."},{"url":"https://github.com/open-gigaai/giga-brain-0/blob/main/LICENSE","label":"Verified code license","fields":["release.license"]}],"caveats":["3.5B names the action/VLA checkpoint, not all three systems combined.","Sample data availability does not mean the full 37k-hour corpus is released."],"verified":"2026-10-17"},{"id":"vitar","name":"ViTaR","title":"ViTaR: Visuo-Tactile Residual Adaptation for Foundation VLA Manipulation","date":"2026-08-16","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Bounded tactile residual adaptation modulates a frozen VLA using offline branch-outcome preferences.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2608.15816"},"policy":{"family":"Hybrid","baseModel":"Frozen OpenVLA-OFT; Effect-Guided Modeling (EGM) and Residual Action Modulation (RAM).","modelSize":"Not reported","inputs":"Scene/wrist RGB, language, proprioception, marker-derived contact descriptors and bilateral tactile summaries.","outputs":"Base VLA action plus a selected bounded residual direction and learned continuous scale.","trainingData":"50 OFT demonstrations per UniVTAC task; additional 20 restored decision groups/task ×5 short branches =100 branch rollouts, reused in 160 preference pairs; not 100 independent demonstrations.","trainingTime":"EGM120 epochs; RAM120 epochs; wall-clock Not reported.","trainingHardware":"8 NVIDIA RTX4090 GPUs for foundation fine-tuning, EGM/RAM and offline branch processing.","controlRate":"Real controller 10 Hz; camera/tactile collection 30 Hz.","trainingMethod":"Offline EGM ranking/adaptability training, then frozen-EGM RAM selection/magnitude training, each 120 epochs; no online base-policy weight update.","evaluation":"Seven UniVTAC simulation tasks,100 episodes/method/task; real insertion, sliding and grasping, 20 trials/task."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2608.15816","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."}],"caveats":["Branch rollouts and preference pairs are separate supervision units and are not added to expert-demo totals.","Tactile summaries modulate residual magnitude; offline outcome labels are unavailable at runtime."],"verified":"2026-10-17"},{"id":"core-counterfactual","name":"CoRe","title":"Imagining Recovery: Inference-Time Counterfactual Realignment for Vision-Language-Action Models","date":"2026-08-14","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"A frozen VLA is probed on counterfactually restored observations to choose a minimal physical recovery after task switches or disruptions.","institution":"Not reported in accessible author affiliation markup","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2608.14822"},"policy":{"family":"Hybrid","baseModel":"Inference-time counterfactual recovery wrapper. Frozen π0/π0.5, X-VLA, GR00T and OpenVLA variants; calibrated 3D observation synthesis rather than a learned video world model.","modelSize":"Not reported","inputs":"VLA observations/state/language plus a recovery system with depth, object masks/poses, robot kinematics and retained scene geometry. Simulation uses privileged poses/masks; real perception uses Grounding-DINO, SAM2 and shape-specific geometry.","outputs":"Recovery object-restoration plan and robot rejoin configuration, followed by resumed frozen-policy actions.","trainingData":"Underlying policy demonstrations only; real π0.5 fine-tuning uses 100 clean demonstrations each for pick, place and push.","trainingTime":"No recovery-specific training; underlying fine-tuning wall time not reported.","trainingHardware":"Real π0.5 fine-tuning: one H200. Physical inference/perception: two RTX 4090 cards; simulation evaluation uses RTX A6000 shards.","controlRate":"Real xArm6 control 10 Hz; ten-step policy chunks, eight executed before a query.","trainingMethod":"Underlying policies are task-trained offline; recovery uses OOD detection, synthesized counterfactual observations and physical restoration without policy updates.","evaluation":"LangSwitch language changes, perturbed LIBERO-Long and physical xArm6 recovery."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No author-linked implementation was verified in the inspected primary manuscript/project. This is an evidence gap, not proof that no implementation exists."},"evidence":[{"url":"https://arxiv.org/abs/2608.14822","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."}],"caveats":["Simulation recovery relies on privileged state; real recovery replaces it with object detection, depth/analytic pose estimators and motion planning.","This is a full perception-and-restoration system around a VLA, not an RGB-only VLA recovery improvement."],"verified":"2026-10-17"},{"id":"stellavla","name":"StellaVLA","title":"StellaVLA: In-Context Structured Demonstration for Generalizable Vision-Language-Action Models","date":"2026-08-12","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Converts demonstrations into structured visual, kinematic and language context, then trains a VLA with an action expert and a spatial-language auxiliary objective.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2608.11671"},"policy":{"family":"VLA","baseModel":"Qwen3-VL-4B-Instruct with an OpenVLA-OFT-style MLP action expert and a spatial-language expert.","modelSize":"4B backbone; complete policy parameter count is not reported.","inputs":"Third-person and wrist RGB, language-rendered robot state, task instruction and one retrieved same-task structured demonstration.","outputs":"Continuous action chunks from the MLP expert; a parallel spatial-language expert predicts subtask and 2D / 3D action descriptions during training but is not decoded at inference.","trainingData":"Structured robot / human / XR demonstrations; offline annotation derives subgoals and motion descriptions from trajectories.","trainingTime":"30,000 full-parameter optimization steps, global batch size 128; elapsed duration is not reported.","trainingHardware":"Not reported","controlRate":"The real AgileX Piper control stack runs at 30 Hz; a 16-step chunk spans approximately 0.53 seconds.","trainingMethod":"Offline action L1 loss plus spatial-language cross-entropy weighted 0.3; retrieval supplies test-time examples without new target-task weight updates.","evaluation":"Standard LIBERO, LIBERO-Plus perturbations and real AgileX Piper tasks, with context and language-loss ablations."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2608.11671","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["2D / 3D action descriptions are structured supervision; they do not establish measured depth input.","Increasing the language-loss weight does not monotonically improve out-of-distribution robustness in the paper."],"verified":"2026-10-17"},{"id":"shaper","name":"SHAPER","title":"Self-Evolving Embodied Agents via Skill-Harness Evolution","date":"2026-08-11","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Adapts a frozen planner by separately evolving textual skills and the context-construction harness through held-out rollout feedback.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2608.11350"},"policy":{"family":"Robot agent","baseModel":"Frozen planner / optimizer with Qwen3.6-27B API-equivalent costing in the reported setup.","modelSize":"Not reported","inputs":"Embodied observations, task context, textual skill and context-construction code, with training / validation rollout feedback.","outputs":"Evolved skills and executable context / interaction harness artifacts for a fixed planner and tool interface.","trainingData":"VLABench: 15 training episodes and 24 validation episodes, disjoint from 800 evaluation episodes; ESI-Bench uses ten training and ten validation questions.","trainingTime":"Four beam-search rounds, width three and branch factor two, using feedback minibatches of four rollouts; elapsed duration is not reported.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen weights; optimize the skill first, then the harness while holding the skill fixed, using text feedback and beam search.","evaluation":"VLABench robot manipulation and a separately reported ESI-Bench embodied-question subset."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2608.11350","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["API-equivalent costs exclude final evaluation and simulator / GPU infrastructure.","The question-answering evaluation is adjacent to, rather than a substitute for, physical manipulation evaluation."],"verified":"2026-10-17"},{"id":"4d-wam","name":"4D-WAM — trajectory alignment","title":"4D-WAM: Infusing Spatiotemporal Awareness into World Action Models through Trajectory Fields","date":"2026-08-08","year":2026,"collections":["policy"],"categories":["rgbd-3d","generalist-foundation"],"resourceType":"policy","summary":"Adds trajectory-field representation alignment to existing WAMs, supervising local 3D motion and longer-horizon destinations without a deployment geometry branch.","institution":"HKUST (Guangzhou) / Adelaide University","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2608.08023"},"policy":{"family":"World-action model","baseModel":"Fast-WAM-Joint or LingBot-VA plus frozen Trace Anything trajectory teacher during training","modelSize":"Base-model dependent; alignment does not define one fixed full-policy parameter count","inputs":"RGB, language and base-policy robot state; trajectory/3D supervision is training-only","outputs":"Base WAM action chunks and optional visual futures","trainingData":"Official LIBERO/RoboTwin demonstrations; RoboTwin C2R uses clean-only training","trainingTime":"Fast-WAM: 30k RoboTwin / 25k LIBERO steps; LingBot-VA C2R 20k steps; wall-clock Not reported","trainingHardware":"Eight H100 GPUs for reported experiments","controlRate":"No additional deployment branch; base WAM latency applies by configuration","trainingMethod":"Motion alignment of temporal features and destination-distribution alignment to frozen 3D trajectory fields","evaluation":"LIBERO and RoboTwin ID/OOD, precision, robustness/generalization and physical manipulation"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified; pretrained backbones and data retain separate terms","notes":"Availability concerns author-linked artifacts; no training or installation reproduced."},"evidence":[{"url":"https://arxiv.org/abs/2608.08023","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."}],"caveats":["This robotics paper, arXiv: 2608.08023, is distinct from the autonomous-driving 4D-WAM paper, arXiv: 2608.10107.","Training-time 3D trajectory targets do not make it a measured RGB-D deployment policy."],"verified":"2026-10-17"},{"id":"vla-talker","name":"In-Context VLA / VLA-Talker","title":"In-Context VLA: Endowing Vision-Language-Action Models with Language via In-Context Post-Training and Agentic Tool Use","date":"2026-08-06","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A VLA consumes tool-grounded language context and learns when to request perceptual evidence while supervising only its action path.","institution":"Nankai University / Tsinghua University / EPFL","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2608.05738"},"policy":{"family":"VLA","baseModel":"Pretrained VLA action policy augmented with a structured language-context interface; tool stack includes an open-vocabulary detector, DepthAnything and Qwen2.5-VL-7B locator.","modelSize":"Not reported","inputs":"RGB observations, task instruction, proprioception and tool-generated structured perceptual context. Depth is monocular prediction, not a measured depth stream.","outputs":"Action chunks and optional perceptual-tool requests.","trainingData":"Not reported","trainingTime":"Eight-A100 stage averages: in-context SFT 17.1 h / 137 GPU-hours; GRPO 8.7 h / 70 GPU-hours.","trainingHardware":"Eight NVIDIA A100-80GB GPUs for the reported stage budgets.","controlRate":"12.8 Hz inference on one A100, batch size one; distinct from actuator frequency.","trainingMethod":"Action-only in-context supervised post-training, followed by trajectory-level GRPO to align tool use with task success.","evaluation":"RoboCasa-GR1, SimplerEnv, LIBERO and eight real manipulation tasks."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/2608.05738","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."}],"caveats":["The paper’s method name is VLA-Talker; In-Context VLA is the paper-title prefix.","Predicted monocular depth is not classified as an observed Depth modality."],"verified":"2026-10-17"},{"id":"bridgevla-plus","name":"BridgeVLA++","title":"BridgeVLA++: A Data-Efficient, Generalizable, and Memory-Augmented Vision-Language-Action Framework for 3D Manipulation","date":"2026-08-05","year":2026,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Adds temporal keyframe memory and re-rendered spatial memory to heatmap-based 3D manipulation, including bimanual control.","institution":"CASIA / UCAS / FiveAges / ByteDance Seed","venue":"Preprint; submitted toTPAMI","modalities":["RGB","Depth","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2608.05042","project":"https://bridgevla-plus.github.io/","code":"https://github.com/BridgeVLA/BridgeVLA/tree/main","data":"https://huggingface.co/datasets/LPY/BridgeVLA","weights":"https://huggingface.co/datasets/LPY/BridgeVLA/tree/main/checkpoints"},"policy":{"family":"VLA","baseModel":"PaliGemma heatmap backbone; temporal/spatial memory injection and subgoal gate","modelSize":"2.92B backbone plus 269.77M memory modules; full action-head total not separately reported","inputs":"Calibrated RGB-D point clouds, language, initial scene and selected history keyframes","outputs":"End-effector keyposes, rotation, gripper and optional collision flags; arm-specific bimanual heads","trainingData":"120k RoboPoint grounding images; simulation generally100 demos/task (RMBench50); realFranka10/task, Dobot70 total","trainingTime":"Grounding≈2 h; real adaptation≈1.5 h; benchmark fine-tuning130–320 epochs; simulation wall-clock not reported","trainingHardware":"Grounding/real 8 A100; RLBench/COLOSSEUM/GemBench32H20; RMBench8H20/task; MemoryBench8 A100","controlRate":"Full-model point-cloud-to-action latency0.57 s on oneRTX4090; planner execution adds time","trainingMethod":"Grounding pretraining then freeze/unfreeze imitation fine-tuning with supervised memory selection","evaluation":"RLBench, COLOSSEUM, GemBench, RMBench, MemoryBench; Franka Research 3 andDobotCR5A real tasks"},"release":{"code":"Available","weights":"Available","data":"Partial","license":"Apache-2.0 code; PaliGemma/Gemma and data terms separate","notes":"Main branch releases five-benchmark training/evaluation and checkpoint download scripts; grounding/simulation data linked; self-collected real data not published."},"evidence":[{"url":"https://arxiv.org/abs/2608.05042","label":"First submission and canonical paper identity","fields":["title","date","venue"],"note":"Distinct journal-extension manuscript, not an alias of2506.07961."},{"url":"https://arxiv.org/html/2608.05042v1","label":"Full paper:SectionsIII–IV,TableVII,AppendicesE–F","fields":["policy","modalities","environments","embodiments"],"note":"Memory parameter overhead and full-model real latency are scoped separately from the original BridgeVLA. Epoch counts vary by benchmark."},{"url":"https://github.com/BridgeVLA/BridgeVLA/tree/main","label":"Official BridgeVLA++ release","fields":["release","links"],"note":"README distinguishes original bridgevla branch, exposes code/checkpoint download recipes and states real data unpublished."},{"url":"https://bridgevla-plus.github.io/","label":"Author architecture diagram","fields":["summary","imageCandidates"],"note":"Fig2 includes the observation/action interface and new memory path."},{"url":"https://raw.githubusercontent.com/BridgeVLA/BridgeVLA/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code scope only."}],"caveats":["A keypose policy: motion-planner/controller dependencies remain part of execution.","Distinct extension of BridgeVLA; shared project/repository does not make the two architectures aliases.","Training figures exclude original PaliGemma pretraining."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Partial","license":"Apache-2.0 code; PaliGemma/Gemma and data terms separate","notes":"Main branch releases five-benchmark training/evaluation and checkpoint download scripts; grounding/simulation data linked; self-collected real data not published."}},"relatedIds":[]},{"id":"dreamwam","name":"DreamWAM","title":"DreamWAM: Beyond RGB Future Prediction for World Action Models","date":"2026-08-05","year":2026,"collections":["policy"],"categories":["rgbd-3d","generalist-foundation"],"resourceType":"policy","summary":"Uses motion, geometry and semantic future supervision to improve RGB-only WAM representations, discarding auxiliary beyond-RGB modules at deployment.","institution":"Huazhong University of Science and Technology / Horizon Robotics","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2608.04996","project":"https://hustvl.github.io/DreamWAM/","code":"https://github.com/hustvl/DreamWAM","weights":"https://huggingface.co/hustvl/DreamWAM"},"policy":{"family":"World-action model","baseModel":"Wan2.2-5B VideoDiT and ActionDiT; RAFT, Depth Anything 3, and DINOv2 teachers with gated geometry/semantic residual branches.","modelSize":"5B video backbone plus action expert; full training/deployment totals Not reported here","inputs":"RGB, language and state at deployment; flow/depth/semantic signals are training-only","outputs":"32-action chunks, with ten actions executed before replanning and ten denoising steps in the matched simulation setting. Joint RGB-video rollout and no-rollout variants are evaluated separately.","trainingData":"Standard LIBERO training uses 2,000 demonstrations and no LIBERO-Plus data. Real multi-task training pools 400 demonstrations for each of four tasks, or 1,600 total.","trainingTime":"Real multi-task policies train for five epochs on the same pooled data. Full wall-clock duration is Not reported.","trainingHardware":"Matched simulation variants train on eight H20 GPUs with bfloat16 and batch 16.","controlRate":"Separate no-rollout and joint video-action inference settings; common deployment rate Not reported here","trainingMethod":"Joint RGB/motion latent denoising, gated geometry/semantic supervision and shared video-action attention","evaluation":"LIBERO: 2,000 rollouts per seed. LIBERO-Plus: seven perturbation dimensions and 10,030 episodes per seed. Physical evaluation changes lighting, background, and object layout."},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 code; third-party teacher/model terms separate","notes":"Availability concerns author-linked artifacts; no training or installation reproduced."},"evidence":[{"url":"https://arxiv.org/abs/2608.04996","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/hustvl/DreamWAM","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://github.com/hustvl/DreamWAM/blob/main/LICENSE","label":"Verified code license","fields":["release.license"]}],"caveats":["Deployment uses RGB rather than measured depth or tactile input; depth and optical-flow targets are training supervision.","Matched no-rollout and joint-rollout baselines use different inference modes."],"verified":"2026-10-17"},{"id":"mimir","name":"Mimir","title":"Mimir: A Neuro-Symbolic Memory System with Dynamic Grounding for Embodied Agents in Interactive Environments","date":"2026-08-05","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Separates persistent world evidence from task progress, then dynamically grounds the active goal against both memories before acting.","institution":"PrimeBot Research Institute; Peking University; University of Chinese Academy of Sciences","venue":"Preprint","modalities":["RGB","Language"],"environments":["Simulation"],"embodiments":["Simulated household agent"],"links":{"paper":"https://arxiv.org/abs/2608.04933"},"policy":{"family":"Robot agent","baseModel":"Backbone-agnostic memory wrapper evaluated with Qwen3-VL, Qwen3.5, InternVL, Gemma and Cosmos-Reason variants; main examples include Qwen3-VL-8B / 32B.","modelSize":"Evaluated backbones range from 8B to 78B; these are alternatives rather than one combined model.","inputs":"Task, current visual observations, world entities / relations / evidence and explicit task progress / held-object / failure memory.","outputs":"Grounded next actions through discrete household-agent interfaces, plus updates to world and task memory.","trainingData":"No new model-weight training is required by the memory system; task episodes supply external execution evidence.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen VLM with neuro-symbolic world / task memory, goal-conditioned recall and grounding validation.","evaluation":"EmbodiedBench EB-ALFRED and EB-Habitat, including base and long-horizon conditions."},"release":{"code":"Not released","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The primary paper explicitly says code will be released soon; no released implementation is claimed. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms."},"evidence":[{"url":"https://arxiv.org/abs/2608.04933","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."}],"caveats":["The benchmark uses discrete household interaction; reported results do not establish continuous-arm contact precision.","Comparisons across different backbone sizes should not be interpreted as memory-only ablations."],"verified":"2026-10-17"},{"id":"rtcf","name":"RTCF","title":"Retrieve in Time, Correct in Frequency","date":"2026-08-05","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A training-free correction layer aligns current execution with successful memories and transfers bounded low-frequency action residuals to a frozen VLA.","institution":"Research Institute of Tsinghua University in Shenzhen / Everwise-Tech","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2608.04527"},"policy":{"family":"Hybrid","baseModel":"Frozen π0-FAST policy with Progressive Memory Alignment and frequency-domain residual correction.","modelSize":"Not reported","inputs":"Current/history RGB observations, task/state inputs of the frozen policy, and complete successful demonstration trajectories.","outputs":"Corrected continuous action chunks; high-frequency motion and gripper decisions remain from the base policy.","trainingData":"Successful trajectory memory bank for each evaluated LIBERO suite; no correction-module gradient training.","trainingTime":"No correction-module training.","trainingHardware":"Not applicable to the training-free correction module.","controlRate":"Median added CPU-stage latencies sum to 10.99 ms per action chunk; base-policy timing measured on RTX 4090, CPU on Xeon Platinum 8358P.","trainingMethod":"Causal monotonic visual-memory alignment and coefficient-clipped low-frequency action transfer at inference.","evaluation":"Four LIBERO suites, 2,000 episodes per condition."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/2608.04527","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."}],"caveats":["The malformed abstract hyperlink overhead.RTCF is not a verified project URL.","CPU correction overhead is not the overall policy frequency."],"verified":"2026-10-17"},{"id":"vlaff","name":"VLAff","title":"VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances","date":"2026-08-05","year":2026,"collections":["policy"],"categories":["rgbd-3d","generalist-foundation"],"resourceType":"policy","summary":"A unified affordance model predicts interaction regions, grasp poses and trajectories, then uses scene depth to execute robot manipulation.","institution":"The University of Tokyo","venue":"IROS 2026","modalities":["RGB","Depth","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2608.05215","project":"https://ojh6404.github.io/vlaff/"},"policy":{"family":"Hybrid","baseModel":"Qwen2.5-VL with DINOv2 visual features and visual/grasp/trajectory affordance outputs.","modelSize":"Not reported","inputs":"RGB observation and language instruction; downstream execution uses camera intrinsics and scene depth to anchor predicted affordances in 3D.","outputs":"Affordance heatmap, grasp pose and trajectory transformed to object-centric executable robot commands.","trainingData":"EgoAffordance: 204,025 episodes, 5,782,431 visual heatmaps and 11,612,524 trajectory sequences in the implementation section.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Extract affordances from human egocentric videos using SfM/hand reconstruction, then train unified multimodal affordance prediction.","evaluation":"Affordance benchmarks, zero-shot real manipulation and affordance-guided robot learning."},"release":{"code":"Not released","weights":"Not released","data":"Not released","license":"Not verified","notes":"Official project labels code, dataset and model as forthcoming."},"evidence":[{"url":"https://arxiv.org/abs/2608.05215","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://ojh6404.github.io/vlaff/","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."}],"caveats":["The robot-executable system includes 3D anchoring/control; an affordance heatmap alone is not treated as an action policy.","Human reconstruction supplies training labels; runtime depth belongs to downstream action grounding."],"verified":"2026-10-17"},{"id":"eta-openeta","name":"OpenETA","title":"ETA: A New Agentic Paradigm for Embodied Tasks","date":"2026-08-04","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Places a general reasoning agent above a host-checked tool interface, with fresh visual observations after each world-changing action.","institution":"Shanghai Innovation Institute; Fudan University; Jilin University; Nanjing University; Zhejiang University","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2608.03924","project":"https://openmoss.ai/OpenETA/","code":"https://github.com/OpenMOSS/OpenETA"},"policy":{"family":"Robot agent","baseModel":"OpenETA and an OpenETA-for-Codex host with GPT-5.6 Luna / Terra / Sol at medium reasoning effort.","modelSize":"Not reported","inputs":"Task, current visual observations, versioned tool schemas, execution receipts and selected / replayed memory.","outputs":"Host-validated observe, mark_point and move_to calls followed by fresh world evidence.","trainingData":"No task-policy fine-tuning in the reported Codex condition; interaction traces and versioned tools are execution artifacts.","trainingTime":"The separate 400-episode GPT-5.6 Luna evaluation batch took 19.59 hours; this is evaluation wall time, not model training.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen planner weights; task-level feedback, replayed context and tool / host refinement change execution behavior without a learned VLA tool in the reported configuration.","evaluation":"Fixed 400-episode LIBERO matrix and a separate task-level Pass@k study over 130 LIBERO tasks."},"release":{"code":"Partial","weights":"Not applicable","data":"Not verified","license":"Apache-2.0 (code); model, dependency and dataset terms are separate.","notes":"The repository publishes the core host, Codex plugin and separately documented physical-deployment branch; paper experiments retain condition-specific tool / infrastructure manifests. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms."},"evidence":[{"url":"https://arxiv.org/abs/2608.03924","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/OpenMOSS/OpenETA/main/README.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."},{"url":"https://raw.githubusercontent.com/OpenMOSS/OpenETA/main/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Code-license text checked directly; this license is not automatically assigned to model weights or third-party data."}],"caveats":["GPT-5.6 Sol solves 117/130 tasks at Pass@5, while first-seed success is 92/130; these are different metrics.","The 400-episode matrix includes infrastructure-contaminated cells identified by the authors; resource totals are not an infrastructure-clean benchmark claim."],"verified":"2026-10-17"},{"id":"roboreact","name":"RoboReact","title":"RoboReact: Agentic Skill Distillation from Generated Egocentric Videos for Generalizable Whole-Body Manipulation","date":"2026-08-04","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Generated interaction videos become object-centric dexterous keyframe programs; offline VLM refinement precedes frozen, perception-grounded execution.","institution":"The Chinese University of Hong Kong, Shenzhen","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Real world"],"embodiments":["Dexterous hand","Bimanual","Humanoid"],"links":{"paper":"https://arxiv.org/abs/2608.03387","project":"https://roboreact.github.io/"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The inspected primary sources do not establish a public implementation, model checkpoint or dataset download. Paper figures retain their original rights."},"evidence":[{"url":"https://arxiv.org/html/2608.03387","label":"Original full paper, methods and evaluation","fields":["title","date","institution","summary","modalities","policy","model","benchmark","benchmark.dataCollection","caveats"],"note":"Publication date is arXiv v1; implementation and deployment observations are distinguished from training supervision, generated geometry and base-model identity."}],"caveats":["The VLM participates in offline calibration/refinement and is absent from the test-time control loop. Online feedback re-grounds the frozen keyframe program.","A BrainCo Revo2 Touch hand name does not establish tactile feedback in the reported policy.","Real RGB-D is supplied by head RealSense D435i; a third-person D435 records calibration feedback."],"verified":"2026-10-21","policy":{"family":"Hybrid","baseModel":"Video generator, VLM keyframe selector/code editor, WildDet3D pose estimation and HOMIE whole-body controller.","modelSize":"Not reported","inputs":"Initial egocentric RGB-D/task; generated-video keyframes and calibration traces; current object pose/robot state during frozen execution.","outputs":"Object-centric end-effector/hand keyframes projected into feasible whole-body targets.","trainingMethod":"Generate/select video, recover and retarget interaction keyframes; bounded VLM code edits from calibration rollouts; freeze skill before test.","trainingData":"Generated interaction videos and task-specific calibration rollouts; no human task demonstration or policy-weight update is required by the reported refinement.","trainingTime":"Not reported","trainingHardware":"Offboard RTX 4080 Super for perception/high-level execution.","controlRate":"Not reported","evaluation":"G1 29DoF + dual Revo2 Touch hands; Hand Over, Open Box, Pour Water, Open Drawer and robustness/object/pose variants."}},{"id":"retouch","name":"ReTouch","title":"ReTouch: Empowering Contact-Rich Dexterous Manipulation with Online-Refined Tactile Prediction","date":"2026-08-03","year":2026,"collections":["policy"],"categories":["tactile-foundation","tactile-il"],"resourceType":"policy","summary":"A tactile-patch VLA refines predicted future contact latents and remaining actions as new hand feedback arrives.","institution":"University of Science and Technology of China / iFLYTEK / The Chinese University of Hong Kong","venue":"Preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2608.01824"},"policy":{"family":"VLA","baseModel":"Pretrained π0; separate hindsight and foresight 18-layer, width-1024 action Transformers.","modelSize":"Total parameter count Not reported.","inputs":"Three RGB views, instruction, 18 joint positions and a ten-frame window (nine past plus current) of 5×120×3 tactile force readings; patch/finger identity retained.","outputs":"16-step chunks of 18-dimensional absolute joint-position targets for UR7e and XHand, plus internally maintained future tactile latents.","trainingMethod":"20k-step tactile-patch pretraining; joint flow-matching and hindsight-latent alignment. Privileged hindsight expert and alignment projector removed at deployment; foresight expert recursively revises chunks.","trainingData":"XHT-Dataset: 900 successful demonstrations over seven tasks; trajectory-level split of 800 common training / 100 held-out offline test episodes. One policy trains jointly across tasks.","trainingTime":"20k encoder pretraining + 80k policy steps; encoder frozen for the first 5k policy steps. Wall-clock time Not reported.","trainingHardware":"8 NVIDIA A800 GPUs, bfloat16.","controlRate":"RTX 5090 model-only benchmark: 9.02 full cycles / 36.08 FAE passes per second, excluding communication, execution and scheduling; physical achieved control Hz Not reported. Full variant blocks while refinements return.","evaluation":"UR7e–XHand: Pipette Press, Bottle Grasp, Cob Grasp, Sponge Wipe, Button Press, Cabinet Retrieval and Liquid Transfer; 20 trials per task plus four robustness conditions. Reported “success rate” is mean normalized staged completion, binary only for Button Press."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No retrievable official policy code/checkpoint/data release was verified."},"evidence":[{"url":"https://arxiv.org/abs/2608.01824","label":"Original paper and reviewed full text","fields":["title","date","institution","summary","policy","caveats"],"note":"Full primary text, including available supplementary implementation details; first public date retained separately from reviewed revision."}],"caveats":["Reviewed arXiv v2 implementation appendices. Future ground-truth touch is a training-only hindsight signal.","The advertised 9/36 Hz comes from model-compute timings, not end-to-end robot throughput.","Except Button Press, scores grant partial completion credit and must not be treated as binary task success frequencies."],"verified":"2026-10-17"},{"id":"weights-or-skills-survey","name":"Weights or Skills?","title":"Weights or Skills? A Survey of Robot-Learning Techniques: from Action-Predicting Weights to Robots that Write their Own Skills","date":"2026-08-03","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"survey","summary":"A focused review of learned weights, executable skills and how feedback, persistent memory and search change robot capability.","institution":"Author affiliations: UC Berkeley; SJSU; Meta; Apple; PocketFM; BITS Pilani Goa (independent work)","venue":"Preprint · Survey","modalities":[],"environments":["Literature review"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2608.01851"},"survey":{"scope":"Robot-learning techniques organized by the artifact that stores reusable capability.","taxonomy":["Code-as-policy","End-to-end VLA","Reward synthesis","Skill libraries","Sim-to-real and transfer","Benchmarks and simulators"],"adaptationRegimes":["Program synthesis","Closed-loop repair","Library accumulation","Evolutionary search","Feedback–memory–search loops"],"contextSources":["Language","Execution feedback","Stored code and skills"],"evaluationAxes":["Capability representation","Revision mechanism","Composition","Portability"],"corpus":"40-page paper: 77 representative taxonomy systems and 225 separate landscape works, totaling 302 systems in the author's accounting.","reviewMethod":"Focused literature selection, taxonomy and contrast tables; broader landscape entries appear in Appendix A.","coverageLimits":"The six branches include adjacent simulators and open-world software agents; this atlas selectively follows robot-manipulation methods."},"release":{"code":"Not applicable — no new robot policy","weights":"Not applicable — survey","data":"Literature tables in paper; separate structured release not verified","license":"Paper and research figures retain author/publisher rights; no software license is inferred from the survey.","notes":"Author affiliations do not imply employer-sponsored publication."},"evidence":[{"url":"https://arxiv.org/abs/2608.01851","label":"Primary survey","fields":["title","date","institution","survey","release"],"note":"PDF Figure 2, Section 2 and Appendix A distinguish taxonomy systems from landscape works."}],"caveats":["The paper's comparative categories are its own taxonomy, not a shared performance ranking."],"relatedIds":["code-as-policies","aspire","enpire","eureka","dreureka"],"verified":"2026-10-17"},{"id":"agentic-robotics-loop","name":"AgenticRobotics","title":"You Don't Need To Stay in The Loop: An Agentic Robotics Loop for Robot-Policy Improvement","date":"2026-08-02","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Uses an LLM control plane, disposable workers and durable experiment transactions to automate reproducible robot-policy screening and improvement campaigns.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2608.07555"},"policy":{"family":"Hybrid","baseModel":"LLM experiment controller with policy candidates from π0.5, MolmoAct2, GR00T N1.7 and X-VLA families; a separate offline-RL campaign is also reported.","modelSize":"Not reported","inputs":"Immutable experiment objectives, code / checkpoints, training and evaluation artifacts, statistical gates and worker execution logs.","outputs":"Reproduced experiments, policy / inference configurations, evaluation records and gated candidate promotions.","trainingData":"Two proof-of-concept campaigns: a task-specific VLA screening study and an offline-RL workflow; this is experiment orchestration rather than a new common robot dataset.","trainingTime":"Not reported","trainingHardware":"NVIDIA Quadro RTX 8000 for the reported proof-of-concept campaigns.","controlRate":"Not reported","trainingMethod":"Frozen controller organizes train–evaluate–improve transactions; workers can train policies or change inference settings while preserving provenance and promotion checks.","evaluation":"A deliberately selected LIBERO-10 book-in-caddy task, an incomplete two-task extension and an offline-RL campaign."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The paper identifies a working-tree revision and experimental artifacts; a standalone public implementation repository was not verified."},"evidence":[{"url":"https://arxiv.org/abs/2608.07555","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["The main LIBERO task was selected for its highest baseline in a probe; results are task-conditional and cannot support suite-level state-of-the-art claims.","The work demonstrates reliable automation of experiments rather than proving a universally better robot-policy discovery algorithm."],"verified":"2026-10-17"},{"id":"caat","name":"CAAT","title":"CAAT: Contact-Aware Attention Scaling and Tactile Masking for Data-Efficient Contact-Rich Manipulation","date":"2026-08-02","year":2026,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"Masks background tactile appearance and scales attention by contact to improve data efficiency across multiple policy backbones.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2608.01102","project":"https://mrjiangjm.github.io/caat/"},"policy":{"family":"Hybrid","baseModel":"ACT, Diffusion Policy and π0 with contact-aware tactile fusion","modelSize":"Not reported","inputs":"RGB, tactile images/reference, robot state and language for π0","outputs":"Backbone-specific action chunks","trainingData":"Not reported","trainingTime":"Not reported","trainingHardware":"ACT/DP: one A100 80GB; π0 LoRA: two A100 80GB","controlRate":"Not reported","trainingMethod":"Reference-based tactile masking and contact-aware attention scaling","evaluation":"Five UniVTAC tasks; Lift Bottle, Open Box and another real contact task (examples only)"},"release":{"code":"Not released","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The current author code repository contains only README.md and explicitly states training/evaluation code is coming soon. The official project is now accessible; paper plots and the code-repository description do not establish an implementation/checkpoint/dataset release."},"evidence":[{"url":"https://arxiv.org/abs/2608.01102","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://arxiv.org/html/2608.01102","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Requires a tactile reference and contact estimation.","Model count/size depend on the chosen backbone."],"verified":"2026-10-19","releaseByCollection":{"policy":{"code":"Not released","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The current author code repository contains only README.md and explicitly states training/evaluation code is coming soon. The official project is now accessible; paper plots and the code-repository description do not establish an implementation/checkpoint/dataset release."}},"relatedIds":[]},{"id":"sparse-meets-dense","name":"Sparse Meets Dense","title":"Sparse Meets Dense: Correspondence Guided Robotic Manipulation with Rigid-Deformable Interactions","date":"2026-08-02","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Sparse structural keypoints and dense deformable correspondences jointly constrain rigid-deformable robot interactions.","institution":"Peking University","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Bimanual"],"links":{"paper":"https://arxiv.org/abs/2608.01083","project":"https://sparse-meets-dense.github.io/"},"policy":{"family":"Geometric transfer","baseModel":"Hybrid geometric correspondence and constraint planning. VLM task decomposition, sparse interaction keypoints, learned dense garment correspondence and constraint optimization.","modelSize":"Not reported","inputs":"RGB demonstration sequence, image annotated with sparse keypoints and text task; RGB-D rigid/deformable geometry and tracked points at execution.","outputs":"Stage-wise keypoint constraints and robot motion plans for rigid-deformable manipulation.","trainingData":"Contrastive dense-correspondence training across garment deformations/topologies and few task demonstrations; exact aggregate count not reported.","trainingTime":"Not reported","trainingHardware":"RTX 4090 workstation with Intel i7 and 64 GB RAM for inference/planning; offline training hardware not reported.","controlRate":"Not reported","trainingMethod":"Offline dense representation learning plus coarse-to-fine refinement from online correspondence failures; transfer stages via sparse structural constraints.","evaluation":"Simulation and real dressing/container interaction tasks including inserting a hanger into a shirt, racket into bag and books into backpack."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No author-linked implementation was verified in the inspected primary manuscript/project. This is an evidence gap, not proof that no implementation exists."},"evidence":[{"url":"https://arxiv.org/abs/2608.01083","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://sparse-meets-dense.github.io/","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."}],"caveats":["Requires annotated sparse candidate keypoints and task decomposition; not annotation-free imitation.","Dense correspondence learning and online refinement are distinct from retraining a complete action policy."],"verified":"2026-10-17"},{"id":"cwm-rsi","name":"Criticality World Model RSI","title":"Policy-Level Recursive Self-Improvement for Embodied AI with a Criticality World Model","date":"2026-07-30","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Targets risky scenarios with a learned criticality model, then trains and selectively deploys specialist robot policies.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Legged robot"],"links":{"paper":"https://arxiv.org/abs/2607.28251"},"policy":{"family":"Hybrid","baseModel":"Criticality prediction model and domain-specific RL, behavioral-cloning or VLA policies, with nominal / specialist gating.","modelSize":"Not reported","inputs":"Scenario descriptions, rollout outcomes and domain-specific robot observations; scenario selection uses the published perturbation / collection interfaces.","outputs":"Criticality scores, targeted data selections, updated specialist policies and nominal-versus-specialist execution choices.","trainingData":"Risk-guided data collection across five domains, with scenario-probability weighting / resampling to correct collection shifts.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Policy weights are updated using domain-specific RL / BC / VLA objectives. The criticality model steers data collection; this is not an online LLM coding agent.","evaluation":"Five embodied domains, including quadruped locomotion, ManiSkill manipulation and VLA adaptation under perturbations."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2607.28251","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["Finite-buffer normalized resampling is only approximately unbiased; the paper distinguishes it from the exact fixed-integrand importance identity.","Privileged perturbation / scenario interfaces are part of the data-collection setting."],"verified":"2026-10-17"},{"id":"semancorr","name":"SemAnCorr","title":"SemAnCorr: Semantic Anchored Correspondence for Zero-Shot Manipulation Skill Transfer","date":"2026-07-30","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Joint semantic-anchor and pose optimization establishes coherent dense surface correspondence for transferring object-centric skills.","institution":"Carnegie Mellon University / Vanderbilt University / University of California, Berkeley","venue":"Preprint","modalities":["RGB","Depth","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2607.28382","project":"https://semancorr.github.io/","code":"https://github.com/semancorr/SemAnCorr"},"policy":{"family":"Geometric transfer","baseModel":"Training-free functional-map skill transfer. SigLIP2 semantic features, joint semantic-anchor/pose optimization and Laplace-Beltrami functional maps.","modelSize":"Not reported","inputs":"Source/target connected surface meshes and demonstration-relative interaction frame. Physical meshes are reconstructed with SAM3D from RGB-D observations and aligned to measured depth.","outputs":"Transferred local interaction frames and object-centric robot skill trajectories.","trainingData":"No task-specific learned model; PartNet-Mobility correspondence benchmark and one-shot physical demonstrations.","trainingTime":"No task-specific training.","trainingHardware":"Runtime benchmark on RTX 4090; not a training-resource claim.","controlRate":"Approximately six seconds per object-pair correspondence computation; this is preprocessing latency rather than robot servo Hz.","trainingMethod":"Optimize semantically consistent anchors, then propagate correspondence over surface geometry.","evaluation":"Dense correspondence on PartNet-Mobility and five physical skill-transfer tasks, ten trials per task."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"No repository-wide license found in inspected tree","notes":"Author-linked repository and actual README/tree inspected; weights/data are tracked separately. No installation or reproduction attempted."},"evidence":[{"url":"https://arxiv.org/abs/2607.28382","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://semancorr.github.io/","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://github.com/semancorr/SemAnCorr","label":"Official code","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."}],"caveats":["Requires a single connected mesh and accurate mesh-to-workspace alignment.","The policy includes an explicit reconstruction/correspondence stage, not only raw-image inference."],"verified":"2026-10-17"},{"id":"tacwam","name":"TacWAM","title":"TacWAM: Anchor-Guided World Action Model with Mechanics-Aware Tactile Prediction","date":"2026-07-30","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Anchors multimodal attention to current observations and uses mechanics-aware tactile prediction as a parallel training objective.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2607.28391"},"policy":{"family":"World-action model","baseModel":"Wan2.2-TI2V-5B MoT; aligned ActionDiT action/tactile experts","modelSize":"5B video backbone; action/tactile widths1024; total not reported","inputs":"Overhead/wrist RGB, Xense tactile images, joint state and language","outputs":"Joint-position action chunks; future tactile supervision does not condition actions in finalized attention mask","trainingData":"Not reported","trainingTime":"Two stages, 20 epochs each; wall-clock not reported","trainingHardware":"Stage2: 8 NVIDIA A800-SXM4-80GB","controlRate":"Sensing/control aligned30 Hz; model-query rate not separately reported","trainingMethod":"Anchor-Guided Transformer with mechanics-aware force/deformation prediction","evaluation":"Real Agilex Piper contact-rich manipulation; 20 trials per task; single training checkpoint"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."},"evidence":[{"url":"https://arxiv.org/abs/2607.28391","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://arxiv.org/html/2607.28391","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Future tactile is predictive co-training, not an action-conditioned consequence rollout or direct future conditioning.","Training-seed variation is not reported."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."}},"relatedIds":[]},{"id":"hero","name":"HERO","title":"Practice Makes Policies: Bootstrapping and Consolidating Robotic Capabilities from Zero Human Demonstrations","date":"2026-07-29","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Bootstraps robot skills through heuristic reasoning, exemplar reuse and learned reflex policies, consolidating self-collected practice into faster execution.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2607.26809","project":"https://hero-agent.github.io"},"policy":{"family":"Hybrid","baseModel":"Gemini 3 Flash for VLM reasoning; a three-layer system combines heuristic primitives, retrieved successful exemplars and learned visuomotor policies.","modelSize":"Not reported","inputs":"Task, RGB observations / scene reconstruction, robot state, exemplar library and learned-policy observations.","outputs":"L1 geometric primitive actions, L2 exemplar-conditioned execution or L3 learned visuomotor action chunks, with fallback between layers.","trainingData":"Zero human demonstrations in the reported bootstrapping protocol; 30 successful trajectories per task support reflex-policy learning, with 664 forward / reverse subtask episodes reported.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Collected robot trajectories are recorded at 15 Hz; learned-policy termination checks action convergence over recent chunks.","trainingMethod":"Frozen reasoning model drives autonomous practice; successful experiences populate exemplars and update learned reflex-policy weights.","evaluation":"Real Franka manipulation and multi-stage task execution with layer / fallback ablations."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2607.26809","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["Zero human demonstrations does not mean zero policy training or zero interaction data.","The learned-reflex stage is a trained visuomotor controller, separate from the frozen VLM.","L1 reconstructs 3D geometry from RGB and calibrated extrinsics with Depth-Anything-3. This inferred depth does not establish measured depth as an input modality."],"verified":"2026-10-17"},{"id":"kinrt","name":"KinRT","title":"Route by Kinematics, Act by Observation: Kinematics-Supervised Expert Routing in MoE-Augmented VLA","date":"2026-07-29","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Supervises MoE expert routing with action-trajectory kinematic clusters that are predicted from observations at deployment.","institution":"Tsinghua University / Pengcheng Lab","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2607.26807","project":"https://gleeacast.github.io/KinRT/","code":"https://github.com/gleeacast/KinRT","weights":"https://huggingface.co/Gleez/kinrt-robodojo-full35-a800-60k"},"policy":{"family":"VLA","baseModel":"π0.5-style frozen SigLIP/PaliGemma-2B prefix; Gemma-300M action suffix with MoE extensions","modelSize":"2B language component + 300M action base + MoE extensions; complete total not reported","inputs":"RGB, language and current robot state; no future kinematic trajectory needed at deployment","outputs":"Continuous flow action chunks; training clusters use50-step action/velocity trajectories","trainingData":"Eight selected RoboTwin tasks and DIYRobot real tasks; later RoboDojo checkpoint released","trainingTime":"Paper controlled comparisons10k optimization steps, batch 32; wall-clock not reported","trainingHardware":"2 × NVIDIA L20 for paper controlled comparisons","controlRate":"Not reported; flow integration/action horizon do not determine measured feedback rate","trainingMethod":"Trajectory/velocity clustering → router supervision → observation-conditioned MoE flow policy","evaluation":"RoboTwin controlled task subset and DIYRobot; released RoboDojo integration/checkpoint"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Not reported","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2607.26807","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/gleeacast/KinRT","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."}],"caveats":["Kinematic signals supervise training; the deployed router uses observations, avoiding future-action-label leakage."],"verified":"2026-10-17"},{"id":"architect","name":"ARCHITECT","title":"A Few Words Go a Long Way: Language Guided Robot Policy Synthesis","date":"2026-07-26","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Uses human language feedback and execution traces to synthesize modular robot programs and retain corrected behaviors in a reusable skill library.","institution":"University of Washington; Microsoft Research; Massachusetts Institute of Technology","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2607.23784","project":"https://robo-architect.github.io/","code":"https://github.com/robo-architect/architect-franka"},"policy":{"family":"Robot agent","baseModel":"Claude Opus 4.6 orchestrator; perception tools include a GPT-5.4 visual-question-answering call; cuRobo supplies motion generation.","modelSize":"Not reported","inputs":"Task instruction, wrist / scene observations including stereo-derived geometry, tool returns and localized human corrections.","outputs":"Executable modular policy programs invoking perception, grasp / placement tools and local motion primitives.","trainingData":"No robot-specific weight-training data; human corrections and execution traces guide code / skill-library refinement.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen coding model; interactive policy synthesis, trace-grounded corrections and persistent skill-library reuse.","evaluation":"Eight real-robot tasks and challenge categories; multiple policy / feedback conditions yield 400 reported real rollouts."},"release":{"code":"Available","weights":"Not applicable","data":"Not verified","license":"MIT (code); model, dependency and dataset terms are separate.","notes":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms."},"evidence":[{"url":"https://arxiv.org/abs/2607.23784","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/robo-architect/architect-franka/main/README.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."},{"url":"https://raw.githubusercontent.com/robo-architect/architect-franka/main/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Code-license text checked directly; this license is not automatically assigned to model weights or third-party data."}],"caveats":["Human correction is an explicit acquisition input and must be counted separately from final execution.","cuRobo and language-parameterized perception / grasp modules supply substantial execution capability."],"verified":"2026-10-17"},{"id":"n0-twam","name":"N₀-TWAM","title":"$N_0$-TWAM: Scaling Tactile-Native World-Action Model for Contact-Rich Manipulation","date":"2026-07-26","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"A tactile-native asymmetric mixture-of-transformers jointly predicts video/contact futures and robot actions at foundation-model scale.","institution":"NeoteAI / Fudan TEAI","venue":"Preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2607.23783","project":"https://research.neoteai.com/n0-twam/","code":"https://github.com/neoteai/N0-TWAM"},"policy":{"family":"World-action model","baseModel":"Wan2.2-TI2V-5B video expert warm-started from LingBot-VA; NeoForce DINOv2-ViT-B encoder","modelSize":"7.2B total; ≈5B video expert component","inputs":"Multi-view RGB, instruction, proprioception, tactile streams and rolling history","outputs":"Future video/tactile latents and bimanual delta end-effector actions; training horizon 24, evaluation horizon 16","trainingData":">30k h NeoData; six embodiments, 450 tasks; NeoForce pretraining: 20k demos / 30 tasks","trainingTime":"WAM: 30k steps (≈2.2 epochs); NeoForce: 100k; task adaptation generally 1k steps","trainingHardware":"WAM pretraining: 128 H800; NeoForce: 8 A100","controlRate":"Not reported","trainingMethod":"Flow-matching causal cascade; observed tactile conditioning and predicted contact; contact-event task staging","evaluation":"UniVTAC, NeoSim and real contact-rich/long-horizon tasks"},"release":{"code":"Available","weights":"Available","data":"Partial","license":"Not verified","notes":"Official repository links weights and post-training/inference; full pretraining corpus availability not established."},"evidence":[{"url":"https://arxiv.org/abs/2607.23783","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/neoteai/N0-TWAM","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2607.23783","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Prediction streams and observed-tactile conditioning are separate pathways.","Large-scale pretraining differs from task-demonstration-only settings."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Partial","license":"Not verified","notes":"Official repository links weights and post-training/inference; full pretraining corpus availability not established."}},"relatedIds":[]},{"id":"n0-vtla","name":"N₀-VTLA","title":"$N_0$-VTLA: Scaling Vision-Tactile-Language-Action Model with Latent Tactile Tokens","date":"2026-07-26","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Adds latent tactile-change tokens to a PaliGemma-based action expert and studies tactile-grounded offline policy refinement.","institution":"NeoteAI / Fudan TEAI","venue":"Preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2607.23782","project":"https://research.neoteai.com/n0-vtla/","code":"https://github.com/neoteai/N0-VTLA","weights":"https://huggingface.co/NeoteAI/n0-vtla-base"},"policy":{"family":"VLA","baseModel":"π0.5-style PaliGemma VLA; frozen DINOv2 tactile encoder","modelSize":"Not reported","inputs":"Multi-view RGB, language, discrete robot state and contact-difference images","outputs":"50-step action chunks in a 32D padded canonical container","trainingData":"NeoData multi-platform corpus; companion source reports >30k h, six embodiments and 450 tasks","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Three-stage latent tactile grounding; optional ALTER offline policy learning","evaluation":"UniVTAC eight tasks plus NeoSim twelve tasks; NeoReal nine real tasks"},"release":{"code":"Available","weights":"Available","data":"Partial","license":"CC BY-SA 4.0 repository; PaliGemma/Gemma-derived weights additionally subject to Gemma terms","notes":"Base and selected task checkpoints plus adaptation/serving code available; full pretraining corpus is not verified public."},"evidence":[{"url":"https://arxiv.org/abs/2607.23782","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/neoteai/N0-VTLA","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2607.23782","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Serving contract executes the full 50-step chunk before a new request; it is not fast in-chunk tactile feedback.","Most tactile pathway grounding is predictive latent supervision."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Partial","license":"CC BY-SA 4.0 repository; PaliGemma/Gemma-derived weights additionally subject to Gemma terms","notes":"Base and selected task checkpoints plus adaptation/serving code available; full pretraining corpus is not verified public."}},"relatedIds":[]},{"id":"vitacworld","name":"ViTacWorld","title":"ViTacWorld: Scaling Visuo-Tactile World Models for Contact-Rich Robot Manipulation","date":"2026-07-24","year":2026,"collections":["policy"],"categories":["tactile-foundation","tactile-representation"],"resourceType":"model","summary":"An action-conditioned world model generates aligned RGB and tactile rollouts for policy training and imagined evaluation.","institution":"ShanghaiTech University / InstAdapt","venue":"Preprint","modalities":["RGB","Tactile"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2607.22530","project":"https://vitacworld.github.io/"},"release":{"code":"Not released","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Official project explicitly labels code coming soon. No downloadable model or dataset release verified."},"evidence":[{"url":"https://arxiv.org/abs/2607.22530","label":"Original paper and reviewed full text","fields":["title","date","institution","summary","model","caveats"],"note":"Full primary text, including available supplementary implementation details; first public date retained separately from reviewed revision."},{"url":"https://vitacworld.github.io/","label":"Official author project","fields":["links","release"],"note":"Current project links and release claims checked independently of paper prose."}],"caveats":["Supporting predictive model, not an executable action policy; π0.5 and ACT supply actions for rollouts and are trained separately.","World-model prediction uses RGB/tactile views and provided action sequences; downstream policy proprioception and language are not automatically model observation channels.","Imagined evaluation uses human task/plausibility judgment; aggregate correlation does not prove trial-level agreement or exact success calibration."],"verified":"2026-10-17","model":{"role":"Action-conditioned visuo-tactile rollout generator for downstream policy augmentation and evaluation.","family":"Diffusion world model","baseModel":"Pretrained action-conditioned robot video world-model prior; exact checkpoint Not reported.","modelSize":"Not reported.","inputs":"Current external/wrist RGB and image-like tactile views, view-presence mask and a provided sequence of relative end-effector motions with gripper commands.","outputs":"Temporally aligned future RGB and tactile observations; does not generate robot actions itself.","trainingMethod":"Latent denoising with view-aware modulation and cross-view attention; real/simulation pretraining followed by target-domain expert and policy-rollout fine-tuning.","trainingData":">16k public real trajectories and >5k task-aligned Isaac Sim trajectories, sampled about 2:1. Target adaptation uses 300 expert demonstrations total across four tasks (120+50+62+68), plus 50 real policy rollouts per task. Synthetic augmentation selects 200 successful trajectories/round, yielding 500 then 700 downstream training trajectories.","trainingTime":"30k pretraining and 7k fine-tuning steps; wall-clock time Not reported.","trainingHardware":"Not reported.","inference":"13-frame windows sampled at 15 Hz during training; generator throughput Not reported. Isaac Sim version Not reported.","evaluation":"Franka Panda / Robotiq 2F-85 with Xense tactile: Charger Plugging, Cucumber Peeling, U-Block Insertion, Cuboid Insertion. Three downstream policies/stages use 50 real trials/task. Imagined evaluation compares 600 matched real/imagined pairs aggregated into 12 success-rate points."}},{"id":"pigey","name":"Pigey / Physical Agency","title":"Addressing the Orchestration Gap in Generalist Robots via Physical Agency","date":"2026-07-23","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Routes subgoals through complementary frozen VLA and geometric-planning backends, verifying outcomes and recovering through a closed physical-agent loop.","institution":"Princeton University; Together AI","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2607.21725","project":"https://lianegalanti.github.io/Pigey/","code":"https://github.com/lianegalanti/Pigey"},"policy":{"family":"Hybrid","baseModel":"Frontier VLM orchestrator; real experiments use Claude Opus 4.7. Motor backends are frozen π0.5 and TiPToP-style grounding / TAMP.","modelSize":"Not reported","inputs":"Task, visual / stereo-depth observations, known-object lists, robot state and execution / verification feedback.","outputs":"Parameterized skill / policy calls selected for each subgoal, with success checks and recovery.","trainingData":"No additional robot data collection or policy post-training for the orchestrator experiment; existing motor backends retain their prior training.","trainingTime":"Not reported","trainingHardware":"No new-policy training is claimed. Real perception / policy serving uses one RTX 5090 32 GB; simulation uses B300 GPU servers.","controlRate":"Not reported","trainingMethod":"Frozen models / motor backends; an observe–reason–act–verify loop selects suitable tools and corrects execution.","evaluation":"LIBERO-Pro and challenging real Franka Research 3 tasks; five physical trials per task and ten simulation trials per task / reasoner."},"release":{"code":"Available","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms."},"evidence":[{"url":"https://arxiv.org/abs/2607.21725","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/lianegalanti/Pigey/main/README.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."}],"caveats":["TiPToP grounding / grasp planning and the learned VLA are substantive execution components.","The physical and simulation experiments use different reasoning-model and hardware configurations."],"verified":"2026-10-17"},{"id":"host","name":"HOST","title":"HOST: Robots Acquire Manipulation Skills in Seconds from a Single Human Video","date":"2026-07-22","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Aligned human video drives progress localization, future robot observation prediction and bimanual action generation without per-task fine-tuning.","institution":"Beijing Institute of Technology / X SQUARE ROBOT / Tsinghua University","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Real world"],"embodiments":["Bimanual"],"links":{"paper":"https://arxiv.org/abs/2607.20033","project":"https://host-site.host-robotics.workers.dev/","code":"https://github.com/CGuangyan-BIT/HOST","weights":"https://huggingface.co/Guangyan/HOST"},"policy":{"family":"World-action model","baseModel":"One-shot human-video-conditioned world-action policy. Wan2.2-TI2V-5B video expert, 30-layer action expert and progress/localization head. Qwen3-VL-Embedding-8B alignment module is used offline.","modelSize":"5B video and 8B offline alignment components; total deployed parameter count not reported.","inputs":"Single human RGB demonstration window, three live robot RGB views and 20D bimanual proprioception. Offline alignment network is absent from deployment.","outputs":"32-step chunks of 20D bimanual Cartesian position, 6D rotation and gripper aperture; also predicts localization and future observations.","trainingData":"193,462 robot trajectories spanning 229 tasks; Stage 2 uses 5,847 additional human demonstrations paired with same-task robot trajectories.","trainingTime":"Alignment: 10,000 steps; robot pretraining: 500,000; human adaptation: 100,000. Wall-clock training duration not reported.","trainingHardware":"64 GPUs; model type not reported in training tables. Effective batch 256 for alignment, 128 for both diffusion stages.","controlRate":"32 actions fully executed per query; recorded action stream 32 Hz. Reported 29 seconds is average skill acquisition from a demonstration, not inference latency.","trainingMethod":"Self-supervised temporal alignment constructs prediction targets; autoregressive flow matching predicts localization, visual future and actions sequentially; parameters frozen on novel tasks.","evaluation":"50 novel manipulation tasks with 20 trials each, on two ARX R5 arms with parallel grippers."},"release":{"code":"Partial","weights":"Available","data":"Not verified","license":"No repository-wide license; policy_training subtree carries inherited Fast-WAM MIT terms","notes":"Model-training/offline processing components and an author-linked model release are present. Complete robot deployment and data release are not established. Nested license does not cover the whole repository."},"evidence":[{"url":"https://arxiv.org/abs/2607.20033","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://host-site.host-robotics.workers.dev/","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://github.com/CGuangyan-BIT/HOST","label":"Official code","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://raw.githubusercontent.com/CGuangyan-BIT/HOST/main/policy_training/LICENSE","label":"Actual source license file","fields":["release.license"],"note":"Scope limited to the file/repository subtree carrying these terms, not paper figures or external models/data."}],"caveats":["The acquisition-speed comparison includes supplying a demonstration and is distinct from control frequency.","Temporal alignment is trained offline on paired tasks; this is not a training-free robot system."],"verified":"2026-10-17"},{"id":"worldscape-policy2","name":"WorldScape Policy 2.0","title":"WorldScape Policy 2.0: Empowering Steerable World Action Modeling with Reasoning-Augmented Memory and In-Context Learning","date":"2026-07-21","year":2026,"collections":["policy"],"categories":["generalist-foundation","agent-rsi-icl"],"resourceType":"policy","summary":"Combines event-level progress memory, short visual history and latent subgoal reasoning with controllable video-action prediction.","institution":"Manifold AI","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2607.18840","project":"https://manifoldai-research.github.io/WorldScape-Policy/","code":"https://github.com/manifoldai-research/WorldScape-Policy","weights":"https://huggingface.co/manifoldai-research/WorldScape-Policy-2"},"policy":{"family":"World-action model","baseModel":"Wan2.2-5B video/action DiTs, Qwen3-VL-4B latent reasoner and T5 instruction conditioning.","modelSize":"5B video backbone and 4B reasoning component; complete aggregate not reported.","inputs":"RGB views, language, state and memory; goal images or reference videos can persist as visual prefill for ICL.","outputs":"Paper uses a raw 20D bimanual end-effector representation. Released RoboTwin adapter uses absolute joint control with a 24-step horizon.","trainingData":"ManipEvent-5M: nearly five million segments with actions, event captions, goal images and video references. RoboTwin uses clean plus randomized posttraining data.","trainingTime":"50k posttraining steps, batch 128; full wall-clock duration not reported.","trainingHardware":"Not reported","controlRate":"Not reported as a common deployment frequency.","trainingMethod":"Event-grounded pretraining, semantic forcing for latent planning and interactive posttraining; reasoning-augmented short/long-term memory.","evaluation":"50 RoboTwin tasks, 100 trials per task/setting; physical AgileX planning, memory, cross-embodiment ICL and sequential control, 20 trials per task."},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 original code and current WorldScape-Policy-2 model-card grant; upstream backbones/assets/data retain separate terms.","notes":"Current model inventory contains base/midtrain and RoboTwin2 post-training safetensor shards, including clean-to-random and auto variants with checkpoint manifests/configs/provenance. These are WorldScape Policy 2.0 artifacts, not a public R²SI or 2.5 release."},"evidence":[{"url":"https://arxiv.org/abs/2607.18840","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/manifoldai-research/WorldScape-Policy","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://manifoldai-research.github.io/WorldScape-Policy/","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."},{"url":"https://huggingface.co/manifoldai-research/WorldScape-Policy-2","label":"Official current source / artifact inventory — 21 October 2026","fields":["release","links","policy"],"note":"October 21 inventory of 82 files includes c2r_auto checkpoint shards and manifests. Version identity remains2.0; no R²SI release is inferred. This clarifies the existing version-specific artifact scope; file inventory does not establish a new checkpoint release on the review date."}],"caveats":["The paper’s raw 20D end-effector interface differs from the released absolute-joint adapter.","Reference prompts and memory do not imply inference-time weight updates."],"verified":"2026-10-21","relatedIds":["worldscape-r2si"]},{"id":"roboharness","name":"RoboHarness","title":"RoboHarness: Memory-Driven Orchestration of Heterogeneous Robot Policies for Long-Horizon Planning","date":"2026-07-20","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Routes heterogeneous robot policies using execution memory and prepares their handoffs with memory-derived bridge states.","institution":"Huawei Noah’s Ark Lab; University of British Columbia; University of Toronto; McGill University; Huawei 2012 Labs","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2607.18060"},"policy":{"family":"Hybrid","baseModel":"Codex / GPT-5.5 orchestration with π0.5, RL-post-trained OpenVLA-OFT, TAMP and additional independent skills; SigLIP2, DINOv2 and BGE encoders support context assessment.","modelSize":"Not reported","inputs":"Task, current visual / robot observations, policy-specific execution memory and capability uncertainty.","outputs":"Policy selection, memory-bridge handoff actions, execution calls and online repairs to orchestration code.","trainingData":"Existing LIBERO policy checkpoints plus task-specific π0.5 fine-tuning for RMBench / BEHAVIOR-1K; execution traces populate memory.","trainingTime":"Task-specific π0.5 fine-tuning uses a 30,000-step schedule with global batch size 256; orchestration-code refinement is separate.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen heterogeneous policy composition without joint retraining; a lightweight progress estimator is learned from memory, and GPT-5.5 repairs orchestration code online.","evaluation":"Five public benchmarks and 135 real-robot trials, including perturbation / long-horizon and policy-handoff studies."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The original paper is public; code, model and dataset release scopes are verified separately where established. The paper includes trained component / policy variants; a complete method-specific checkpoint release has not been established."},"evidence":[{"url":"https://arxiv.org/abs/2607.18060","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."}],"caveats":["Distinct paper and system from RoboHarn-Evo and Robo-Harness K1; similar names do not denote aliases.","No joint retraining does not mean no component training: benchmark-specific policies and a progress estimator are prepared separately."],"verified":"2026-10-17"},{"id":"rynnbrain-11-vla","name":"RynnBrain 1.1 / RynnBrain-VLA","title":"RynnBrain 1.1: Towards More Capable and Generalizable Embodied Foundation Model","date":"2026-07-20","year":2026,"collections":["policy"],"categories":["rgbd-3d","generalist-foundation"],"resourceType":"policy","summary":"Native 3D/contact-point grounding supports a cross-embodiment VLA across humanoid and dexterous bimanual robots.","institution":"DAMO Academy, Alibaba Group; Hupan Lab","venue":"arXiv preprint","modalities":["RGB","Language","Proprioception"],"environments":["Real world"],"embodiments":["Humanoid","Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2607.17977","project":"https://alibaba-damo-academy.github.io/RynnBrain/","code":"https://github.com/alibaba-damo-academy/RynnBrain","weights":"https://huggingface.co/collections/Alibaba-DAMO-Academy/rynnbrain-11"},"release":{"code":"Partial","weights":"Partial","data":"Not verified","license":"Apache-2.0 for repository code; pretrained models, data and figure rights remain separate","notes":"Public 1.1 brain checkpoints and pretraining/evaluation tools; task-specific VLA-head checkpoints/deployment implementation not independently identified."},"evidence":[{"url":"https://arxiv.org/abs/2607.17977","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://alibaba-damo-academy.github.io/RynnBrain/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/alibaba-damo-academy/RynnBrain","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/alibaba-damo-academy/RynnBrain/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/alibaba-damo-academy/RynnBrain/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"133 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."},{"url":"https://huggingface.co/collections/Alibaba-DAMO-Academy/rynnbrain-11","label":"Official weights release destination","fields":["release.weights"],"note":"Destination linked by the official project/repository; component release scope is retained in release notes."}],"caveats":["Contact-point prediction is an image-based grounding target, not a tactile input stream."],"verified":"2026-10-17","policy":{"family":"VLA","baseModel":"Qwen3.5-derived embodied brain with single-stream flow-matching DiT action adaptation and embodiment masking.","modelSize":"Foundation 2B / 9B / 122B-A10B; do not assign the largest model to every VLA experiment","inputs":"RGB views, language and robot proprioception; no skin tactile observation claimed.","outputs":"Unified padded action chunks with embodiment-specific masks.","trainingMethod":"Embodied 3D/contact pretraining followed by joint multi-task and multi-embodiment robot finetuning.","trainingData":"Embodied image/video grounding corpus plus separate physical robot demonstrations.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Unitree G1, Astribot-S1 and Tianji-Wuji; long-horizon and cross-embodiment task comparisons.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"robottt","name":"RoboTTT","title":"RoboTTT: Context Scaling for Robot Policies","date":"2026-07-16","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Introduces fast-weight test-time training into a robot action model to compress long histories and adapt behavior from context.","institution":"NVIDIA; Stanford University; University of Texas at Austin","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2607.15275","project":"https://research.nvidia.com/labs/gear/robottt/"},"policy":{"family":"In-context policy","baseModel":"GR00T N1.7 with one Test-Time Training layer added to each of its 16 action DiT layers; each fast model is a two-layer MLP.","modelSize":"The action DiT grows from 538M to 690M parameters; this excludes the full vision-language backbone.","inputs":"Long sensorimotor histories, current visual observations / robot state and optional human-video or robot demonstration context, up to 8,000 timesteps.","outputs":"Continuous robot actions from the context-conditioned action model; fast weights encode the preceding trajectory.","trainingData":"Context-scaled pretraining plus task-specific post-training. Three real tasks have 8, 6 and 5 hours of collected data respectively, with online correction / DAgger-style distillation.","trainingTime":"30,000 pretraining steps with gradually increased context length; post-training has task-specific schedules.","trainingHardware":"16 NVIDIA GB200 GPUs per pretraining run; eight GB200 GPUs per post-training run; RTX 5090 inference workstation.","controlRate":"30 Hz real-robot control in the reported deployment; 8,000 timesteps cover roughly five minutes at this rate.","trainingMethod":"Slow model weights are learned offline; fast weights are updated by gradient descent during both training and inference. Pretraining freezes the original GR00T components; post-training fine-tunes all parameters.","evaluation":"Three long-horizon real assembly tasks, including a ten-stage task; human-video one-shot imitation and on-the-fly improvement studies."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2607.15275","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["Inference updates fast weights, so this is not a strictly frozen-parameter attention-only ICL policy.","The 690M count is for the augmented action model, not the complete foundation policy."],"verified":"2026-10-17"},{"id":"xiaomi1","name":"Xiaomi-Robotics-1","title":"Xiaomi-Robotics-1: Scaling Vision-Language-Action Models with over 100K Hours of Real-World Trajectories","date":"2026-07-16","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Scales UMI pretraining and robot post-training for language-conditioned mobile manipulation in unfamiliar environments.","institution":"Xiaomi Robotics","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2607.15330","project":"https://robotics.xiaomi.com/xiaomi-robotics-1.html","code":"https://github.com/XiaomiRobotics/Xiaomi-Robotics-1","weights":"https://huggingface.co/XiaomiRobotics/Xiaomi-Robotics-1-5B"},"policy":{"family":"VLA","baseModel":"Qwen3-VL with MoT flow action transformer","modelSize":"Paper variants: 2.6B total(2.1B VLM + 470M DiT), 5.1B(4.4B + 604M), 10.5B(8.8B + 1.5B); released5B asset","inputs":"RGB observations, imperative task language and proprioceptive state","outputs":"Relative delta end-effector action chunks; mobile robot commands depend on embodiment; 5 flow integration steps","trainingData":"Over 100k hours UMI real-world trajectories; about 10k hours post-training mixture including 7,200+ robot hours and 1,000+ annotated UMI hours","trainingTime":"Not reported; data collection duration is distinct from training wall-clock","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Large-scale trajectory pretraining with state-transition captions → robot/imperative-language post-training and auxiliary VLM action supervision","evaluation":"Zero-shot home mobile manipulation and four difficult adapted tasks; 10-minute suitcase-packing demonstration"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2607.15330","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/XiaomiRobotics/Xiaomi-Robotics-1","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."},{"url":"https://raw.githubusercontent.com/XiaomiRobotics/Xiaomi-Robotics-1/main/LICENSE","label":"Repository license file","fields":["release.license"],"note":"Code scope; model, data and third-party component terms can differ."}],"caveats":["The 100k-hour corpus is mainly UMI collection; post-training adds target robot data.","Dataset scale is not a disclosed training-compute budget."],"verified":"2026-10-17","relatedIds":["rex-m1-preview"]},{"id":"gigaworld-policy05","name":"GigaWorld-Policy-0.5","title":"GigaWorld-Policy-0.5: A Faster and Stronger WAM Empowered by AutoResearch","date":"2026-07-15","year":2026,"collections":["policy"],"categories":["generalist-foundation","agent-rsi-icl"],"resourceType":"policy","summary":"Separates visual and action experts for faster action-only decoding and uses automated pilot training to search configurations.","institution":"GigaAI / Tsinghua University","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2607.13960","project":"https://open-gigaai.github.io/giga-world-policy/","code":"https://github.com/open-gigaai/giga-world-policy","weights":"https://huggingface.co/open-gigaai/Giga-World-Policy-0.5"},"policy":{"family":"World-action model","baseModel":"GigaWorld-1 visual initialization; MoT visual expert width 3072 and action expert width 1024.","modelSize":"Exact total not reported; expert hidden widths are not parameter counts.","inputs":"RGB observations, language and robot state.","outputs":"48-action chunks following the original model; action-only inference.","trainingData":"GigaWorld-1 initialization from over 10k video hours; robot pretraining on 2k hours of filtered public/internal data, followed by target-task posttraining.","trainingTime":"Wall-clock duration not reported. AutoResearch uses 1k-step pilots; the fruit study has 930 episodes in total, with 300 training and 30 validation episodes.","trainingHardware":"Training hardware not reported; A100 and RTX 4090 inference settings are separate.","controlRate":"189 ms on A100 with compilation; 110 ms on RTX 4090 Python deployment, or 85 ms with C++ deployment.","trainingMethod":"Mixed action-conditioned world modeling and WAM pretraining; MoT action-only path; agent-assisted configuration search.","evaluation":"Physical multi-step tasks including Tableware Arrangement and Food Heating; architecture, pretraining and AutoResearch ablations."},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Not verified; pretrained backbones and data retain separate terms","notes":"Availability concerns author-linked artifacts; no training or installation reproduced."},"evidence":[{"url":"https://arxiv.org/abs/2607.13960","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/open-gigaai/giga-world-policy","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://open-gigaai.github.io/giga-world-policy/","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."}],"caveats":["AutoResearch searches training configurations; it does not establish unbounded recursive self-improvement.","The 85 ms result uses C++ on RTX 4090; the 189 ms result uses the compiled A100 setting."],"verified":"2026-10-17"},{"id":"tactidex","name":"TactiDex","title":"TactiDex: A Real-World Tactile-Guided Benchmark for Human-Like Dexterous Manipulation","date":"2026-07-10","year":2026,"collections":["benchmark"],"categories":["tactile-benchmarks","dexterous-benchmarks"],"resourceType":"dataset","summary":"Tactile-rich human hand–object interaction dataset and contact-aware human-to-robot transfer benchmark. The physical demonstration uses open-loop trajectory execution rather than online tactile feedback.","institution":"ShanghaiTech University","venue":"ACM Multimedia 2026 (accepted, official project)","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world","Simulation"],"embodiments":["Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2607.09190","project":"https://tactidex.github.io/"},"benchmark":{"simulator":"NVIDIA Isaac Gym / PhysX","simulatorVersion":"Exact Isaac Gym preview release Not reported.","taskCount":"757 human interaction sequences over 49 objects; 73 representative sequences in simulation transfer evaluation, not 73 semantic task types.","tasks":["Pick up Keyboard","Beaker Lid","Drink","Stamp","Book","Shovel","Call","Cook","Play PingPong","Move PorcelainCup"],"taskListScope":"Examples from official human-interaction gallery only; no complete semantic task count reported.","capabilities":["Human-like dexterity","Tool use","Bimanual contact","Tactile guidance","Cross-domain transfer"],"metrics":"Object translation/rotation tracking error, MPJPE, fingertip error, mean tactile force error, Contact F1; SRkin / SRtac; PeakSafe@3N and SafeTac@3N.","robots":"Human data collection; simulation 12-DoF Inspire model; physical two Franka Panda arms with two Inspire hands exposing 6 independent motors each.","sensors":"Human piezoresistive glove: 162 elements, 17 Hz; kinematic/pose streams 120 Hz via tracking glove and OptiTrack. Physical robot tactile sensors are available but not fed back online.","dataCollection":"Human hand–object capture with motion tracking and a tactile glove; kinematics, pressure and object poses are synchronized. Retargeted robot-policy training is downstream.","data":"757 synchronized human interaction sequences, 49 calibrated objects; 73 representative sequences selected for quantitative simulated transfer.","protocol":"TactiSkill PPO asymmetric actor-critic tracks retargeted demonstrations with tactile-guided rewards. Physical transfer retargets to six actuator commands and executes open-loop.","baselines":"Kinematic-only transfer baseline and tri-component tactile reward ablations (TactiSkill)."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not reported","notes":"Official project currently exposes manuscript and task videos; code/data download link not resolved."},"evidence":[{"url":"https://arxiv.org/abs/2607.09190","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://tactidex.github.io/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://arxiv.org/abs/2607.09190","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Official gallery examples are not a complete released task catalogue.","Dataset human tactile labels and robot deployment sensing must be distinguished.","Physical tactile sensors are explicitly not used as policy feedback; Appendix C.3 states open-loop execution. Human tactile supervision is not measured robot RGBDT feedback."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not reported","notes":"Official project currently exposes manuscript and task videos; code/data download link not resolved."}},"relatedIds":[]},{"id":"dexverse","name":"DexVerse","title":"DexVerse: A Modular Benchmark for Multi-Task, Multi-Embodiment Dexterous Manipulation","date":"2026-07-09","year":2026,"collections":["benchmark"],"categories":["dexterous-benchmarks"],"resourceType":"benchmark","summary":"Modular dexterous suite covering 100 tasks, multiple arms/hands and configurable visual variation, with a 19-task baseline study.","institution":"University of North Carolina at Chapel Hill","venue":"Preprint","modalities":["RGB","Depth","Proprioception"],"environments":["Simulation"],"embodiments":["Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2607.08751","project":"https://ycyao216.github.io/DexVerse.site/","code":"https://github.com/ycyao216/DexVerse"},"benchmark":{"simulator":"NVIDIA Isaac Lab / Isaac Sim","simulatorVersion":"Current official README: Isaac Sim 5.1.0 / Isaac Lab 2.3.2.","taskCount":"100 task definitions; baseline evaluation on 19 tasks; 3 arm models and 6 hand models","tasks":["Dexverse-GraspBleach-v0","Dexverse-GraspPan-v0","Dexverse-GraspKettle-v0","Dexverse-GraspCup-v0","Dexverse-RemoveCupFromRack-v0","Dexverse-FunctionalPourCan-v0","Dexverse-FunctionalPourMug-v0","Dexverse-FunctionalHammerStrike-v0","Dexverse-FunctionalDrillApply-v0","Dexverse-PourWineGlass-v0","Dexverse-PushSphereUpSlope-v0","Dexverse-PushSmallSphereObstacleSlope-v0","Dexverse-PushT-v0","Dexverse-PivotLargeCuboidAgainstWall-v0","Dexverse-TakeBookOffShelf-v0","Dexverse-LongHorizon-MicrowaveRetrievePlace-v0","Dexverse-LongHorizon-OvenBakeSalmon-v0","Dexverse-LongHorizon-TrashDrawerSortSimple-v0","Dexverse-LongHorizon-CookFoods-v0","Dexverse-BimanualLiftTray-v0","Dexverse-BimanualLiftBasket-v0","Dexverse-BimanualLiftCarton-v0","Dexverse-BimanualLiftDutchOven-v0","Dexverse-BimanualHandover-v0","Dexverse-PickCube-v0","Dexverse-StackCube-v0","Dexverse-RelocateSphere-v0","Dexverse-PickUpStick-v0","Dexverse-RelocateObject-v0","Dexverse-GraspTwoItems-v0","Dexverse-LongHorizon-MakeCoffee-v0","Dexverse-NutThread-v0","Dexverse-InsertPipette-v0","Dexverse-PlugCharger-v0","Dexverse-InsertPen-v0","Dexverse-GearMesh-v0","Dexverse-InsertPeg-v0","Dexverse-PickFromClutter-v0","Dexverse-PickThinObjectFromContainer-v0","Dexverse-OpenMicrowavePickUpCan-v0","Dexverse-OpenMicrowavePickUpStick-v0","Dexverse-OpenMicrowavePourMug-v0","Dexverse-OpenMicrowaveRelocateSphere-v0","Dexverse-OpenDoorPickUpCan-v0","Dexverse-OpenDoorPickUpStick-v0","Dexverse-OpenDoorPourMug-v0","Dexverse-OpenDoorRelocateSphere-v0","Dexverse-OpenDrawerPickUpCan-v0","Dexverse-OpenDrawerPickUpStick-v0","Dexverse-OpenDrawerPourMug-v0","Dexverse-OpenDrawerRelocateSphere-v0","Dexverse-LiftLidPickUpCan-v0","Dexverse-LiftLidPickUpStick-v0","Dexverse-LiftLidPourMug-v0","Dexverse-LiftLidRelocateSphere-v0","Dexverse-GraspPotPickUpCan-v0","Dexverse-GraspPotPickUpStick-v0","Dexverse-GraspPotPourMug-v0","Dexverse-GraspPotRelocateSphere-v0","Dexverse-PushButtonPickUpCan-v0","Dexverse-PushButtonPickUpStick-v0","Dexverse-PushButtonPourMug-v0","Dexverse-PushButtonRelocateSphere-v0","Dexverse-RotateKnobPickUpCan-v0","Dexverse-RotateKnobPickUpStick-v0","Dexverse-RotateKnobPourMug-v0","Dexverse-RotateKnobRelocateSphere-v0","Dexverse-TurnOnSwitchPickUpCan-v0","Dexverse-TurnOnSwitchPickUpStick-v0","Dexverse-TurnOnSwitchPourMug-v0","Dexverse-TurnOnSwitchRelocateSphere-v0","Dexverse-GraspBucketPickUpCan-v0","Dexverse-GraspBucketPickUpStick-v0","Dexverse-GraspBucketPourMug-v0","Dexverse-GraspBucketRelocateSphere-v0","Dexverse-PourMugPickUpCan-v0","Dexverse-PourMugPickUpStick-v0","Dexverse-PourMugRelocateSphere-v0","Dexverse-OpenCabinet-v0","Dexverse-LiftLid-v0","Dexverse-OpenDoor-v0","Dexverse-RotateKnob-v0","Dexverse-OpenFaucet-v0","Dexverse-OpenMicrowave-v0","Dexverse-OpenDrawer-v0","Dexverse-TurnOnSwitch-v0","Dexverse-GraspBucket-v0","Dexverse-LiftBucket-v0","Dexverse-GraspPot-v0","Dexverse-PushButton-v0","Dexverse-OpenLaptop-v0","Dexverse-SqueezeScissors-v0","Dexverse-SlideUtilityKnife-v0","Dexverse-LiftBasketHandle-v0","Dexverse-OpenStapler-v0","Dexverse-OpenFlatFolder-v0","Dexverse-OpenHuaweiPhone-v0","Dexverse-CabinetDoubleDoor-v0","Dexverse-UnscrewCap-v0","Dexverse-OpenGlasses-v0"],"taskListScope":"Complete 100 unique current registered environment IDs parsed from category package registrations; paper taxonomy grouping differs slightly from current folders.","capabilities":["Relocation","Articulated objects","Tool use","Bimanual cooperation","Non-prehensile control","Contact-rich execution","Long horizons"],"metrics":"Success rate and generalization under texture/background/light/camera perturbations; task-conditioned baseline comparison.","robots":"Franka Research 3, UR10e, xArm7 arms; Sharpa Wave, WUJI, Shadow, Inspire, Allegro and LEAP hands.","sensors":"RGB, depth, point clouds, proprioception and privileged state; no tactile stream established.","dataCollection":"VR teleoperation with Apple Vision Pro through Isaac Lab CloudXR; tracked wrist poses drive arm IK and human hand motion is retargeted to robot joints.","data":"3,180 demonstrations collected through VR teleoperation.","protocol":"Shared task definitions and configurable visual randomization; baseline comparison across 19 selected tasks, not all 100.","baselines":"Diffusion Policy, DP3, OpenVLA and π0.5."},"release":{"code":"Partial","weights":"Not verified","data":"Partial","license":"BSD-3-Clause code; assets/third-party terms separate","notes":"Official roadmap distinguishes initial task/assets release, teleoperation, baseline code, full Shadow Hand dataset and cross-embodiment artifacts; inspect actual available files."},"evidence":[{"url":"https://arxiv.org/abs/2607.08751","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://ycyao216.github.io/DexVerse.site/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/ycyao216/DexVerse","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://github.com/ycyao216/DexVerse/blob/main/LICENSE","label":"Official source","fields":["release.license"],"note":"License text read directly; code license does not automatically cover models, data or simulator."},{"url":"https://github.com/ycyao216/DexVerse/tree/main/source/dexverse/dexverse/tasks/config","label":"Official source","fields":["benchmark.tasks"],"note":"Read static register_env calls across all category __init__.py files, without executing simulator code."},{"url":"https://arxiv.org/abs/2607.08751","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["The 100-task headline, 19-task benchmark experiment and staged release roadmap have different scopes.","Depth/point-cloud recording does not imply every baseline consumes them."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Partial","weights":"Not verified","data":"Partial","license":"BSD-3-Clause code; assets/third-party terms separate","notes":"Official roadmap distinguishes initial task/assets release, teleoperation, baseline code, full Shadow Hand dataset and cross-embodiment artifacts; inspect actual available files."}},"relatedIds":[]},{"id":"dm05","name":"DM0.5 / OpenDM","title":"DM0.5: An Open-World Foundation Model for General-Purpose Embodied Intelligence","date":"2026-07-09","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Adds historical visual context, embodied reasoning supervision and dynamic action matching to a flow-action VLA.","institution":"Dexmal","venue":"Official project / technical release","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"project":"https://www.dexmal.com/blog/dm0.5","code":"https://github.com/dexmal/opendm","weights":"https://huggingface.co/Dexmal/DM05"},"policy":{"family":"VLA","baseModel":"4B VLM backbone and 680M flow-matching action expert; backbone family not specified in technical blog","modelSize":"4B backbone + 680M action expert; no recomputed aggregate","inputs":"RGB views/current-history, state and language; pretrained serving also accepts control_mode/speed text conditioning","outputs":"Continuous action chunks; released inference default 50 steps with embodiment-specific normalization","trainingData":"Robot manipulation, navigation, egocentric human and general multimodal data; aggregate scale not disclosed","trainingTime":"Not reported","trainingHardware":"Technical blog discusses H100 optimization; complete training accelerator count/budget not disclosed","controlRate":"Author-reported inference 10 Hz on one RTX4090 and 20 Hz on one H100; underlying servo rate not reported","trainingMethod":"Mixture pretraining with 11autoregressive embodied-reasoning tasks, dynamic action matching and flow action learning","evaluation":"RoboChallenge Table 30v2, RoboTwin2, LIBERO, VLA-Arena, real SO101 and memory-finetuned RoboDojo"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 code; Hugging Face model card declares Gemma terms; individual checkpoints/components separate","notes":"Public project and model release; undisclosed training details remain explicitly unknown. A manuscript is not required to identify the released policy."},"evidence":[{"url":"https://www.dexmal.com/blog/dm0.5","label":"Official model project / technical release","fields":["title","date","policy","release"],"note":"Research-report facts and current executable release are scoped separately; undated releases retain an unknown publication date."},{"url":"https://github.com/dexmal/opendm","label":"Official implementation / author-contributed adapter","fields":["policy.inputs","policy.outputs","release.code"],"note":"Actual deployment contract is checked; adapter presence alone is not evidence of benchmark participation."}],"caveats":["DM05-MEM-Robodojo-Sim is an adapted checkpoint; do not treat base-model and memory-finetuned benchmark scores as identical."],"verified":"2026-10-17"},{"id":"harness-vla","name":"Harness VLA","title":"Harness VLA: Steering Frozen VLAs into Reliable Manipulation Primitives via Memory-Guided Agents","date":"2026-07-09","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"An agent composes analytic primitives and a frozen VLA, using memory to invoke the learned policy within more reliable operating conditions.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2607.08448","project":"https://harnessvla.github.io","code":"https://github.com/RLinf/RPent"},"policy":{"family":"Hybrid","baseModel":"Frozen VLA executors including an RLinf π0.5 LIBERO checkpoint; agent configurations are evaluated separately.","modelSize":"Not reported","inputs":"Task description, RGB-D observations, robot state, execution feedback and memory of usable operating conditions.","outputs":"Structured calls to fixed analytic primitives and vla_act for contact-rich behavior; RoboCasa365 also has mobile-base primitives.","trainingData":"No new VLA weight training in the harness method; executor checkpoints retain their prior supervised / reinforcement training.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen VLA; memory-guided staging, re-grounding, transport, navigation, release and recovery compose retryable VLA calls.","evaluation":"LIBERO, LIBERO-Pro, RoboCasa365, RoboTwin C2R and real dual-Franka manipulation."},"release":{"code":"Available","weights":"Not applicable","data":"Not verified","license":"Apache-2.0 runtime; external VLA/model/simulator terms separate","notes":"RPent explicitly identifies Harness VLA (arXiv:2607.08448) as its first publication and releases agent runtime, planner configuration and benchmark execution."},"evidence":[{"url":"https://arxiv.org/abs/2607.08448","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/RLinf/RPent","label":"Official implementation, README and file/license inspection on 19 October 2026","fields":["links","release"],"note":"RPent explicitly identifies Harness VLA (arXiv:2607.08448) as its first publication and releases agent runtime, planner configuration and benchmark execution. Apache-2.0 runtime; external VLA/model/simulator terms separate."}],"caveats":["RGB-D and analytic motion primitives are additional deployment inputs / tools beyond an RGB-only VLA.","The author-linked RLinf / RPent code reference must not be assumed to release every component of this specific method."],"verified":"2026-10-19"},{"id":"lingbot-va2","name":"LingBot-VA 2.0","title":"Native Video-Action Pretraining for Generalizable Robot Control","date":"2026-07-09","year":2026,"collections":["policy"],"categories":["generalist-foundation","agent-rsi-icl"],"resourceType":"policy","summary":"Native causal video-action pretraining adds semantic action-aware tokenization, sparse experts, human-video context learning and hierarchical planning.","institution":"Robbyant / Ant Group","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2607.08639","project":"https://technology.robbyant.com/lingbot-va-v2","code":"https://github.com/Robbyant/lingbot-va"},"policy":{"family":"World-action model","baseModel":"Semantic visual-action tokenizer, causal MoE video stream, action expert and high-level VLM planner.","modelSize":"Approximately 15.3B trained parameters: 13B video MoE, 0.6B action expert and 1.7B MCP heads; approximately 2.5B active per inference token.","inputs":"RGB observations, language, state and causal history; optional human reference-video context for ICL.","outputs":"32 low-level actions per chunk plus future latents; the planner emits structured subtask context.","trainingData":"Public/internal robot mixture plus thousands of internal egocentric human-video hours across 65.4k episodes. Exact combined hours not reported.","trainingTime":"Full elapsed duration not reported; mixed image, video, video-action, ICL and human co-training curriculum.","trainingHardware":"FSDP configuration reported; complete GPU count/type not reported in the inspected source.","controlRate":"Optimized 142 ms per 32-action chunk gives 225 Hz asynchronous action throughput, not 225 observation-conditioned queries per second. Planner approximately 2 Hz.","trainingMethod":"Native causal MoE pretraining, semantic visual-action tokenization and ICL; consistency distillation, FP8 TensorRT and KV-cache acceleration.","evaluation":"RoboTwin 2.0, physical precision/long-horizon tasks and human-video ICL on held-out task compositions."},"release":{"code":"Partial","weights":"Not verified","data":"Not verified","license":"Apache-2.0 for repository code, verified from LICENSE.txt; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Paper and project describe 2.0; the shared repository retains original VA artifacts. Complete 2.0 checkpoint availability remains unresolved."},"evidence":[{"url":"https://arxiv.org/abs/2607.08639","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/Robbyant/lingbot-va","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://technology.robbyant.com/lingbot-va-v2","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."},{"url":"https://github.com/Robbyant/lingbot-va/blob/main/LICENSE.txt","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["225 Hz is asynchronous action throughput computed from 32 actions / 142 ms, not new observation-conditioned query frequency.","Original VA and VA 2.0 release artifacts must be distinguished."],"verified":"2026-10-19"},{"id":"touchworld","name":"TouchWorld","title":"TouchWorld: A Predictive and Reactive Tactile Foundation Model for Dexterous Manipulation","date":"2026-07-08","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Hierarchical planning and tactile goal prediction combine with a fast tactile residual controller for dexterous manipulation.","institution":"Harbin Institute of Technology, Shenzhen / PHANES AI","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2607.07287","project":"https://phanes-lab.github.io/TouchWorld-website/"},"policy":{"family":"World-action model","baseModel":"Qwen 3-VL-4 B subtask planner; Wan 2.2-TI2 V-5 B tactile world model; nominal visuo-tactile policy and 8-layer tactile residual transformer.","modelSize":"4 B planner+5 B world model; total policy parameters Not reported.","inputs":"RGB, language, current tactile observations, proprioception and compact subtask memory; fast branch sees recent tactile/state history.","outputs":"120D nominal actions with 58D tactile-sensitive residual corrections; generated visual/tactile subgoals.","trainingData":"Six real tasks with 200 teleoperated training trajectories/task; planner 128,866 SFT records; world model human interaction pretraining plus 10 h robot data.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Figure 2 depicts 1 Hz high-level planning, 10 Hz nominal policy and 30 Hz tactile refinement; appendix scheduling specifies 32-step nominal chunks, 16-step residual windows and 4-step commits, with realized rate dependent on hardware.","trainingMethod":"Planner 20 epochs Lo RA; world model 50 epochs Lo RA; nominal policy learning then frozen-policy residual imitation.","evaluation":"Humanoid with Wuji hands/JQ tactile gloves:Water Flower, Tabletop Clearing, Cup Insertion, Power Plug Insertion, Pot Wiping, Tissue Pulling; clean/human perturbation cohorts."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2607.07287","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://phanes-lab.github.io/TouchWorld-website/","label":"Official author project","fields":["links","release"],"note":"Live artifact status reviewed separately from paper claims."}],"caveats":["A high-frequency residual module is described but no universal fixed Hz is inferred from chunk length.","Planner/world model/nominal policy are different computation stages; parameter counts must not be summed as reported total."],"verified":"2026-10-17"},{"id":"wam-ttt","name":"WAM-TTT","title":"WAM-TTT: Steering World-Action Models by Watching Human Play at Test Time","date":"2026-07-08","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","generalist-foundation"],"resourceType":"policy","summary":"Human videos update small fast-weight memories inside a frozen world-action model before robot execution.","institution":"Peking University / Galbot / CASIA / Tsinghua University","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Bimanual","Dexterous hand","Humanoid"],"links":{"paper":"https://arxiv.org/abs/2607.06988"},"policy":{"family":"World-action model","baseModel":"Test-time training of WAM fast-weight memory. LDA: Qwen3-VL-4B-Instruct interface and DiT-L MMDiT head; added video-side TTT residuals.","modelSize":"4B VLM component; full system parameter count not reported. Fast-weight MLP width 128, head dimension 48.","inputs":"Current robot visual/state context and task instruction; action-free egocentric human videos supply deployment-time memory. No human pose/action annotations.","outputs":"Robot action chunks through the unchanged LDA action expert; embodiment-specific control mapping.","trainingData":"2,286 paired human/robot episodes covering nine manipulation tasks; human videos captured with GoPro.","trainingTime":"100,000 meta-training steps; wall-clock duration not reported. One inner SGD step at test time.","trainingHardware":"8 NVIDIA H800 GPUs for meta-training, global batch 128.","controlRate":"Not reported","trainingMethod":"Paired meta-training jointly optimizes WAM and TTT slow weights. Deployment freezes these and updates only fast memory weights from human video prediction and memory reconstruction.","evaluation":"Nine tasks on Unitree G1, Galbot gripper and Galbot Sharpa; original cubicle and new homes, 25 trials per task/setting, partial-credit progress metric."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No author-linked implementation was verified in the inspected primary manuscript/project. This is an evidence gap, not proof that no implementation exists."},"evidence":[{"url":"https://arxiv.org/abs/2607.06988","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."}],"caveats":["This is fast-weight parameter adaptation, rather than only context prompting. WAM weights remain frozen at deployment, but are optimized during meta-training.","The same nine broad tasks appear in the paired meta-training corpus; new variants/scenes must not be described as nine wholly unseen task families."],"verified":"2026-10-17"},{"id":"lingbot-vla2","name":"LingBot-VLA 2.0","title":"From Foundation to Application: Improving VLA Models in Practice","date":"2026-07-07","year":2026,"collections":["policy"],"categories":["generalist-foundation","rgbd-3d"],"resourceType":"policy","summary":"A multi-embodiment VLA expands pretraining and whole-body action coverage, using sparse action-expert MoE and dual-query geometric/temporal distillation.","institution":"Robbyant / Ant Group","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Bimanual","Parallel-jaw gripper","Dexterous hand","Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2607.06403","project":"https://technology.robbyant.com/lingbot-vla-v2","code":"https://github.com/Robbyant/lingbot-vla-v2","weights":"https://huggingface.co/collections/robbyant/lingbot-vla-v2"},"policy":{"family":"VLA","baseModel":"Qwen3-VL-4B with a sparse-MoE flow action expert; current/future query distillation from LingBot-Depth and DINO-Video teachers.","modelSize":"Official released checkpoint named 6B; paper also studies dense 0.6B versus MoE 1.6B action experts with 0.6B active parameters. These action-expert ablation sizes are not the complete model size.","inputs":"Multi-view RGB, language and robot state. Native-depth refers to internally distilled geometry; frozen depth/video teachers supervise training and are not deployed depth-sensor streams.","outputs":"Unified embodiment-conditioned continuous action chunks covering arms, grippers, head, waist, mobile base and dexterous-hand degrees of freedom where data/configuration support them.","trainingData":"Approximately 60000 h curated pretraining: 50000 h robot trajectories across 20 robot configurations and 10000 h selected egocentric human video. This is a training pool, not all publicly released data.","trainingTime":"Full-pretraining wall-clock not reported. Current RoboTwin50-task post-training release gives per-iteration timing5.93s Muon /4.31s DistributedMuon on a single H20 node, not complete training duration.","trainingHardware":"Full-pretraining hardware not reported. Current repository profiles single-node NVIDIA H20 downstream post-training; the8 GPU evaluation launcher is an inference setting.","controlRate":"Not reported","trainingMethod":"Cross-embodiment action learning with token-level MoE routing; joint geometric/current and temporal/future teacher distillation; downstream post-training recipes.","evaluation":"Paper: 9 GM-100 dual-arm tasks and 2 long-horizon mobile tasks on 2 platforms. Current release adds50-task RoboTwin2.0 post-training/evaluation with clean and randomized conditions."},"release":{"code":"Available","weights":"Available","data":"Partial","license":"Apache-2.0 original code and released model card; teacher backbones/checkpoints and source datasets retain their licenses, including DINO-Video terms.","notes":"Base 6B and RoboTwin post-trained weights available. Public GM-100 resources do not establish release of the entire60000 h training pool."},"evidence":[{"url":"https://arxiv.org/abs/2607.06403","label":"Paper and first-public arXiv record","fields":["title","date","institution","policy","modalities","embodiments","environments"],"note":"Technical fields follow the inspected full manuscript and appendices; date is arXiv v1, not the current revision."},{"url":"https://technology.robbyant.com/lingbot-vla-v2","label":"Official project","fields":["summary","imageCandidates","release"],"note":"Current author project and release links."},{"url":"https://github.com/Robbyant/lingbot-vla-v2","label":"Official repository","fields":["release","policy.baseModel","policy.inputs","policy.evaluation","imageCandidates"],"note":"Current implementation/release scope can differ from paper experiments."},{"url":"https://github.com/Robbyant/lingbot-vla-v2/blob/main/LICENSE","label":"Apache-2.0 code license","fields":["release.license"]},{"url":"https://huggingface.co/robbyant/lingbot-vla-v2-6b","label":"Official6B model card","fields":["policy.modelSize","release.weights","release.license"]}],"caveats":["Native-depth distillation does not imply measured depth input at inference.","Official RoboTwin checkpoint validation uses FP32; README states BF16 can materially change success rates.","Published GM-100/mobile evaluation and later RoboTwin releases are separate scopes."],"verified":"2026-10-17"},{"id":"rynnworld-4d","name":"RynnWorld-4D / RynnWorld-4D-Policy","title":"RynnWorld-4D: 4D Embodied World Models for Robotic Manipulation","date":"2026-07-07","year":2026,"collections":["policy"],"categories":["rgbd-3d","geometry-models"],"resourceType":"policy","summary":"A tri-branch RGB/depth/flow world model provides frozen 4D latents to a dexterous bimanual flow-matching action head.","institution":"DAMO Academy, Alibaba Group; Hupan Lab; CUHK; Hong Kong Embodied AI Lab","venue":"arXiv preprint","modalities":["RGB","Language","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2607.06559","project":"https://alibaba-damo-academy.github.io/RynnWorld-4D.github.io/","code":"https://github.com/alibaba-damo-academy/RynnWorld-4D","weights":"https://huggingface.co/Alibaba-DAMO-Academy/RynnWorld-4D"},"release":{"code":"Available","weights":"Partial","data":"Partial","license":"Apache-2.0 for repository code; pretrained models, data and figure rights remain separate","notes":"World-model weights verified in HF file listing. Separate trained task-policy head weights not identified; three Tianji sample episodes ship with code."},"evidence":[{"url":"https://arxiv.org/abs/2607.06559","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://alibaba-damo-academy.github.io/RynnWorld-4D.github.io/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/alibaba-damo-academy/RynnWorld-4D","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://huggingface.co/api/models/Alibaba-DAMO-Academy/RynnWorld-4D","label":"Actual checkpoint file inventory","fields":["release.weights"],"note":"ema_weights.pt and model-state files present; no separate task-policy-head checkpoint identified."},{"url":"https://github.com/alibaba-damo-academy/RynnWorld-4D/blob/main/rynnworld4d_policy/README.md","label":"Policy implementation details","fields":["policy.baseModel","policy.inputs","policy.outputs","release.data"],"note":"DA3 inference dependency, 54-D observation.state, four-step Euler action head and three sample episodes."},{"url":"https://github.com/alibaba-damo-academy/RynnWorld-4D/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/alibaba-damo-academy/RynnWorld-4D/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"360 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."},{"url":"https://huggingface.co/Alibaba-DAMO-Academy/RynnWorld-4D","label":"Official weights release destination","fields":["release.weights"],"note":"Destination linked by the official project/repository; component release scope is retained in release notes."}],"caveats":["Estimated input depth, pseudo depth labels and generated future depth are distinct from sensor-measured depth.","The advertised 9 Hz is a chunk-derived action rate; the heavy visual backbone does not refresh observations at 9 Hz."],"verified":"2026-10-17","policy":{"family":"World-action model","baseModel":"Wan2.2-TI2V-5B-derived tri-branch DiT; Flow Former and VPP-derived action head.","modelSize":"Wan 5B initialization; full tri-branch and action-head parameter totals not reported","inputs":"Head RGB, language, 54-D proprioception; current deployment estimates depth with Depth Anything 3 (DA3) rather than requiring measured depth.","outputs":"Ten-step chunks of 54-D dual-arm/hand joint commands; the world model separately generates RGB, depth and flow.","trainingMethod":"Three-stage world-model adaptation; frozen backbone with flow-matching head trained for 100 epochs.","trainingData":"254.4M video frames with pseudo depth/flow; Tianji demonstrations for the action head.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Six real Tianji-Wuji tasks: dual picking, block pushing, hand-over, bimanual lifting, lid placement, bowl stacking; 35 trials each.","controlRate":"RTX 5090 FP8: 1,106 ms/forward (~0.9 Hz observation refresh); ten-action chunks yield author-reported ~9 Hz effective action rate."}},{"id":"internvla-a15","name":"InternVLA-A1.5","title":"InternVLA-A1.5: Unifying Understanding, Latent Foresight, and Action for Compositional Generalization","date":"2026-07-06","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Uses a lightweight unified action/foresight expert and training-only frozen video supervision while retaining native VLM semantics.","institution":"Shanghai AI Laboratory / InternRobotics","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2607.04988","code":"https://github.com/InternRobotics/InternVLA-A-series","data":"https://huggingface.co/datasets/InternRobotics/InternData-A1","weights":"https://huggingface.co/InternRobotics/InternVLA-A1.5-base"},"policy":{"family":"VLA","baseModel":"Qwen3.5-2B VLM; 460M unified expert; frozen WAN2.2-5B visual-foresight teacher","modelSize":"2B backbone + 460M expert; frozen 5B WAN teacher is excluded from action-only deployment","inputs":"Multi-view RGB, task/subtask language and robot state","outputs":"Continuous 50-step flow action chunks; FAST action tokens are auxiliary training supervision; real deployment omits WAN","trainingData":"1.2M robot episodes / 861M frames from six real/synthetic sources; approximately 3M InternVLA-M1 QA samples","trainingTime":"Stage 1 300k, Stage 2 600k, default post-training 60k; RoboTwin appendix uses 100k; wall-clock not reported","trainingHardware":"RoboTwin appendix: 24 GPUs, accelerator model not disclosed; real inference RTX5090","controlRate":"Real action-only inference about 0.1s on one RTX5090; action execution/replanning depends on setting","trainingMethod":"VLM transfer with VQA/subtask/FAST → shared expert with frozen-video foresight supervision → continuous flow action SFT","evaluation":"LIBERO/Plus, RoboTwin2, SimplerEnv, DOMINO, EBench and four real compositional/generalization tasks"},"release":{"code":"Available","weights":"Available","data":"Available","license":"CC BY-NC-SA 4.0 code (official README); model/data terms separate","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2607.04988","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/InternRobotics/InternVLA-A-series","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."},{"url":"https://raw.githubusercontent.com/InternRobotics/InternVLA-A-series/master/LICENSE","label":"Repository license file","fields":["release.license"],"note":"Code scope; model, data and third-party component terms can differ."}],"caveats":["WAN is a frozen training-time teacher and is discarded for the optimized action-only release.","Default 60k post-training and the appendix RoboTwin-specific 100k run have different scopes."],"verified":"2026-10-17"},{"id":"lingbot-vision-depth2","name":"LingBot-Vision / LingBot-Depth 2.0","title":"Vision Pretraining for Dense Spatial Perception","date":"2026-07-06","year":2026,"collections":["policy"],"categories":["geometry-models","rgbd-3d"],"resourceType":"model","summary":"Boundary-centric visual pretraining improves dense spatial representations and the LingBot-Depth2 completion backbone.","institution":"Robbyant, Ant Group","venue":"arXiv preprint","modalities":["RGB","Depth"],"environments":["Simulation","Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2607.05247","project":"https://technology.robbyant.com/lingbot-vision","code":"https://github.com/Robbyant/lingbot-vision"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Apache-2.0 for repository code; pretrained models, data and figure rights remain separate","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2607.05247","label":"Original manuscript and first submission history","fields":["title","date","summary","model","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://technology.robbyant.com/lingbot-vision","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/Robbyant/lingbot-vision","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/Robbyant/lingbot-vision/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/Robbyant/lingbot-vision/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"29 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["The vision backbone is not an action policy; depth completion is a downstream model with different inputs.","No suitable released model architecture diagram was verified; no unrelated result plot is used as a cover."],"verified":"2026-10-17","model":{"role":"Dense geometry encoder and depth-completion backbone","family":"Geometry representation","baseModel":"Masked boundary modeling with ViT-g/16 teacher and distilled ViT-L/B/S students.","modelSize":"~1.1B ViT-g teacher; ViT-L/B/S distilled students","inputs":"RGB for the vision encoder; RGB plus partial depth for the downstream completion model.","outputs":"Dense spatial features; downstream metric depth completion.","trainingMethod":"Self-supervised boundary token modeling and subsequent masked-depth learning.","trainingData":"About 161M curated RGB images for vision pretraining; separate depth completion data.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Dense spatial benchmarks and 14 depth-completion benchmarks; manipulation relevance comes through depth sensing rather than a new robot policy.","inference":"Not reported in the inspected primary-source sections"}},{"id":"robodojo","name":"RoboDojo","title":"RoboDojo: A Unified Sim-and-Real Benchmark for Comprehensive Evaluation of Generalist Robot Manipulation Policies","date":"2026-07-05","year":2026,"collections":["benchmark"],"categories":["general-manipulation","precision-assembly"],"resourceType":"benchmark","summary":"Unified simulation/physical benchmark with five capability dimensions and common policy deployment through XPolicyLab.","institution":"RoboDojo consortium","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2607.04434","project":"https://robodojo-benchmark.com/","code":"https://github.com/RoboDojo-Benchmark/RoboDojo","data":"https://huggingface.co/datasets/RoboDojo-Benchmark/RoboDojo"},"benchmark":{"simulator":"Isaac Sim / Isaac Lab","simulatorVersion":"Official original release: Isaac Sim 5.1.0 / Isaac Lab 2.3; Python 3.11.","taskCount":"42 simulation tasks + 18 physical tasks across 3 real robot embodiments","tasks":["Stack Bowls","Push T","Pack Objects Into Box","Fold Clothes","Hang Mugs","Sweep Blocks","Pour Liquid Into Cup","Make Toast","Arrange Largest Number","Sort Nesting Dolls By Size","Store Laptop And Headphones","Stack Blocks","Cover Blocks","Match And Pick From Conveyor","Swap Blocks","Swap T","Press By Number","Imitate Sorting Sequence","Fasten Screws","Plug In Charger","Insert Tubes","Pour Balls Into Vase","Play Xylophone","Deposit Coin","Insert Key","Build Tower","Fill Pen Holder","Classify Objects","Put Bottles Into Dustbin","Play Tic-Tac-Toe","Fill Egg Holder","Organize Table","Make Kong","Play Stacking Toy","Align Blocks","General Pickup","Solve Equation","Stack Blocks By Language","Classify Objects By Language","Pick From Conveyor By Image","Store Tools In Toolbox","Pour By Language"],"taskListScope":"Complete 42-task core simulation catalogue; 18 real tasks and later DLC are separate catalogues.","capabilities":["Generalization","Memory","Precision","Long-horizon execution","Open-vocabulary instruction following"],"metrics":"Capability-specific / overall task-success aggregates; fine-grained progress under task criteria; separate sim and physical leaderboards.","robots":"Simulation dual-arm ARX X5; physical ARX X5, Piper and Piper X.","sensors":"Head and wrist RGB cameras, proprioception and instructions. Simulator can export depth, but listed baseline observation channels must be verified separately; official suite has no tactile sensor.","dataCollection":"Simulation: annotated skill composition with cuRobo v2 motion planning, or VR teleoperation for difficult tasks. Real-world: homogeneous leader–follower robot teleoperation.","data":"Demonstration data downloadable by task; exact current total count varies by release.","protocol":"Seed-controlled layouts; standard/randomized generalization conditions; heterogeneous parallel simulation; physical standardized scene reset and cloud evaluation.","baselines":"Paper:30 policy baselines. Official October 20 snapshot: 60 named simulation submissions (7 Agent-track rows and53 non-Agent rows) and11 real submissions. GPT-6-Astra/GPT-5.5/DeepSeek-Flash are variants of one RoboProbe interface. XPolicyLab:69 integrated adapters at 5d0be14, separately scoped from scored submissions."},"release":{"code":"Available","weights":"Not applicable","data":"Available","license":"MIT benchmark code; assets/data/policy models have separate terms","notes":"RoboDojo release is evaluation-only; model servers and policy adapters are owned by XPolicyLab."},"evidence":[{"url":"https://arxiv.org/abs/2607.04434","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://robodojo-benchmark.com/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/RoboDojo-Benchmark/RoboDojo","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://robodojo-benchmark.com/doc/sim-tasks/","label":"Official implementation / documentation","fields":["benchmark.tasks"],"note":"Complete official core task catalogue."},{"url":"https://arxiv.org/abs/2607.04434","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Sections3.1.4/3.2.2 and4.1.4; simulated trajectory synthesis/VR and physical leader–follower collection retain separate scopes."},{"url":"https://robodojo-benchmark.com/data/leaderboard.generated.json","label":"Official current source / artifact inventory — 21 October 2026","fields":["benchmark.baselines"],"note":"October 20 official JSON has 60 simulation / 11 real rows; official app system-type map identifies 7 agent rows. Paper baselines, live submission aliases and integrations remain separate."},{"url":"https://github.com/XPolicyLab/XPolicyLab/tree/5d0be1499634d3176258c8f2e31f5fe816497b0a/policy","label":"Official current source / artifact inventory — 21 October 2026","fields":["benchmark.baselines"],"note":"69 actual policy directories excluding demo_policy; CloudRobo-WM newly integrated. Integration does not establish a leaderboard score."}],"caveats":["A simulator depth output does not mean original evaluated policies use depth.","Report core 42 tasks separately from DLC and adapted external reproductions."],"verified":"2026-10-21","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not applicable","data":"Available","license":"MIT benchmark code; assets/data/policy models have separate terms","notes":"RoboDojo release is evaluation-only; model servers and policy adapters are owned by XPolicyLab."}},"relatedIds":[]},{"id":"softvtbench","name":"SoftVTBench","title":"SoftVTBench: A Safety-Aware Visuo-Tactile Benchmark for Physically Constrained Robotic Manipulation of Deformable Objects (Early Version)","date":"2026-07-05","year":2026,"collections":["benchmark"],"categories":["tactile-benchmarks","robustness-evaluation"],"resourceType":"benchmark","summary":"Matched rigid and deformable task suites evaluate task completion together with deformation and retention, using policy-hidden FEM diagnostics.","institution":"Tuojing Intelligence","venue":"Preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2607.04234","project":"https://softvtbench.github.io/","code":"https://github.com/TuojingAI/SoftVTBench","data":"https://huggingface.co/datasets/Arthur12137/SoftVTBench"},"benchmark":{"simulator":"Isaac Sim / Isaac Lab with FEM deformable objects","simulatorVersion":"Paper early version: Isaac Sim 4.5.0 / Isaac Lab 0.41.3 as written. Current release README: Isaac Sim 4.5 / Isaac Lab 2.1.1 with a pinned commit.","taskCount":"Current project release: 40 tasks, four suites; early paper: 2,000 episodes / 33 assets; homepage: 4,000 demonstrations / 50+ assets.","tasks":["Object-Soft","Spatial-Soft","Object-Rigid","Spatial-Rigid"],"taskListScope":"Four suite names only; each suite contains 10 language-conditioned pick-and-place task configurations. Detailed per-task prompts are in paper Tables 7–8 and released task definitions.","capabilities":["Deformable manipulation","Safe grasp-force window","Object discrimination","Spatial grounding","Rigid–soft controlled comparison"],"metrics":"Paper: Goal Success and Safety Success (no drop + peak deformation below calibrated per-object tolerance). Current page: Task Success Rate and Deformation-aware Success Rate (DSR).","robots":"Franka Panda arm with parallel-jaw gripper","sensors":"Third-person / wrist RGB; dual-finger GelSight Mini tactile RGB and marker motion; proprioception, language and actions at 20 Hz. Privileged FEM states restricted to evaluator.","dataCollection":"Constraint-aware simulated expert rollouts, followed by integrity screening and human verification. The low-level expert implementation is Not verified; calibration sweeps are separate.","data":"Current homepage advertises 4,000 expert demonstrations / 40 tasks / 50+ assets; early paper states 2,000 episodes and 33 assets with 50 demos per task.","protocol":"Four matched suites separate object type and variation axis; deformation calibration performed before policy training. Current default evaluation uses 50 episodes per task over ten tasks per suite.","baselines":"Early manuscript: π0.5 visual-only / visuotactile. Current project additionally lists Diffusion Policy and FastWAM."},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"Apache-2.0 code; third-party assets and OpenPI terms separate","notes":"Task/dataset release and locked evaluation environment public; project has expanded since early manuscript."},"evidence":[{"url":"https://arxiv.org/abs/2607.04234","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://softvtbench.github.io/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/TuojingAI/SoftVTBench","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://github.com/TuojingAI/SoftVTBench/blob/main/LICENSE","label":"Official source","fields":["release.license"],"note":"License text read directly; code license does not automatically cover models, data or simulator."},{"url":"https://softvtbench.github.io/","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Project title and scale have evolved beyond the early arXiv manuscript; counts and metric names must be tied to source version.","FEM object state used for scoring is privileged and must not be supplied to the policy.","The four suite names are not a complete forty-task catalogue."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not verified","data":"Available","license":"Apache-2.0 code; third-party assets and OpenPI terms separate","notes":"Task/dataset release and locked evaluation environment public; project has expanded since early manuscript."}},"relatedIds":[]},{"id":"dex-pih-benchmark","name":"DexPiH Benchmark","title":"A Simulation Benchmark for Dexterous Peg-in-Hole Assembly With Force-Tactile-Based Pose Estimation Across Multiple Embodiments","date":"2026-07-02","year":2026,"collections":["benchmark"],"categories":["precision-assembly","dexterous-benchmarks","tactile-benchmarks"],"resourceType":"benchmark","summary":"Dexterous insertion across four arms, two hands and ten geometries, using idealized wrist/fingertip wrenches and a learned pose-estimation baseline.","institution":"Gwangju Institute of Science and Technology","venue":"IEEE Access 14:101816–101830","modalities":["Force/Torque","Tactile","Proprioception"],"environments":["Simulation"],"embodiments":["Dexterous hand"],"links":{"paper":"https://doi.org/10.1109/ACCESS.2026.3709302"},"benchmark":{"simulator":"Isaac Gym / PhysX TGS with SDF collision meshes","simulatorVersion":"Exact Isaac Gym release Not reported; SDF resolution 512, contact offset 1e-4 m are physics settings, not software version.","taskCount":"10 peg geometries × 4 arms × 2 hands = 80 embodiment–geometry combinations","tasks":["Square peg insertion","Octagon peg insertion","Cross peg insertion","Polygon peg insertion","T-shape peg insertion"],"taskListScope":"Five explicitly named geometry examples; paper Figure 3 contains all ten shapes.","capabilities":["Occluded peg-pose estimation","Cross-embodiment force/tactile fusion","Alignment and insertion"],"metrics":"Pose MAE (mm / degrees); insertion success after ≥3.5 cm insertion; failures include dropping or reaching 2,000 timesteps.","robots":"Franka Panda, KUKA iiwa, Kinova Gen3, UR5e arms; Allegro and KISTAR hands.","sensors":"Idealized six-axis wrist and per-fingertip net wrench; no noise/hysteresis/bandwidth/calibration model; no per-taxel pressure/shear image.","dataCollection":"Automated state-machine expert sequences in parallel simulation; successful insertion sequences are retained for training.","data":"80K successful state-machine sequences, 1K per embodiment–shape; 400 parallel environments on one RTX 3090. Post-contact training subsequences average 370 steps.","protocol":"80/20 train/test for estimator; friction/restitution/compliance/controller randomization; static hand grasp, arm corrects wrist; vision ablations limited to Franka–Allegro.","baselines":"DexPiHNet Transformer; LSTM/temporal learned baselines and classical force-search controllers."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Open-access article Creative Commons terms; code/data license Not reported","notes":"Article and detailed benchmark definitions accessible; public implementation release not resolved."},"evidence":[{"url":"https://doi.org/10.1109/ACCESS.2026.3709302","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://doi.org/10.1109/ACCESS.2026.3709302","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Simulation only and idealized sensing; hand does not perform reactive finger correction.","Fingertip tactile means integrated wrench here, not optical deformation images or spatial taxel fields."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Open-access article Creative Commons terms; code/data license Not reported","notes":"Article and detailed benchmark definitions accessible; public implementation release not resolved."}},"relatedIds":[]},{"id":"abot-m05","name":"ABot-M0.5","title":"ABot-M0.5: Unified Mobility-and-Manipulation World Action Model","date":"2026-07-01","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Models future video, frame-level latent actions and separate mobility/manipulation action streams with progressive dream-forcing alignment.","institution":"AMAP CV Lab","venue":"Preprint","modalities":["RGB","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2607.00678","project":"https://amap-cvlab.github.io/ABot-Manipulation/","code":"https://github.com/amap-cvlab/ABot-Manipulation","weights":"https://huggingface.co/acvlab"},"policy":{"family":"World-action model","baseModel":"Wan2.2-5B video diffusion backbone, latent-action model and dual-level MoT action experts","modelSize":"5B video foundation backbone; complete latent/action expert total not reported","inputs":"Multi-view RGB histories and language; camera slots retain third-person/wrist semantic roles","outputs":"Future video latents, frame-level latent actions and executable arm/base action sequences","trainingData":"Public/synthetic robot mixture: OXE, OXE-AugE, Agibot-Beta, RoboCOIN, RoboMind, InternData-A1 and other sources; aggregate not disclosed","trainingTime":"World-model and latent-action pretraining then progressive inverse-dynamics SFT; exampleSFT50k + 5k dream-forcing; wall-clock not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Action-unconditioned visual pretraining → self-supervised latent actions → inverse-dynamics learning with model-predicted-video dream forcing","evaluation":"RoboCasa365, mobile/long-horizon simulation and real fine-grained/mobile manipulation"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 for repository code, verified from LICENSE.txt; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2607.00678","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/amap-cvlab/ABot-Manipulation","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."},{"url":"https://github.com/amap-cvlab/ABot-Manipulation/blob/main/LICENSE.txt","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["M0.5 is a distinct world-action architecture from original Qwen-VLA-based M0; preserve separate paper/version."],"verified":"2026-10-19"},{"id":"aspire","name":"ASPIRE","title":"ASPIRE: Agentic /Skills Discovery for Robotics","date":"2026-06-30","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Parallel coding agents discover and repair robot programs, then retain validated skills that can guide other tasks or embodiments.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2607.00272"},"policy":{"family":"Robot agent","baseModel":"Simulation: Claude Code with Opus 4.6 and a one-million-token context. Real transfer: Codex GPT-5.5 at xhigh reasoning.","modelSize":"Not reported","inputs":"Task instruction, CaP-X perception / geometry / motion tools, multimodal execution traces and validated skill-library entries.","outputs":"Executable Python robot programs and reusable in-context skills, developed by task actors coordinated in parallel.","trainingData":"Simulation interaction and verified repair traces; real experiments use three skills developed in Franka simulation.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen coding models; coordinator / actor search, trace-guided debugging and validated skill-library reuse. No task-specific base-model weight training.","evaluation":"LIBERO-Pro, Robosuite, BEHAVIOR-1K and bimanual YAM transfer of can pickup, bowl placement and drawer push / pull."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2607.00272","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["Real transfer uses a different coding model, embodiment and API from simulation.","The reported real debugging loop can run and inspect robot programs before held-out evaluation."],"verified":"2026-10-17"},{"id":"tactx","name":"TactX","title":"TactX: Learning Shared Tactile Representations Across Diverse Sensors","date":"2026-06-30","year":2026,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Sensor-specific encoders map optical, magnetic and resistive touch to one transferable latent, evaluated through frozen-encoder ACT policy transfer.","institution":"UC San Diego; Seoul National University; Amazon FAR","venue":"arXiv preprint 2026 (submitted to CoRL 2026)","modalities":["Tactile"],"environments":["Real robot"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2606.31236","project":"https://tactx-project.github.io/"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Official project advertises code, dataset and checkpoints, but its inspected resource elements have no usable download destinations. Public artifacts remain unverified."},"model":{"role":"Cross-sensor tactile representation","family":"Tactile encoder","baseModel":"Sensor-specific ResNet-18, MLP or residual-CNN encoders/decoders with a shared 16D variational latent; downstream ACT uses frozen tactile latents.","modelSize":"Not reported in the inspected primary manuscript","inputs":"Daimon optical contact composites, eFlesh magnetic vectors or FlexiTac resistive grids; paired quasi-static contacts supply training alignment.","outputs":"Shared 16D tactile posterior mean; downstream ACT separately outputs robot action chunks.","trainingMethod":"Joint pairwise contrastive alignment, self/cross reconstruction and KL regularization; encoders remain frozen for downstream imitation learning.","trainingData":"Representation: 2,670 paired-contact trajectories / approximately 145K frames across ten printed objects and six mounting configurations. Downstream ACT: approximately 50 GELLO demonstrations per task.","trainingTime":"Representation: 300 epochs. Downstream ACT: 50K steps with 64-step chunks. Wall-clock times not reported.","trainingHardware":"Not reported in the inspected primary manuscript","inference":"Not reported in the inspected primary manuscript","evaluation":"Sensor invariance, transitive alignment and object/contact preservation; frozen-latent ACT evaluated on pick-and-place, plug insertion, board wiping and reorientation, plus a color-shift pick-and-place setting."},"evidence":[{"url":"https://arxiv.org/abs/2606.31236","label":"Original manuscript and first public submission history","fields":["title","date","institution","model","policy","modalities","environments","caveats"],"note":"Full author method, evaluation and appendix reviewed; downstream controller probes are distinguished from the reusable model."},{"url":"https://tactx-project.github.io/","label":"Official author project and current artifact destinations","fields":["release","venue","imageCandidates"],"note":"A project button or advertised artifact does not establish a downloadable release; paper-version differences are retained."}],"caveats":["Primary contribution is the transferable tactile representation; ACT is a downstream control probe, so this record is counted as a Model.","The 16D tactile latent differs from ACT’s 32D action-CVAE latent; sensors are sampled separately from the action-chunk horizon."],"verified":"2026-10-17"},{"id":"behavior-prompting-policy","name":"Behavior Prompting Policy (BPP)","title":"What Enables In-Context Behavior Prompting for Manipulation?","date":"2026-06-29","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A demonstration-conditioned diffusion policy uses dense observation and action sequences to reproduce new manipulation behavior, with one prompt demonstration at deployment.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2606.30457","project":"https://behavior-prompting.github.io","code":"https://github.com/real-stanford/behavior_prompting"},"policy":{"family":"Diffusion","baseModel":"Diffusion action policy with a cross-attention prompt encoder / transformer decoder; prompt attention pooling associates observations, proprioception and full action sequences.","modelSize":"Not reported","inputs":"RGB observations, proprioception and a demonstration prompt. Prompt images / states are typically sampled at 1 Hz while prompt actions retain full temporal resolution.","outputs":"Continuous robot-action sequences conditioned on the current observations and demonstration behavior.","trainingData":"DrawAnything simulation and iPhUMI real demonstrations, LIBERO-Gen procedural tasks and real laundry folding; datasets are released with benchmark-specific configurations.","trainingTime":"Reported runs: LIBERO-Gen Combination 28 hours; LIBERO-Gen Chain 34 hours; DrawAnything-Sim 27 hours; DrawAnything-Real 42 hours; Laundry Folding 24 hours.","trainingHardware":"Four NVIDIA RTX A6000 GPUs for LIBERO-Gen Combination; four NVIDIA L40S GPUs for the other listed runs.","controlRate":"Not reported","trainingMethod":"Offline demonstration-conditioned diffusion learning. New task / behavior prompting at evaluation does not update weights.","evaluation":"DrawAnything-Sim / Real, LIBERO-Gen Combination / Chain and Laundry Folding; generalization to unseen behaviors and prompt-content ablations."},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"MIT for the code repository; dataset and hardware terms should be checked separately.","notes":"Training / evaluation code, datasets and iPhUMI hardware / application software are linked by the authors."},"evidence":[{"url":"https://arxiv.org/abs/2606.30457","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/real-stanford/behavior_prompting","label":"Author-linked repository","fields":["links.code"],"note":"Repository identity; code and checkpoint release status are stated separately."}],"caveats":["The current arXiv title is What Enables In-Context Behavior Prompting for Manipulation?; BPP remains the method name.","Although iPhUMI records LiDAR depth, the paper explicitly states that depth is not used in its models or experiments."],"verified":"2026-10-17"},{"id":"htt","name":"HTT / Heterogeneous Tactile Transformer","title":"Heterogeneous Tactile Transformer","date":"2026-06-29","year":2026,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Shares a transformer trunk across optical and taxel-array tactile sensors through sensor-specific tokenizers and paired masked objectives.","institution":"National University of Singapore; Carnegie Mellon University","venue":"CoRL 2026 (official repository)","modalities":["Tactile"],"environments":["Simulation","Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2606.29948","project":"https://jxbi1010.github.io/htt-gh-page/","code":"https://github.com/jxbi1010/HTT","data":"https://huggingface.co/datasets/AllenBi21/HTT-dataset","weights":"https://huggingface.co/datasets/AllenBi21/HTT-dataset/resolve/main/htt_4sensors_best.pth"},"model":{"role":"Reusable representation or perception model; downstream controllers are separate systems","family":"Tactile encoder","baseModel":"Sensor-specific spatial/temporal encoders + shared transformer trunk; per-sensor reconstruction and cross-sensor predictors","modelSize":"Embedding width 192 with 3 heads; shared trunk depth 9; sensor-specific frontend and decoder modules additional. Total parameter count not reported.","inputs":"Optical tactile images or high-rate tactile array windows from Xela, TAC-02, 9DTact or GelSight Mini","outputs":"Sensor-shared tactile embeddings and downstream force, slip, object or policy features","trainingMethod":"Per-modality masked reconstruction and bidirectional cross-sensor latent prediction","trainingData":"HPT: 1.6M paired frames across four sensors; unlabeled pretraining and labeled object/force/slip splits separated","trainingTime":"50,000 pretraining steps; alignment weight ramps during first 20,000; wall-clock not reported","trainingHardware":"Not reported","inference":"Not reported","evaluation":"Object classification, force/slip perception, unseen-sensor transfer and physical/simulated robot policy learning in ManiFeel settings."},"release":{"code":"Available","weights":"Available — official download link","data":"Available — official resource link","license":"CC BY-NC 4.0 repository LICENSE; upstream assets retain their own notices","notes":"Code, pretrained encoders and downstream task data are separate artifacts; no policy checkpoint is implied by an encoder release."},"evidence":[{"url":"https://arxiv.org/abs/2606.29948","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Method, experiments and implementation sections; specifications retain component and evaluation scope."},{"url":"https://jxbi1010.github.io/htt-gh-page/","label":"Official project","fields":["links","release"],"note":"Author project; author-reported release scope is distinguished from an executed reproduction."},{"url":"https://github.com/jxbi1010/HTT","label":"Official implementation and current release instructions","fields":["release","links"],"note":"README and repository files inspected on 2026-10-17."},{"url":"https://github.com/jxbi1010/HTT/blob/main/LICENSE","label":"Repository license file","fields":["release.license"],"note":"Applies to repository scope; model/data/base-model terms can differ."}],"caveats":["Model metadata reflects arXiv v2 of 2026-10-13; first-public date remains 2026-06-29.","HPT paired-frame count is not the number of independent demonstrations or benchmark tasks."],"relatedIds":["t3","unitac-nv","anytouch2"],"verified":"2026-10-17"},{"id":"tactile-wam","name":"Tactile-WAM","title":"Tactile-WAM: Touch-Aware World Action Model with Tactile Asymmetric Attention","date":"2026-06-25","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Protects video prediction from tactile-token interference while making action attention sensitive to predicted contact changes.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2606.26663"},"policy":{"family":"World-action model","baseModel":"Wan2.2-TI2V-5B diffusion Transformer and causal VAE","modelSize":"5B backbone component; total not reported","inputs":"RGB, tactile image mosaics, language and robot state","outputs":"Future tactile/visual latents and 24-step robot actions; execute 8","trainingData":"UniVTAC eight-task suite and ManiFeel nine-task suite demonstrations","trainingTime":"UniVTAC100k /ManiFeel60k updates; wall-clock not reported","trainingHardware":"8 NVIDIA A100 GPUs","controlRate":"ManiFeel observations10 fps; physics60 Hz/control decimation4; replan after8 actions","trainingMethod":"VideoClean asymmetric attention mask plus contact-change action-attention bias","evaluation":"UniVTAC and ManiFeel simulation"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."},"evidence":[{"url":"https://arxiv.org/abs/2606.26663","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://arxiv.org/html/2606.26663","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Published evaluation is simulation; real tactile transfer is not established."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."}},"relatedIds":[]},{"id":"icwm","name":"In-Context World Modeling (ICWM)","title":"In-Context World Modeling for Robotic Control","date":"2026-06-24","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Short task-agnostic interaction histories let a VLA infer the camera-to-action configuration before executing a task without parameter updates.","institution":"Fudan University / Shanghai Innovation Institute","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2606.26025"},"policy":{"family":"VLA","baseModel":"Qwen2.5-VL-3B with a shared context/system-identification transformer and FAST action tokenizer.","modelSize":"3B vision-language backbone; full aggregate not reported.","inputs":"Current RGB observation and instruction, plus short self-generated observation/action interaction clips.","outputs":"FAST-tokenized robot action chunks of five steps.","trainingData":"Simulated and real manipulation with viewpoint variation; real protocol splits 12 cameras into six training and six held-out viewpoints.","trainingTime":"Not reported","trainingHardware":"Eight NVIDIA A100 GPUs.","controlRate":"RTX 4090 model latency: 0.165 s with three context clips and 0.185 s with five, versus 0.112 s without context. This is inference latency, not low-level servo frequency.","trainingMethod":"Train action prediction jointly with configuration-aware context from task-agnostic exploration; no deployment weight update.","evaluation":"Simulated manipulation and real robot viewpoint generalization, including unseen camera configurations."},"release":{"code":"Not released","weights":"Not released","data":"Not released","license":"Not verified","notes":"No unambiguous author-linked executable release located in the original paper."},"evidence":[{"url":"https://arxiv.org/abs/2606.26025","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."}],"caveats":["World modeling denotes implicit system identification here; it is not a video-world-action generative model."],"verified":"2026-10-17"},{"id":"tacverse","name":"TacVerse","title":"TacVerse: A Multi-Sensor Dataset and Benchmark for Cross-Sensor Vision-Based Tactile Perception","date":"2026-06-24","year":2026,"collections":["benchmark"],"categories":["supporting-resources","tactile-benchmarks"],"resourceType":"dataset","summary":"Controlled tactile perception dataset assessing within-sensor learning, zero-shot sensor transfer and few-shot adaptation across seven optical sensor designs.","institution":"Imperial College London","venue":"Advanced Robotics Research 2026","modalities":["Tactile"],"environments":["Real world"],"embodiments":["Sensor-level resource"],"links":{"paper":"https://arxiv.org/abs/2606.25877","project":"https://lannwei.github.io/Tactile_Database/","code":"https://github.com/LannWei/Tactile_Database","data":"https://huggingface.co/datasets/Lan-2025/Tactile"},"benchmark":{"simulator":"Not applicable — measured tactile image dataset","simulatorVersion":"Not applicable","taskCount":"3 perception tasks, not closed-loop manipulation tasks","tasks":["9-way shape classification","30-way grating classification","Force regression"],"taskListScope":"Complete three-task perception benchmark.","capabilities":["Sensor-shift robustness","Tactile shape/texture recognition","Force estimation","Few-shot adaptation"],"metrics":"Classification accuracy and force-regression error under within-sensor / cross-sensor / few-shot splits.","robots":"Sensor data collection rigs; no common robot manipulation embodiment.","sensors":"GelSightNoMarker, GelSightMarker, MagicGripper, MagicTac, TacTip, ViTac, ViTacTip.","dataCollection":"Robot-controlled indenter contacts and orientation sweeps collect tactile images; synchronized six-axis force measurements label force-regression samples. This is perception data, not policy demonstrations.","data":"106,800 tactile images from seven vision-based tactile sensors.","protocol":"Trial/trajectory-disjoint chronological 60/20/20 train/validation/test split after near-duplicate filtering. Exhaustive 7×7 source-target shape transfer; fixed-source grating and force transfer. Few-shot target adaptation is force-regression only: 0.5%, 1%, 2.5%, 5% and 10% of target training samples, with untouched target test set.","baselines":"Transfer study: fixed ImageNet-initialized ViT. Representation study: ResNet-18, ViT from scratch, ImageNet ViT and MAE-initialized ViT; up to 30 epochs, batch128; reported server has four H200 GPUs, not a per-run allocation/time claim."},"release":{"code":"Available","weights":"Not verified","data":"Gated — automatic Hugging Face access terms","license":"Not verified","notes":"Project/data public; code and per-dataset terms should be inspected separately. The current official artifact inventory is gated (automatic access terms); files are listed but anonymous unrestricted download is not claimed."},"evidence":[{"url":"https://arxiv.org/abs/2606.25877","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://lannwei.github.io/Tactile_Database/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/LannWei/Tactile_Database","label":"Official repository","fields":["benchmark.simulatorVersion"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://advanced.onlinelibrary.wiley.com/doi/10.1002/adrr.70169","label":"Publisher full text","fields":["venue","benchmark.protocol","benchmark.baselines"],"note":"Advanced Robotics Research, first published 25 September 2026. Section4 gives leakage-controlled chronological split, fixed-source transfer, force-only few-shot protocol and backbone study. Entry date retains arXiv v1."},{"url":"https://arxiv.org/abs/2606.25877","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."},{"url":"https://github.com/LannWei/Tactile_Database","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/datasets/Lan-2025/Tactile","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["Perception benchmark supports encoder evaluation; does not measure manipulation policy success.","TacVerse in this entry is the Lan Wei multi-sensor perception resource, not an unrelated commercial data workbench."],"verified":"2026-10-19","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not verified","data":"Gated — automatic Hugging Face access terms","license":"Not verified","notes":"Project/data public; code and per-dataset terms should be inspected separately. The current official artifact inventory is gated (automatic access terms); files are listed but anonymous unrestricted download is not claimed."}},"relatedIds":[]},{"id":"halo","name":"HALO","title":"Memory Retrieval in Visuomotor Policies for Long-Horizon Robot Control","date":"2026-06-23","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Attention-based memory retrieval learns long-horizon visuomotor control using VLM-derived memory questions and online-policy data.","institution":"The University of Texas at Austin","venue":"RSS 2026","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2606.25136","project":"https://robin-lab.cs.utexas.edu/HALO","code":"https://github.com/UT-Austin-RobIn/HALO","data":"https://huggingface.co/datasets/Rutav/ReMemBench-Dataset"},"policy":{"family":"Transformer policy","baseModel":"Frozen CrossMAE-RTX vision encoder, local/top-k attention transformer, MLP 32-action head and auxiliary text-answer decoder.","modelSize":"Not reported","inputs":"RGB observations, proprioception, prior actions and long-horizon retrieved memory.","outputs":"32 future actions; auxiliary memory-question answers regularize learned representations.","trainingData":"ReMemBench demonstrations and policy-rollout data; VLM-generated memory QA labels supply auxiliary supervision.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Imitation learning with attention retrieval, distilled VLM memory priors and data augmentation addressing memory drift.","evaluation":"Memory-dependent long-horizon simulation and real-world robot control."},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"Apache-2.0 for repository code, verified from LICENSE.txt; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Official training/evaluation code and ReMemBench dataset are public; an upstream visual encoder checkpoint is not the final HALO policy checkpoint."},"evidence":[{"url":"https://arxiv.org/abs/2606.25136","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://robin-lab.cs.utexas.edu/HALO","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/UT-Austin-RobIn/HALO","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."},{"url":"https://github.com/UT-Austin-RobIn/HALO/blob/main/LICENSE.txt","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["Memory QA is training supervision; runtime need not query the teacher VLM for every action."],"verified":"2026-10-19"},{"id":"reflective-vla","name":"Reflective VLA","title":"Reflective VLA: In-Context Action Consequences Make VLAs Generalize","date":"2026-06-23","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Conditions a continuous-action VLA on bounded histories of observations, executed action chunks and observed consequences.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2606.25215"},"policy":{"family":"VLA","baseModel":"Mixture-of-Transformers VLA with either PaliGemma-3B or Qwen3-VL-2B visual-language prefix and a 1,024-hidden-dimension flow-matching action expert.","modelSize":"3B or 2B VLM component, depending on variant; total policy size is not reported.","inputs":"Language, third-person / available wrist RGB, proprioception and seven historical observation–action–consequence triplets plus the current query.","outputs":"Ten-step continuous action chunks; historical chunks are encoded with eight learned tokens each.","trainingData":"Benchmark demonstrations with bounded randomized milestone sampling; matched reactive baselines use the same data and architecture with one context unit.","trainingTime":"LIBERO reflective setting uses 50,000 training steps; other benchmark variants have separate schedules in Appendix C.","trainingHardware":"Eight NVIDIA H20 GPUs for all reported training runs.","controlRate":"Not reported","trainingMethod":"Offline flow matching with block-causal history conditioning and KV-cached inference; test-time history changes without online policy-weight training.","evaluation":"LIBERO, Simpler Bridge and LIBERO-Plus / Hard, plus real AgileX Piper manipulation."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2606.25215","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["Consequence conditioning differs from simply appending unstructured past images.","Backbone size denotes the VLM component, not the action expert plus full system."],"verified":"2026-10-17"},{"id":"cosmos3","name":"Cosmos3-Nano-Policy-DROID","title":"Cosmos 3: Omnimodal World Models for Physical AI","date":"2026-06-22","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Post-trains an omnimodal world model for closed-loop DROID manipulation with joint action/video prediction.","institution":"NVIDIA","venue":"Technical report","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://research.nvidia.com/labs/cosmos-lab/cosmos3/technical-report.pdf","project":"https://research.nvidia.com/labs/cosmos-lab/cosmos3/","code":"https://github.com/NVIDIA/cosmos-framework","weights":"https://huggingface.co/nvidia/Cosmos3-Nano-Policy-DROID"},"policy":{"family":"World-action model","baseModel":"Cosmos3-Nano Mixture-of-Transformers + new action/proprio encoders and decoder","modelSize":"Cosmos3-Nano 16B total, based on dense 8B transformer architecture","inputs":"Three RGB views arranged as an image canvas, language and current proprioception","outputs":"32 future absolute joint-position actions plus auxiliary RGB video","trainingData":"DROID 76k trajectories/350 h/86 tasks/564 scenes; backbone31.05T-token pretraining and action-aware midtraining","trainingTime":"Policy post-training elapsed time not reported; backbone token scale is reported separately","trainingHardware":"Backbone pretraining1,024 GB200 GPUs; policy inference 2 RTX Pro6000 GPUs; post-training compute not separately reported","controlRate":"15 Hz joint controller executes 32-action chunk; inference includes four denoising steps and CFG parallelism","trainingMethod":"World-model pre/midtraining followed by DROID policy post-training; four-step diffusion inference","evaluation":"RoboLab120, RoboArena real robot and MolmoSpaces simulation"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"OpenMDW-1.1 for DROID policy weights and cosmos-framework repository; separately bundled components may carry additional terms","notes":"Official model card gives the DROID policy-server entrypoint in cosmos-framework and downloadable weights; no installation or training performed."},"evidence":[{"url":"https://research.nvidia.com/labs/cosmos-lab/cosmos3/technical-report.pdf","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/NVIDIA/cosmos-framework","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://research.nvidia.com/labs/cosmos-lab/cosmos3/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://huggingface.co/nvidia/Cosmos3-Nano-Policy-DROID","label":"Official DROID model card:License andQuickstart","fields":["release.license","release.code","links.code"],"note":"Model card names OpenMDW1.1 and launches cosmos_framework.scripts.action_policy_server_robolab from the specific framework repository."},{"url":"https://raw.githubusercontent.com/NVIDIA/cosmos-framework/main/LICENSE","label":"Framework repository license","fields":["release.license"],"note":"Repository license isOpenMDW-1.1; not inferred from the general Cosmos platform."}],"caveats":["This entry covers the executable DROID policy, not all Cosmos3 reasoning/video modes.","No measured depth/tactile deployment input in the reported policy.","Backbone pretraining compute is not the cost of DROID adaptation."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Not verified","license":"OpenMDW-1.1 for DROID policy weights and cosmos-framework repository; separately bundled components may carry additional terms","notes":"Official model card gives the DROID policy-server entrypoint in cosmos-framework and downloadable weights; no installation or training performed."}},"relatedIds":[]},{"id":"enpire","name":"ENPIRE","title":"ENPIRE: Agentic Robot Policy Self-Improvement in the Real World","date":"2026-06-18","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Organizes real-world robot self-improvement around autonomous environment preparation, policy improvement, rollout collection and an evolution agent that revises the learning system.","institution":"NVIDIA; University of California, Berkeley; Carnegie Mellon University","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2606.19980","project":"https://research.nvidia.com/labs/gear/enpire/","code":"https://github.com/NVlabs/ENPIRE"},"policy":{"family":"Hybrid","baseModel":"Evolution agents include Codex / GPT-5.5 xhigh, Claude Code / Opus 4.7 High and Kimi Code / Kimi K2.6 thinking; robot policy backbones depend on the experiment.","modelSize":"Not reported","inputs":"Task objectives, robot observations, autonomous reset / verification feedback, rollout and training logs. Appendix D.1 exposes calibrated RGB-D camera APIs and cuRobo collision geometry refreshed from simulation; oracle-target APIs are gated / removed.","outputs":"Robot rollouts and updated learned or heuristic robot policies, plus revisions to collection, evaluation and training components.","trainingData":"Autonomous real-world and simulated interaction; exact data scale varies by experiment.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"The framework supports behavioral cloning, reinforcement learning and gradient-free code-policy search; the evolution agent can improve infrastructure and objectives.","evaluation":"Real bimanual YAM manipulation including pin-box, zip-tie and tool-use settings; Gym-PushT and RoboCasa365 simulations."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Apache-2.0 code, verified from LICENSE; THIRD_PARTY_NOTICES.md and THIRD_PARTY_LICENSES.md preserve external terms.","notes":"Actual environment/station APIs, code-as-policy agent, autonomous experiment tooling and actor/learner training runtime are public. Real runs require station calibration, reset/reward definitions and external model/asset setup. No standalone final policy checkpoint or full interaction corpus was independently identified."},"evidence":[{"url":"https://arxiv.org/abs/2606.19980","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/NVlabs/ENPIRE","label":"Author-linked repository","fields":["links.code"],"note":"Repository identity; code and checkpoint release status are stated separately."},{"url":"https://github.com/NVlabs/ENPIRE","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["ENPIRE is not universally training-free: its learned-policy experiments update model parameters.","Autonomous reset / reward instrumentation is part of the experimental environment.","Calibrated depth and collision-planning tools are part of the interface; the gated oracle-target API is not agent-accessible."],"verified":"2026-10-19"},{"id":"eventvla","name":"EventVLA","title":"EventVLA: Event-Driven Visual Evidence Memory for Long-Horizon Vision-Language-Action Policies","date":"2026-06-18","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Maintains initial/recent visual anchors and a sparse keyframe evidence memory for history-dependent robot actions.","institution":"USTC / Dalian University of Technology / Huawei / Shanghai AI Laboratory","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2606.20092","code":"https://github.com/XPolicyLab/XPolicyLab/tree/main/policy/EventVLA"},"policy":{"family":"VLA","baseModel":"Simulation: Qwen3-VL-4B-Instruct with QwenOFT; real robot: π0.5 / PaliGemma configuration","modelSize":"4B simulation VLM backbone; complete memory/head aggregate not reported; real robot uses a different backbone","inputs":"Current RGB, initial and recent frames, event-selected keyframes, language and robot state","outputs":"OFT or flow action chunks, horizon 50; real table pads actions to 32 dimensions","trainingData":"RMBench, 8-task RoboTwin-MeM, RoboTwin 2.0 demonstrations; real ARX ACONE memory tasks; training-only Qwen3-VL event labels","trainingTime":"Simulation 80k steps; real 60k steps; wall-clock not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"End-to-end action learning with event-keyframe prediction and annealed teacher-forced memory updates","evaluation":"RMBench, RoboTwin-MeM, RoboTwin 2.0 and real bimanual memory-dependent tasks"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Apache-2.0 for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2606.20092","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/XPolicyLab/XPolicyLab/tree/main/policy/EventVLA","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."},{"url":"https://github.com/XPolicyLab/XPolicyLab/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["Qwen3-VL event labeling is an offline training process; it is not a required extra model at deployment.","Public XPolicyLab integration is distinguishable from an independently packaged upstream release."],"verified":"2026-10-19"},{"id":"playful","name":"RATs / Playful Agentic Robot Learning","title":"Playful Agentic Robot Learning","date":"2026-06-17","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Learns a reusable skill library through self-directed play before receiving downstream task requests, selecting experiences that balance novelty and learnability.","institution":"Not reported","venue":"CoRL 2026 Spotlight","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2606.19419","project":"https://playful-rats.github.io","code":"https://github.com/Playful-RATs/rats"},"policy":{"family":"Robot agent","baseModel":"Frozen Code-as-Policy agent team with play-task proposal, execution and skill summarization roles.","modelSize":"Not reported","inputs":"Environment observations, available robot APIs, play history and skill-library context; downstream task instructions are supplied after play.","outputs":"Self-proposed play tasks, executable control programs and verified reusable skills.","trainingData":"Self-directed play rollouts in LIBERO-Pro and MolmoSpaces; downstream evaluation uses the retained skill library.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen language models; Goldilocks task selection and code-policy experimentation acquire skills. The library is frozen for held-out transfer.","evaluation":"LIBERO-Pro and MolmoSpaces, held-out Robosuite cross-environment transfer and physical skill transfer."},"release":{"code":"Available","weights":"Not applicable","data":"Not verified","license":"Apache-2.0","notes":"Official RATs implementation releases skill-acquisition/evaluation runtime and benchmark configuration. Agent skill memory is distinct from model weights."},"evidence":[{"url":"https://arxiv.org/abs/2606.19419","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/Playful-RATs/rats","label":"Official implementation, README and file/license inspection on 19 October 2026","fields":["links","release","venue"],"note":"Official RATs implementation releases skill-acquisition/evaluation runtime and benchmark configuration. Agent skill memory is distinct from model weights. Apache-2.0."}],"caveats":["Robosuite is held out from play and is a transfer environment rather than additional play-training data.","Code / skill learning here does not imply base-language-model parameter updates."],"verified":"2026-10-19"},{"id":"guava","name":"Guava-4B","title":"Guava: Distilling Frontier VLMs into a Compact Agent through a Robotic Manipulation Harness","date":"2026-06-16","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Distills physically validated frontier-model interaction traces into a compact tool-using manipulation agent, with perturbation branches teaching recovery.","institution":"University of Maryland, College Park; University of Illinois Urbana-Champaign; University of Waterloo; MBZUAI; University of Pennsylvania; Amazon FAR","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2606.18363","project":"https://guava-harness.github.io","code":"https://github.com/hdacnw/guava-release","weights":"https://huggingface.co/AIcell/guava-v13b-qwen3.5-4b"},"policy":{"family":"Robot agent","baseModel":"Qwen3.5-4B student trained from GPT-5.4 teacher interaction traces.","modelSize":"4B student backbone; teacher size is not disclosed.","inputs":"RGB images, end-effector state, tool returns and interaction history. Metric depth is used internally by grounding tools rather than directly exposed to the VLM.","outputs":"Reasoning and one semantic robot-tool call per turn, followed by fresh execution feedback.","trainingData":"2,268 validated simulated interaction trajectories, including 714 perturbation / recovery branches.","trainingTime":"SFT uses three epochs at learning rate 1e-5; elapsed duration is not reported. The paper also evaluates a distinct reinforcement-refinement setting.","trainingHardware":"Eight NVIDIA H100 80 GB GPUs, bf16; vision encoder and aligner remain frozen during the stated SFT.","controlRate":"Not reported","trainingMethod":"Student model weights are fine-tuned on teacher traces; the same harness is used for teacher collection and compact-agent deployment.","evaluation":"Custom MuJoCo agentic manipulation benchmark, LIBERO-Pro and real-world seen / unseen tasks, without real-world fine-tuning."},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Not verified","notes":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact. Actual checkpoint tensors are present in the author-linked Hub repository. Guava-4B is the released compact pilot; hosted teacher / external perception tools retain independent access requirements."},"evidence":[{"url":"https://arxiv.org/abs/2606.18363","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/hdacnw/guava-release/main/README.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."},{"url":"https://guava-harness.github.io","label":"Official project links / artifacts","fields":["links","release"],"note":"Author-linked repository / model / dataset endpoints are audited individually; a link alone does not establish a permissive asset license."},{"url":"https://huggingface.co/AIcell/guava-v13b-qwen3.5-4b","label":"Author-linked Hugging Face model files / card","fields":["links.weights","release.weights","release.notes"],"note":"Actual checkpoint tensors are present in the author-linked Hub repository. No card license was identified; code terms are not assigned to this asset."}],"caveats":["The current title identifies frontier-to-compact distillation; the survey cites an earlier universal-harness title for the same arXiv ID.","Depth belongs to the internal tool pipeline; the model-facing interface is not a raw depth-image encoder."],"verified":"2026-10-17"},{"id":"qwen-robotmanip","name":"Qwen-RobotManip","title":"Qwen-RobotManip Technical Report: Alignment Unlocks Scale for Robotic Manipulation Foundation Models","date":"2026-06-16","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"A manipulation VLA aligns state/action semantics, camera-relative motion and behavior prompts across heterogeneous robot and human-derived data.","institution":"Qwen / Alibaba","venue":"Technical report","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Dexterous hand","Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2606.17846","project":"https://qwen.ai/blog?id=qwen-robotmanip","repository":"https://github.com/QwenLM/Qwen-RobotManip"},"policy":{"family":"VLA","baseModel":"Qwen3.5-4B backbone plus a 10-block flow-matching DiT action expert with width 768 and 12 attention heads; alternating visual/language cross-attention.","modelSize":"4B vision-language backbone; complete action-expert and full-policy parameter totals not reported.","inputs":"Multi-camera RGB observations/history, language and embodiment prompts, canonical proprioceptive state and camera extrinsics for camera-aware encoding. No measured depth or tactile stream is reported.","outputs":"Continuous action chunks using an 80-dimensional canonical state/action layout: two 29D arm blocks and 22 reserved dimensions, with embodiment masks. Action expert uses 4 Euler integration steps.","trainingData":"Approximately 38100 hours of curated public robot/human-derived manipulation data; human-to-robot synthesis spans 15 platforms. Training also co-trains VL samples at robot: VL ratio 9: 1.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Four action-denoising Euler steps per prediction; universal latency/control frequency not reported. Dataset capture rates are not model-inference frequency.","trainingMethod":"Representation, motion and behavior alignment; dual-stream VLA/VLM co-training with flow matching and multimodal objectives, then downstream adaptation/co-training.","evaluation":"OOD RoboCasa365, LIBERO-Plus, EBench, RoboTwin Clean2Rand/instruction following/cross-embodiment settings; RoboChallenge Table30 v1 and real AgileX ALOHA, Franka, UR and ARX tests."},"release":{"code":"Not released","weights":"Not released","data":"Partial","license":"No released executable policy/model license verified; constituent source data terms remain separate.","notes":"Official repository is informational and explicitly states no current plan to release Qwen-RobotManip weights. The curated 38100-hour training mixture is not established as a single public release."},"evidence":[{"url":"https://arxiv.org/abs/2606.17846","label":"Paper and first-public arXiv record","fields":["title","date","institution","policy","modalities","embodiments","environments"],"note":"Technical fields follow the inspected full manuscript and appendices; date is arXiv v1, not the current revision."},{"url":"https://qwen.ai/blog?id=qwen-robotmanip","label":"Official project","fields":["summary","imageCandidates","release"],"note":"Current author project and release links."},{"url":"https://github.com/QwenLM/Qwen-RobotManip","label":"Official report repository and release statement","fields":["release","policy.inputs","policy.baseModel"],"note":"Explicit no-current-plan model-weights statement; repository report/demo resources do not constitute executable code."}],"caveats":["80D is a masked canonical interface, not 80 physical actuators on each robot.","Qwen-VLA and Qwen-RobotManip are distinct reports/configurations; dataset composition and action-head design differ."],"verified":"2026-10-17"},{"id":"rho","name":"RHO","title":"RHO: Your Coding Agent is Secretly a Roboticist","date":"2026-06-15","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A tool-enabled coding agent mutates complete robot-policy repositories; gated evaluation and a Pareto frontier retain useful program variants.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2606.16458","project":"https://rho-robotics.github.io"},"policy":{"family":"Robot agent","baseModel":"Coding agents including GPT-5.5 through Codex in the reported CaP-Bench setup.","modelSize":"Not reported","inputs":"Task specification, repository context, permitted robot-environment tools and rollout / evaluation feedback.","outputs":"Versioned multi-file robot-control repositories, executed through the configured perception, grasping and motion interfaces.","trainingData":"Repository-search rollouts and reflective feedback; no task-specific base-model weight updates.","trainingTime":"The reported benchmark configuration uses ten generations per cell and minibatches of three; wall-clock model-training time is not applicable.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen model; HELIX repository evolution, isolated candidate execution, training gates and Pareto-frontier selection.","evaluation":"LIBERO-Pro, Robosuite and an O3DE / RAI-ROS2 deployment study, with protocol-specific interfaces."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The official project links HELIX, a general codebase-evolution framework. Its source and BSD-3-Clause LICENSE do not independently establish release of the robot-specific Rho runtime, task checkpoints or corpus; those remain unverified."},"evidence":[{"url":"https://arxiv.org/abs/2606.16458","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["Frozen generated-code execution and the separate RAI-ROS2 language-model-in-the-loop condition are different controller variants.","Perception and grasp services are part of the execution interface; the coding model is not necessarily producing every actuator command."],"verified":"2026-10-19"},{"id":"t-rex","name":"T-Rex","title":"T-Rex: Tactile-Reactive Dexterous Manipulation","date":"2026-06-15","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Combines slow visual reasoning/action generation with a fast tactile expert for bimanual dexterous correction.","institution":"UC Berkeley","venue":"CoRL 2026","modalities":["RGB","Tactile","Force/Torque","Proprioception","Language"],"environments":["Real world"],"embodiments":["Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2606.17055","project":"https://tactile-reactive-dexterous.github.io/","code":"https://github.com/ZhuoyangLiu2005/T-Rex","data":"https://huggingface.co/datasets/zekaiwang/trex_dataset","weights":"https://huggingface.co/miniFranka/T-Rex_midtrain_mecka23k_ucb100_vqvae_epoch6"},"policy":{"family":"VLA","baseModel":"Variable-rate MoT; Qwen3VL-2B; temporal tactile VQ-VAE and deformation encoder","modelSize":"Latent expert 1.41B; action expert 1.41B; tactile expert 0.62B (reported components)","inputs":"Multi-view RGB, language, robot state, fingertip six-axis wrenches and deformation maps","outputs":"62D bimanual joint actions in 16-step chunks with tactile refinement","trainingData":"22,889 h human egocentric pretraining; 100 h robot tactile midtraining (7,755 episodes, 502 object–primitive pairs)","trainingTime":"Not reported","trainingHardware":"SFT configuration: 24 NVIDIA H100 GPUs","controlRate":"Not reported","trainingMethod":"Human pretraining, robot tactile midtraining, task adaptation; cascaded variable-rate flow matching","evaluation":"12 real-world dexterous manipulation tasks"},"release":{"code":"Available","weights":"Available","data":"Partial","license":"MIT for repository; model/data terms separate","notes":"Pretrain/midtrain checkpoints released; public data ≈50 h / 5,400+ trajectories, not the full 100 h corpus."},"evidence":[{"url":"https://arxiv.org/abs/2606.17055","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/ZhuoyangLiu2005/T-Rex","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2606.17055","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Human pretraining hours are tactile-free; robot tactile midtraining and public data scales differ."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Partial","license":"MIT for repository; model/data terms separate","notes":"Pretrain/midtrain checkpoints released; public data ≈50 h / 5,400+ trajectories, not the full 100 h corpus."}},"relatedIds":[]},{"id":"lawam","name":"LaWAM","title":"LaWAM: Latent World Action Models for Efficient Dynamics-Aware Robot Policies","date":"2026-06-14","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Predicts compact future visual subgoals in a learned latent world model rather than synthesizing future pixels, then generates conditioned actions.","institution":"Jilin University / Tsinghua University / Zhongguancun Academy","venue":"CoRL 2026","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2606.15768","project":"https://rlinf.github.io/LaWAM/","code":"https://github.com/RLinf/LaWAM","data":"https://huggingface.co/datasets/jialei02/robotwin_merged","weights":"https://huggingface.co/collections/jialei02/lawam-checkpoints"},"policy":{"family":"World-action model","baseModel":"Qwen3-VL-2B policy, 230M latent world/action module, DINOv3 features and action expert.","modelSize":"2.3B reported model, including 230M LaWM. WAM comparison counts exclude video VAE/text encoder where applicable.","inputs":"RGB, language and state; predicted latent actions/subgoals are internal, not measured depth.","outputs":"Embodiment-specific action chunks aligned by physical time; future latent predicted in one forward pass.","trainingData":"LaWM: approximately 3,000 robot-video hours plus 1,500 egocentric human-video hours. Policy integration uses language-labeled robot trajectories only.","trainingTime":"LaWM: 100k steps. Policy integration: 200k. RoboTwin posttraining: 100k steps, approximately 20 hours; not full training duration.","trainingHardware":"16 H100 GPUs for LaWM; 64 H100 for policy integration and RoboTwin posttraining.","controlRate":"187 ms per action chunk on A100, averaged over 1,000 calls with ten denoising steps; excludes robot/environment execution.","trainingMethod":"Latent world/action pretraining, latent-action distillation and future-feature supervision; fixed physical-time alignment across control rates.","evaluation":"LIBERO, RoboTwin 2.0 and physical pick/place, drawer opening and towel folding; latent-dynamics and mixed-frequency controls."},"release":{"code":"Available","weights":"Available","data":"Partial","license":"Not verified; pretrained backbones and data retain separate terms","notes":"Availability concerns author-linked artifacts; no training or installation reproduced."},"evidence":[{"url":"https://arxiv.org/abs/2606.15768","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/RLinf/LaWAM","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://rlinf.github.io/LaWAM/","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."}],"caveats":["Human video informs the dynamics prior; policy integration uses instruction-labeled robot data.","187 ms is action-chunk latency, not a servo interval."],"verified":"2026-10-17"},{"id":"recap-retrieval","name":"ReCAP","title":"Retrieve, Don't Retrain: Extending Vision Language Action Models to New Tasks at Test Time","date":"2026-06-14","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A residual world-action policy retrieves inexpensive cross-embodiment state-action chunks, allowing new tasks to be added to a frozen-policy retrieval pool.","institution":"NAVER AI Lab / Korea University","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2606.15631","project":"https://recap-robot.github.io/","code":"https://github.com/jeongeun980906/ReCAP-Cosmos-Policy","data":"https://huggingface.co/datasets/Jeongeun/ReCAP-Cosmos2.5-pusht","weights":"https://huggingface.co/Jeongeun/ReCAP-Cosmos2.5-pusht"},"policy":{"family":"World-action model","baseModel":"Retrieval-conditioned residual world-action policy. Cosmos Predict2.5 2B / Cosmos Policy; DINO visual descriptors and SAM3-assisted object features for retrieval.","modelSize":"2B base video model; complete policy count not reported.","inputs":"Current RGB, proprioception and task/object descriptors; retrieved observations, states and actions in a shared end-effector action space. Training retrieval additionally uses future query actions, removed at deployment.","outputs":"16-step residual action chunks over retrieved actions plus future-state prediction; execute one step before re-retrieval.","trainingData":"RoboTwin: 246 target/pool paired training episodes across five tasks; separate PushT triangular target/disc-pusher pool data.","trainingTime":"20,000 training iterations; wall-clock duration not reported.","trainingHardware":"8 GPUs, per-GPU batch 16 with four gradient accumulation steps; GPU model not reported.","controlRate":"Not reported","trainingMethod":"Train the retrieval-conditioned residual policy once, then extend indexed pool trajectories without model updates.","evaluation":"PushT unseen target angles, five held-out RoboTwin tasks with progressively expanded UR5 retrieval pool, and real cross-embodiment manipulation."},"release":{"code":"Partial","weights":"Available","data":"Partial","license":"Apache-2.0 code; model/data/backbone terms separate","notes":"Released implementation, checkpoint and data are explicitly PushT scoped; complete RoboTwin/physical release is not established."},"evidence":[{"url":"https://arxiv.org/abs/2606.15631","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://recap-robot.github.io/","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://github.com/jeongeun980906/ReCAP-Cosmos-Policy","label":"Official code","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://raw.githubusercontent.com/jeongeun980906/ReCAP-Cosmos-Policy/main/LICENSE","label":"Actual source license file","fields":["release.license"],"note":"Scope limited to the file/repository subtree carrying these terms, not paper figures or external models/data."}],"caveats":["Distinct from Physical Intelligence RECAP reinforcement learning used by π*0.6.","The pool requires state-action trajectories and a compatible action interface; raw human video alone is insufficient."],"verified":"2026-10-17"},{"id":"hy-embodied05","name":"Hy-Embodied-0.5-VLA","title":"Hy-Embodied-0.5-VLA: From Vision-Language-Action Models to a Real-World Robot Learning Stack","date":"2026-06-12","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Combines fingertip-UMI pretraining, temporal visual memory and preference-based robot post-training in a complete learning stack.","institution":"Tencent Robotics X / Futian Laboratory / Tencent Hy","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2606.14409","code":"https://github.com/Tencent-Hunyuan/Hy-Embodied-0.5-VLA","data":"https://huggingface.co/datasets/tencent/Hy-Embodied-0.5-VLA-Data","weights":"https://huggingface.co/tencent/Hy-Embodied-0.5-VLA-UMI"},"policy":{"family":"VLA","baseModel":"Hy-Embodied-0.5-MoT VLM; independent 370M flow action expert and temporal image encoder","modelSize":"370M action expert; total VLM-plus-action parameter count not reported in inspected recipe","inputs":"Current/history multi-view RGB, language and EEF proprioception","outputs":"Relative EEF chunks: xyz + 6D rotation + gripper = 10D per arm; default horizon 50","trainingData":"Hy-UMI-10K: over 10k hours of motion-capture-labelled UMI demonstrations; target-robot and preference rollout data","trainingTime":"Pretraining 200k steps, batch 1,024; real SFT 60k; FlowPRO 3 rounds × 25k optimizer steps = 75k total (Appendix C); wall-clock not reported","trainingHardware":"Accelerator model not reported; FlowPRO recipe global batch 20 /5 per GPU","controlRate":"Pretraining action samples10 Hz; real post-training actions50 Hz with horizon 50; measured replan latency not reported","trainingMethod":"Flow-matching pretraining and SFT; temporal memory; critic-/reward-model-free offline preference optimization (FlowPRO)","evaluation":"RoboTwin2; real intra-/cross-embodiment transfer and long-tail precision/robustness tasks"},"release":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2606.14409","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/Tencent-Hunyuan/Hy-Embodied-0.5-VLA","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."},{"url":"https://raw.githubusercontent.com/Tencent-Hunyuan/Hy-Embodied-0.5-VLA/main/LICENSE","label":"Repository license file","fields":["release.license"],"note":"Code scope; model, data and third-party component terms can differ."},{"url":"https://arxiv.org/html/2606.14409v2","label":"Appendix C: FlowPRO training recipe","fields":["policy.trainingTime"],"note":"Three rounds, 25k optimizer steps each, 75k total; corrected during independent cross-review."}],"caveats":["Fingertip UMI restores human haptic perception during collection; the robot policy does not thereby consume tactile observations."],"verified":"2026-10-17"},{"id":"scdp","name":"Spatially Conditioned Diffusion Policy (SCDP)","title":"Spatially Conditioned Diffusion Policy: Learning Precise and Robust Manipulation with a Single RGB Camera","date":"2026-06-12","year":2026,"collections":["policy"],"categories":["visual-precision-rl"],"resourceType":"policy","summary":"Single-camera diffusion policy that samples multi-scale visual features around intermediate end-effector trajectories during action denoising.","institution":"KAIST / Neuromeka","venue":"CoRL 2026","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2606.14535","project":"https://scdp-project.github.io/","code":"https://github.com/IRSL-robotics/SCDP"},"policy":{"family":"Diffusion","baseModel":"Conditional U-Net Diffusion Policy with a multi-scale visual encoder and trajectory-conditioned point-wise feature sampling.","modelSize":"Not reported","inputs":"Single fixed/global RGB camera and robot state, with camera calibration for trajectory projection. No measured depth or tactile stream.","outputs":"Continuous robot action chunks; prediction horizon 16, observation horizon 2, executed action horizon 8 (Appendix A.1).","trainingData":"Meta-World: 20 scripted demonstrations per task; DexArt: 100 RL-expert demonstrations per task. Real data: 180 USB insertion demos, 90 battery insertion demos, 120 long-horizon demos and 50 for each remaining task (Appendix C.1).","trainingTime":"Wall-clock duration not reported. Meta-World 1000 epochs; DexArt and real-world 3000 epochs, batch 128 (Table5).","trainingHardware":"Not reported","controlRate":"Appendix D: 27.9 ms for a 16-step SCDP prediction using 16 DDIM steps on RTX 5090 (versus 23.9 ms for DP). This is action-chunk latency, not a robot servo rate.","trainingMethod":"Behavior cloning through conditional action denoising; projected intermediate trajectories sample multi-scale image features at each denoising step.","evaluation":"54 simulation tasks: 50 Meta-World and 4 DexArt. Real tasks: USB insertion, battery insertion, cup-handle grasping, cup-wall grasping, drawer/banana long-horizon task and cube pushing, with distractor/clutter variants."},"release":{"code":"Partial","weights":"Not verified","data":"Not verified","license":"Apache-2.0 code; existing LeRobot/upstream components and datasets retain their original terms.","notes":"Current official implementation documents Meta-World and real-world train/inference; real robot control integration remains external. Full paper DexArt release not verified."},"evidence":[{"url":"https://arxiv.org/abs/2606.14535","label":"Paper and first-public arXiv record","fields":["title","date","institution","policy","modalities","embodiments","environments"],"note":"Technical fields follow the inspected full manuscript and appendices; date is arXiv v1, not the current revision."},{"url":"https://scdp-project.github.io/","label":"Official project","fields":["summary","imageCandidates","release"],"note":"Current author project and release links."},{"url":"https://github.com/IRSL-robotics/SCDP","label":"Official repository","fields":["release","policy.baseModel","policy.inputs","policy.evaluation","imageCandidates"],"note":"Current implementation/release scope can differ from paper experiments."},{"url":"https://github.com/IRSL-robotics/SCDP/blob/main/LICENSE","label":"Code license","fields":["release.license"]}],"caveats":["Trajectory reconstruction uses end-effector geometry and camera calibration; single RGB input does not mean calibration-free control.","Paper full-suite54-task results, hard-subset ablations and real task stages have different aggregation scopes."],"verified":"2026-10-17"},{"id":"contactworld","name":"ContactWorld","title":"ContactWorld: What Representations Matter for Vision-Tactile Latent World Models in Contact-Rich Manipulation","date":"2026-06-11","year":2026,"collections":["benchmark","policy"],"categories":["tactile-benchmarks","precision-assembly","tactile-foundation","rgbd-3d"],"resourceType":"benchmark","summary":"A controlled world-model planning study over 12 contact-rich manipulation tasks, comparing visual geometry and tactile channels.","institution":"Purdue University","venue":"Preprint","modalities":["RGB","Depth","Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2606.13877","project":"https://contact-world.github.io/","code":"https://github.com/PokuangZhou/ContactWorld","data":"https://huggingface.co/datasets/Pokuang/ContactWorld"},"benchmark":{"simulator":"Isaac Gym with tactile environments derived from ManiFeel / TacSL","simulatorVersion":"Not reported; installation uses a separately distributed Isaac Gym tactile package.","taskCount":"12 simulation task variants; 6 physical task settings on 2 platforms","tasks":["USB Insertion","Peg Insertion","Power Plug Insertion","Spike Barb","Flat Barb","Loose Lid","Bulb","Nut","Valve","Object Search","Sorting (Normal)","Sorting (Dim)"],"taskListScope":"Complete twelve simulation task labels from manuscript Table I. Physical subset: USB insertion, elastic-clip removal, valve rotation, ball sorting, peg insertion and occluded box search.","capabilities":["Action-conditioned world modeling","Contact-rich planning","Long-horizon prediction","Cross-modal compatibility"],"metrics":"Closed-loop task success, multi-step prediction error, planning frequency and parameter count.","robots":"Simulation Franka Panda; physical Franka Panda and reBot platforms.","sensors":"Wrist/front RGB; point clouds reconstructed from front RGB-D and subsampled to 1,024 XYZRGB points. Three local tactile representations: TacRGB, TacDepth and TacFF (normal plus two tangential components). Physical sensors include GelSight Mini and DM-Tac W2M configurations.","dataCollection":"6-DoF 3Dconnexion SpaceMouse teleoperation in both simulation and the physical robot study.","data":"Simulation 1,872 demonstrations / 247,518 interaction steps at 10 Hz; physical 80 demos per each of 6 tasks (50 train /30 held out).","protocol":"Simulation: 100K gradient updates; goal offsets 12/24/36/48 and 100 evaluation rollouts per task–modality–offset. Physical: 200 training epochs, final checkpoint without task-specific selection; 900 total trials across 6 tasks, 2 platforms and 3 tactile systems, with fixed 30 held-out initial–goal pairs per setting.","baselines":"JEPA-structured world models with a CEM-style planner; visual-image, point-cloud, TacRGB and TacFF combinations."},"release":{"code":"Available","weights":"Available","data":"Available","license":"Not verified","notes":"Official repository links training datasets, pretrained checkpoints and planner/rollout evaluation scripts."},"evidence":[{"url":"https://arxiv.org/abs/2606.13877","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://contact-world.github.io/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/PokuangZhou/ContactWorld","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://arxiv.org/pdf/2606.13877","label":"Full paper: Sections III-C and V; Figure 7","fields":["policy","benchmark.protocol","policySummary","imageCandidates"],"note":"Simulation 100K gradient updates and physical200 epochs are distinct settings. Planning controls are CEM-based; no pretrained foundation backbone is claimed."},{"url":"https://arxiv.org/abs/2606.13877","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["World-model prediction metrics and closed-loop planning success answer different questions.","The project is related to ManiFeel but is a separate world-model evaluation protocol; do not count shared scenes as independent evidence.","Current arXiv title was revised from What Matters in Vision-Tactile World Models for Contact-Rich Manipulation; same canonical work."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Available","data":"Available","license":"Not verified","notes":"Official repository links training datasets, pretrained checkpoints and planner/rollout evaluation scripts."},"policy":{"code":"Available","weights":"Available","data":"Available","license":"Not verified","notes":"Official repository links training datasets, pretrained checkpoints and planner/rollout evaluation scripts."}},"relatedIds":[],"policyName":"ContactWorld MPC","benchmarkSummary":"A controlled world-model planning study over 12 contact-rich manipulation tasks, comparing visual geometry and tactile channels.","policySummary":"A JEPA-style latent world model learns visuo-tactile dynamics from demonstrations and supplies a goal-conditioned CEM planner with receding-horizon control.","policy":{"family":"Model-based planning","baseModel":"From-scratch IMPALA-style RGB/tactile-image encoders, PointNet-style point-cloud encoder or taxel-wise TacFF MLP; latent concatenation and action-conditioned GRU dynamics with JEPA-style prediction.","modelSize":"Representation-dependent; Figure 7 visualizes model sizes with example bubble labels 1.6M, 5.4M and 17.9M. There is no single universal ContactWorld policy size.","inputs":"Current and goal sensory observations: wrist/front RGB or front RGB-D-derived XYZRGB point cloud, optionally paired with TacRGB, TacDepth or normal/tangential TacFF. Combinations are separately evaluated; no language instruction input.","outputs":"Relative Cartesian end-effector actions: 6D pose for insertion with fixed gripper, plus scalar gripper command for other categories. CEM plans 6 steps and executes only the first action before replanning.","trainingData":"Simulation: 1872 demonstrations, 247518 interaction steps at 10 Hz. Physical: 80 demos per each of 6 tasks, split into 50 training and 30 held-out evaluation demos per task.","trainingTime":"Wall-clock duration not reported. Simulation: 100K gradient updates, batch 64. Physical: 200 epochs with the final checkpoint and no task-specific checkpoint selection.","trainingHardware":"Not reported","controlRate":"Closed-loop environment/task-space control at 10 Hz. Model planning-frequency comparisons are a distinct profiling measurement, not the low-level robot servo rate.","trainingMethod":"Learn all encoders/dynamics from scratch using latent prediction, temporal similarity, inverse dynamics and VICReg-style regularization. Plan with CEM: 100 candidates, 4 optimization iterations and 8 elites, minimizing terminal latent-goal distance.","evaluation":"12 contact-rich simulated variants with goal offsets 12/24/36/48 and 100 rollouts per setting; 6 physical tasks across 2 robot platforms and 3 tactile sensing systems, totaling 900 trials."}},{"id":"ftp-1","name":"FTP-1","title":"FTP-1: A Generalist Foundation Tactile Policy Across Tactile Sensors for Contact-Rich Manipulation","date":"2026-06-11","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"A cross-sensor tactile foundation policy that maps image, array and state sensors into morphology-aware tokens and a shared tactile expert.","institution":"Tsinghua University","venue":"Preprint","modalities":["RGB","Tactile","Force/Torque","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2606.13102","project":"https://ftp1-policy.github.io/","code":"https://github.com/michaelyuancb/ftp1-policy","data":"https://huggingface.co/datasets/MJJJJ1064/FTP-1-Dataset","weights":"https://huggingface.co/MJJJJ1064/ftp1_v0426_50kstep"},"policy":{"family":"VLA","baseModel":"π0.5 vision-language/action experts; T3 image-tactile trunk","modelSize":"300M tactile expert; total model size not reported","inputs":"RGB, instruction, proprioception and sensor-specific tactile observations","outputs":"Flow-matching action chunks in a unified embodiment action space","trainingData":"≈3,000 h; 26 sources, 21 tactile sensors; human, dexterous-hand and gripper data","trainingTime":"50k pretraining updates; 20k downstream updates; wall-clock not reported","trainingHardware":"Pretraining: 48 NVIDIA H20; downstream: 8 A800","controlRate":"Not reported","trainingMethod":"Heterogeneous pretraining, followed by sensor/embodiment/task fine-tuning","evaluation":"Six UniVTAC tasks; Sharpa North, Sharpa–Dexmate and unseen Xense/Contactile setups"},"release":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Official repository links pretraining and six UniVTAC checkpoints, dataset and fine-tuning/deployment recipes."},"evidence":[{"url":"https://arxiv.org/abs/2606.13102","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/michaelyuancb/ftp1-policy","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2606.13102","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."},{"url":"https://github.com/michaelyuancb/ftp1-policy/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["State-type tactile sources include force/torque; their information differs from optical tactile images.","UniVTAC evaluation excludes Grasp Classify and Insert HDMI."],"verified":"2026-10-19","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Official repository links pretraining and six UniVTAC checkpoints, dataset and fine-tuning/deployment recipes."}},"relatedIds":[]},{"id":"mu0","name":"μ₀ / Trace Expert + Action Expert","title":"μ₀: A Scalable 3D Interaction-Trace World Model","date":"2026-06-11","year":2026,"collections":["policy"],"categories":["rgbd-3d","geometry-models"],"resourceType":"policy","summary":"A reusable 3D interaction-trace prior conditions a separately trained robot action expert.","institution":"University of Maryland; Seoul National University; collaborating authors","venue":"arXiv preprint","modalities":["RGB","Depth","Language","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2606.13769","project":"https://mu0-wm.github.io/","code":"https://github.com/Yoonkyo/mu0","weights":"https://huggingface.co/collections/furonghuang-lab/mu0"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Apache-2.0 for repository code; pretrained models, data and figure rights remain separate","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2606.13769","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://mu0-wm.github.io/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/Yoonkyo/mu0","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/Yoonkyo/mu0/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/Yoonkyo/mu0/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"379 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."},{"url":"https://huggingface.co/collections/furonghuang-lab/mu0","label":"Official weights release destination","fields":["release.weights"],"note":"Destination linked by the official project/repository; component release scope is retained in release notes."}],"caveats":["No action labels during pretraining does not mean action-free downstream policy training.","Depth is optional in trace-model design; comparisons must retain the selected input setting."],"verified":"2026-10-17","policy":{"family":"World-action model","baseModel":"SmolVLM2-2.2B prefix with modular trace expert, B-spline motion tokens and flow-matching action expert.","modelSize":"342M / 568M / 2.59B trace-inference variants; action-expert parameters separate","inputs":"RGB, optional metric depth, language, query points/history; downstream action expert also uses proprioception.","outputs":"3D traces and embodiment-specific continuous action chunks.","trainingMethod":"Video-only trace pretraining; freeze the prior and train the target action expert.","trainingData":"TraceExtract human/robot video traces; separate robot demonstrations for action training.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"4 NVIDIA L40S GPUs for RoboCasa downstream action-expert training","evaluation":"Trace forecasting, eight RoboCasa365 tasks and three real UR3 tasks.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"tac-dino","name":"Tac-DINO / VTPA","title":"Tac-DINO: Learning Vision-Tactile Features with Patch Alignment","date":"2026-06-10","year":2026,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Patch-aligned visual/tactile representation learning uses contact-local correspondence instead of assigning every tactile observation to the whole object.","institution":"Shanghai Jiao Tong University; Ant Group; Shanghai Innovation Institute","venue":"arXiv preprint 2026","modalities":["RGB","Tactile"],"environments":["Real robot"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2606.12069","project":"https://lihongcs.github.io/tacdino/"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Current author project provides figures and videos but no verified public implementation, checkpoint or Touch3D download destination."},"model":{"role":"Vision-tactile local-to-global alignment","family":"Multimodal representation","baseModel":"DINOv2 ViT-Large student/teacher with Vision-Tactile Patch Alignment; early/late fusion and contrastive alignment variants.","modelSize":"ViT-Large architecture; complete trainable parameter count not reported.","inputs":"Local tactile images and visual patches, depending on variant; scanned object geometry and annotated contact locations construct aligned training pairs.","outputs":"Visual/tactile descriptors for retrieval, material/object recognition and geometric correspondence; no action output in inspected arXiv v1.","trainingMethod":"DINO-style student/teacher learning with local visual-tactile patch alignment and contrastive objectives.","trainingData":"Touch3D: 505 objects, 20,025 tactile contacts, 18 materials; reported 4:1 train/test split. Filtered ObjectFolder Real subset: 85 objects and 2,839 contacts for additional evaluation.","trainingTime":"200 epochs including 40 warmup epochs; elapsed training time not reported.","trainingHardware":"Four NVIDIA H200 GPUs; batch size 32 per GPU.","inference":"Not reported in the inspected primary manuscript","evaluation":"Local-to-global retrieval, object/material recognition and geometric matching on Touch3D and ObjectFolder Real; perception evaluation in the inspected v1 manuscript."},"evidence":[{"url":"https://arxiv.org/abs/2606.12069","label":"Original manuscript and first public submission history","fields":["title","date","institution","model","policy","modalities","environments","caveats"],"note":"Full author method, evaluation and appendix reviewed; downstream controller probes are distinguished from the reusable model."},{"url":"https://lihongcs.github.io/tacdino/","label":"Official author project and current artifact destinations","fields":["release","venue","imageCandidates"],"note":"A project button or advertised artifact does not establish a downloadable release; paper-version differences are retained."}],"caveats":["The current author project additionally describes GeoTAC, CoMT-Bench and simulation policies. These additions are not present in the inspected arXiv v1 and are not merged into its quantitative model fields.","A depth camera records data collection and 3D scanning supplies alignment annotations; this does not imply a measured depth stream at a deployed action policy."],"verified":"2026-10-17"},{"id":"taccorl","name":"TacCoRL","title":"TacCoRL: Integrating Tactile Feedback into VLA via Simulation","date":"2026-06-10","year":2026,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"Adapts tactile-aware VLAs with supervised sim–real co-training and simulation reinforcement learning anchored by real demonstrations.","institution":"Not reported","venue":"CoRL 2026","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2606.11743","project":"https://tac-corl.github.io/"},"policy":{"family":"Hybrid","baseModel":"π0.5-style VLA with CNN2 temporal tactile encoder and prefix/suffix touch tokens","modelSize":"Not reported","inputs":"RGB, tactile feedback, robot state and task instruction","outputs":"10-step, 14D bimanual action chunks","trainingData":"Per task: 50 real demonstrations plus 20 simulated teleoperation demos expanded to 200 with MimicGen","trainingTime":"Supervised co-training1k updates(Assembly1/2) or3k(Puzzle/Test Tube); RL wall-clock not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Sim–real supervised co-training, followed by simulation RL with real-data regularization","evaluation":"Assembly1, Assembly2, Do Puzzle, Test Tube Insertion; 128 parallel sim environments and 20 real trials/task"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."},"evidence":[{"url":"https://arxiv.org/abs/2606.11743","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://arxiv.org/html/2606.11743","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Requires simulation/real camera, tactile and control interfaces to align.","Training hardware/model parameter count not verified from the inspected full text."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."}},"relatedIds":[]},{"id":"touchthinker","name":"TouchThinker / TouchThinker-1M","title":"TouchThinker: Scaling Tactile Commonsense Reasoning to the Open World with Large-scale Data and Action-aware Representation","date":"2026-06-10","year":2026,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Scales tactile commonsense question answering through sensor-diverse data and action-aware tactile representations, with held-out sensor/object evaluation.","institution":"National University of Singapore; Zhongguancun Academy; Xiamen University; CAS Institute of Automation; Xi’an Jiaotong University; Nanyang Technological University; Nanjing University","venue":"Preprint","modalities":["Tactile","Language"],"environments":["Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2606.11637","code":"https://github.com/lvkailin0118/TouchThinker","data":"https://huggingface.co/datasets/ckjs/TouchThinker"},"model":{"role":"Tactile-language reasoning and representation model; no executable robot action interface","family":"Tactile-language model","baseModel":"Action-aware tactile encoder and tactile-language adapter with Qwen2.5 7B/14B and LoRA","modelSize":"Qwen2.5 7B and 14B language backbones; tactile encoder/adapter additional","inputs":"Tactile video, question text and action-aware tactile context","outputs":"Tactile-property descriptions, comparison answers and physical commonsense reasoning text","trainingMethod":"Action-aware tactile representation and two-stage tactile-text alignment / end-to-end supervised fine-tuning with LLM LoRA","trainingData":"TouchThinker-1M: 1,001,344 tactile frames aggregated from nine sources, seven sensor types, 415 deduplicated objects and eight reasoning scenarios; paired QA data is not the same count as frames.","trainingTime":"Two stages, one epoch each in Appendix Table 5; wall-clock duration not reported","trainingHardware":"4 NVIDIA A100 80GB GPUs; Appendix Table 5 states batch 16 per GPU","inference":"Not reported — language generation and tactile encoding costs depend on variant","evaluation":"VTV150K, TouchThinker-Bench and held-out sensor/object tactile QA. Action-aware representation refers to data acquisition dynamics, not robot motor command generation."},"release":{"code":"Not released — current official repository contains README and assets only","weights":"Not verified — no official complete model checkpoint identified","data":"Available — official dataset repository","license":"No standalone code LICENSE identified; dataset and upstream Qwen/checkpoint terms require separate inspection","notes":"Official dataset archives are public; the inspected code repository currently contains documentation and figures, without model training source or released weights."},"evidence":[{"url":"https://arxiv.org/abs/2606.11637","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Current full-text v4 methods and Appendix A.3/Table 5; first-public date retained."},{"url":"https://github.com/lvkailin0118/TouchThinker","label":"Official repository and data release","fields":["release","links"],"note":"README and actual repository tree inspected; no independently confirmed model weights."}],"caveats":["TouchThinker-Bench tests perceptual reasoning, rather than robot-control task success.","One million refers to tactile source frames, not one million physical robot demonstrations.","Paper methods/counts follow the current revision; an unchanged arXiv identifier does not guarantee v1 specifications."],"relatedIds":["vtv-llm","octopi-15","tvl"],"verified":"2026-10-17"},{"id":"mu-vla","name":"μVLA","title":"$\\mu$VLA: On Recurrent Memory for Partially Observable Manipulation in VLA Models","date":"2026-06-10","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Controlled OpenVLA-OFT variants isolate recurrent memory tokens for partially observable manipulation.","institution":"CogAI Lab, Moscow","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2606.12497","project":"https://avanturist322.github.io/mu-vla/","code":"https://github.com/CognitiveAISystems/muVLA","weights":"https://huggingface.co/mu-vla/models"},"policy":{"family":"VLA","baseModel":"OpenVLA-OFT with recurrent memory tokens, LoRA and truncated backpropagation through time.","modelSize":"OpenVLA-OFT backbone; additional memory varies with m tokens of hidden dimension 4,096. A separate aggregate parameter count is not reported.","inputs":"Two RGB views, robot state/instruction and recurrent memory tokens carried across timesteps.","outputs":"Eight-step action chunks; evaluation uses receding-horizon one-call-per-environment-step execution.","trainingData":"Not reported","trainingTime":"Up to 150,000 training steps; batch size four per GPU.","trainingHardware":"Eight NVIDIA H100 80GB GPUs with DDP.","controlRate":"Not reported","trainingMethod":"Action imitation with TBPTT; memory width, update rule and gradient horizon are controlled experimental factors.","evaluation":"Five MIKASA-Robo training tasks and held-out memory tasks; LIBERO evaluation is also included in the public repository."},"release":{"code":"Available","weights":"Available","data":"Partial","license":"MIT for repository code; external model, dataset and dependency terms remain separate.","notes":"Official recurrent training/evaluation code and model collection are public; benchmarks provide underlying demonstrations."},"evidence":[{"url":"https://arxiv.org/abs/2606.12497","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://avanturist322.github.io/mu-vla/","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/CognitiveAISystems/muVLA","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."},{"url":"https://github.com/CognitiveAISystems/muVLA/blob/HEAD/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Actual license file inspected; code license does not automatically license data or weights."}],"caveats":["Receding-horizon μVLA inference is not directly comparable to baselines that execute complete chunks between calls.","Memory-structure shifts remain a distinct held-out generalization test."],"verified":"2026-10-17"},{"id":"hi-vla-design","name":"Hierarchical VLA Design Study","title":"What Matters in Orchestrating Robot Policies: A Systematic Study of Hierarchical VLA Agents","date":"2026-06-09","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Systematically varies planner, low-level policy, switching, observation and memory interfaces, then instantiates stronger hierarchical robot agents.","institution":"Google DeepMind","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2606.10267","project":"https://jiahenghu.github.io/hi-vla"},"policy":{"family":"Hybrid","baseModel":"Main stack: Gemini 2.5 planners with Gemini Robotics On-Device (GROD). Cross-stack validation uses Qwen3-VL and π-family VLAs.","modelSize":"Default GROD executor is the 3B variant; cross-stack planners include Qwen3-VL 4B / 8B / 32B.","inputs":"Task, robot / visual observations, recent history or summaries, and stage / termination context; representations are varied in matched studies.","outputs":"High-level subgoal commands and low-level VLA action chunks, with configurable execution horizons and switching criteria.","trainingData":"Existing real-data-trained GROD policies and separately fine-tuned variants; the study varies narrow fine-tuning rather than claiming one universal training dataset.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Hierarchical options-style execution and controlled interface studies; model preparation / fine-tuning and runtime orchestration are distinct factors.","evaluation":"Short-horizon, long-horizon and reasoning-intensive manipulation in simulation and real ALOHA; cross-stack LIBERO validation uses three tasks with 150 trials each."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The original paper is public; code, model and dataset release scopes are verified separately where established."},"evidence":[{"url":"https://arxiv.org/abs/2606.10267","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."}],"caveats":["This is an empirical design study with concrete agent configurations, not a single universally trained new VLA.","Its default eight-second execution interval is a planner-switching horizon, not a policy inference rate."],"verified":"2026-10-17"},{"id":"aha-wam","name":"AHA-WAM","title":"AHA-WAM:Asynchronous Horizon-Adaptive World-Action Modeling with Observation-Guided Context Routing","date":"2026-06-08","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Asynchronous world planner and action executor reuse routed visual context while updating control from current observations.","institution":"Shanghai Jiao Tong University / Baidu AI Cloud","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2606.09811","project":"https://serene-sivy.github.io/aha-wam/","code":"https://github.com/serene-sivy/AHA-WAM","data":"https://huggingface.co/datasets/yuanty/robotwin2.0-fastwam","weights":"https://huggingface.co/SereneC/AHA-WAM-RoboTwin2.0"},"policy":{"family":"World-action model","baseModel":"Wan2.2-TI2V-5B planner, compact Action DiT, rolling KV memory and observation-guided context routing.","modelSize":"7.23B instantiated: 4.99B video planner, 1.02B Action DiT and 1.22B memory/routing modules.","inputs":"RGB views, proprioception and language; physical Piper deployment uses head-view RGB only.","outputs":"14D bimanual actions; 64-action training horizon split into four 16-action chunks.","trainingData":"RoboTwin: 50 clean and 500 randomized demonstrations per task across 50 tasks. Physical runs separately pretrain on 24,600 RoboCOIN trajectories, approximately 165 hours, then use approximately 120 demonstrations per task.","trainingTime":"Five training epochs; Flash adds five distillation epochs. Wall-clock duration not reported.","trainingHardware":"Training hardware not reported; latency measured on one RTX 5090D.","controlRate":"41.37 ms per chunk / 24.17 Hz reported closed-loop frequency. Distilled Flash: 17.56 ms / 56.95 Hz, a separate sampler setting.","trainingMethod":"Flow matching, randomized planner–executor offsets and context routing; optional ODE distillation.","evaluation":"50 RoboTwin tasks; physical Fold Towel, Organize Desktop, Prepare Soy Milk and Store Plate with OOD variants."},"release":{"code":"Available","weights":"Available","data":"Partial","license":"MIT code; weights/data/backbone terms separate","notes":"Availability concerns author-linked artifacts; no training or installation reproduced."},"evidence":[{"url":"https://arxiv.org/abs/2606.09811","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/serene-sivy/AHA-WAM","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://serene-sivy.github.io/aha-wam/","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."}],"caveats":["No robot pretraining applies to simulation comparisons; physical runs explicitly use a 165-hour RoboCOIN pretraining subset.","Horizon-adaptive means robustness to phase offsets, not online horizon selection."],"verified":"2026-10-17"},{"id":"memoryvla-plusplus","name":"MemoryVLA++","title":"MemoryVLA++: Temporal Modeling via Memory and Imagination in Vision-Language-Action Models","date":"2026-06-08","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Adds memory-guided future imagination in denoising latent space to the perceptual-cognitive memory policy.","institution":"Tsinghua University / Dexmal / HKU","venue":"Preprint","modalities":["RGB","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2606.09827","project":"https://shihao1895.github.io/MemoryVLA-PP-Web"},"policy":{"family":"World-action model","baseModel":"Prismatic/DINOv2/SigLIP/LLaMA-7B; Stable Video Diffusion world model; diffusion action expert","modelSize":"7B language backbone, 1.5B SVD world model and approximately 300M action expert; other encoders/memory additional","inputs":"Current/history RGB and task language; stored perceptual/cognitive tokens guide imagined future latents","outputs":"Continuous 7DoF diffusion action sequences; efficiency study chunk16","trainingData":"OXE-pretrained backbone; Bridge, RT-1, LIBERO, MIKASA, CALVIN and real robot demonstrations; task-specific world-model adaptation","trainingTime":"World model20k–40k steps by suite; policy20k–60k+ by suite; total wall-clock not reported","trainingHardware":"8 × NVIDIA A100 or H20; global batch 208–256","controlRate":"Chunk16 latency0.241s RTX4090 /0.301s H20; 66.4/53.2 action-steps/s are chunk throughput, not observation feedback Hz","trainingMethod":"Memory retrieval/consolidation plus memory-guided SVD latent imagination, conditioning a diffusion action expert","evaluation":"Five simulation benchmarks and general, memory-dependent, imagination-dependent tasks on three real robots"},"release":{"code":"Not released","weights":"Not released","data":"Not verified","license":"Not reported","notes":"Paper/project available; linked repository currently releases original MemoryVLA and MemoryVLA+ code, not the+ + implementation."},"evidence":[{"url":"https://arxiv.org/abs/2606.09827","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/shihao1895/MemoryVLA","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."}],"caveats":["Distinct extended manuscript from MemoryVLA; original, + and++ are not interchangeable checkpoint labels.","Repository says++ code/weights will be released; model-zoo+ + assets remainTBD."],"verified":"2026-10-17"},{"id":"torl-vla","name":"TORL-VLA","title":"TORL-VLA: Tactile Guided Online Reinforcement Learning for Contact-Rich Manipulation","date":"2026-06-08","year":2026,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"Refines a frozen tactile-wrench-aware VLA online using stage-wise RL and an intervention-censored critic.","institution":"Meituan","venue":"Preprint","modalities":["RGB","Tactile","Force/Torque","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2606.09337","project":"https://torl-vla.github.io/"},"policy":{"family":"Hybrid","baseModel":"Wrench-aware VLA plus lightweight online actor–critic","modelSize":"Not reported","inputs":"Three RGB views, state, instruction and tactile-derived 12D wrench history","outputs":"50-step action/wrench references; refines and executes first 10 actions","trainingData":"Not reported","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"20 Hz commands; 0.5 s replanning interval; inference timing measured separately on RTX 4090","trainingMethod":"Supervised action/wrench prediction followed by online RL from autonomous and human-intervention data","evaluation":"Long-horizon latch manipulation, coffee-cup placement and egg handling"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The current official project links openpi-RLT, whose actual implementation identifies itself as an RLT-style real-robot reproduction. This does not establish release of the paper’s full wrench-aware TORL-VLA system, task-specific checkpoint or corpus; those remain unverified."},"evidence":[{"url":"https://arxiv.org/abs/2606.09337","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://torl-vla.github.io/","label":"Author project page","fields":["links"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://arxiv.org/html/2606.09337","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Deployment uses tactile-derived wrench summaries rather than raw tactile images.","Human interventions and real interaction are additional training resources."],"verified":"2026-10-19","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The current official project links openpi-RLT, whose actual implementation identifies itself as an RLT-style real-robot reproduction. This does not establish release of the paper’s full wrench-aware TORL-VLA system, task-specific checkpoint or corpus; those remain unverified."}},"relatedIds":[]},{"id":"dream-tac","name":"Dream-Tac","title":"Dream-Tac: A Unified Tactile World Action Model for Contact-Rich Robot Manipulation","date":"2026-06-07","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Joint video/contact/action modeling with contact-gated tactile attention and diffusion-step caching.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2606.08737","code":"https://github.com/LYFCLOUDFAN/Dream-Tac"},"policy":{"family":"World-action model","baseModel":"Cosmos-Predict2-2B Video2World","modelSize":"2B video backbone component; total policy size not reported","inputs":"First-/third-person RGB, tactile images, proprioception and language","outputs":"20-step actions plus future visual/tactile predictions","trainingData":"100 demonstrations for each of six real tasks","trainingTime":"Warmup 2k updates; schedule beyond 20k; total wall-clock not reported","trainingHardware":"8 NVIDIA H100 GPUs; attention microbenchmark additionally measured on H200","controlRate":"Uncached 10-step inference ≈5 Hz on A800; cache reduces full evaluations to two; data 30 Hz","trainingMethod":"Joint latent denoising with contact-aware logit bias and cached denoising","evaluation":"Peel Cucumber, Cut Banana, Insert USB, Play Mahjong, Pick and Place, Clean Whiteboard"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Official repository contains training/inference and model download instructions."},"evidence":[{"url":"https://arxiv.org/abs/2606.08737","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/LYFCLOUDFAN/Dream-Tac","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2606.08737","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."},{"url":"https://github.com/LYFCLOUDFAN/Dream-Tac/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["Changing tactile-pixel magnitude controls the contact gate; cross-sensor calibration matters.","5 Hz is uncached model inference, not the low-level actuation rate."],"verified":"2026-10-19","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Official repository contains training/inference and model download instructions."}},"relatedIds":[]},{"id":"motionvla","name":"MotionVLA","title":"MotionVLA: Injecting Geometric Motion into Vision-Language-Action Model","date":"2026-06-06","year":2026,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Past-only geometric motion history conditions a VLA without providing future observations during deployment.","institution":"Huazhong University of Science and Technology; D-Robotics; Wuhan University","venue":"arXiv preprint","modalities":["RGB","Language","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2606.08288"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2606.08288","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."}],"caveats":["Geometric history is inferred from RGB, not an additional measured depth modality.","History excludes the current frame and contains only earlier selected observations."],"verified":"2026-10-17","policy":{"family":"VLA","baseModel":"π₀ flow-matching VLA augmented by frozen trajectory-field extraction, past-only motion tokens and query-conditioned fusion.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Current RGB, past head-camera RGB history, language and robot state.","outputs":"Continuous action chunks; future trajectory head is only an auxiliary training objective.","trainingMethod":"Generic-to-specific action training with auxiliary trajectory supervision detached in target adaptation.","trainingData":"100-hour heterogeneous manipulation mixture; over 100 physical demonstrations per task.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"8 NVIDIA H20 for physical-task model training; single A800 evaluation","evaluation":"RoboTwin2 and preliminary real AgileX Piper manipulation.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"synthicl","name":"SynthICL","title":"SynthICL: Scalable In-context Imitation Learning with Synthetic Data","date":"2026-06-06","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A visual-context flow-matching policy learns in-context imitation from synthetic pseudo-demonstrations and an auxiliary subgoal-image objective.","institution":"Not reported","venue":"CoRL 2026","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2606.08154","project":"https://synth-icl.github.io","code":"https://github.com/sNiper-Qian/synthicl"},"policy":{"family":"In-context policy","baseModel":"Frozen DINOv3 image encoder, spatiotemporal context transformer and flow-matching action transformer with subgoal-image prediction.","modelSize":"Not reported","inputs":"Current multi-view RGB images, robot state and up to six selected multi-view demonstration frames. Demonstration actions, metric depth and exact camera calibration are not required.","outputs":"Eight-step, seven-dimensional action chunks; ten flow-integration steps and temporal ensembling are used at inference.","trainingData":"Three million synthetic trajectories in Isaac Sim for real-world training; object assets are drawn from RoboTwin-OD / ShapeNet and randomized context–query trajectory pairs.","trainingTime":"Appendix reports 2,000 training epochs and batch size 64; elapsed duration is not reported.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Offline synthetic in-context training with flow matching and auxiliary subgoal images; a new demonstration conditions the frozen policy.","evaluation":"24 RLBench tasks and 16 real-world tasks; the paper reports 79% real-world success from one visual demonstration."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Apache-2.0 code; DINOv3 backbone remains subject to its own gated terms","notes":"Official training implementation includes ICIL package and scripts. Upstream gated DINOv3 weights do not establish a released trained SynthICL checkpoint."},"evidence":[{"url":"https://arxiv.org/abs/2606.08154","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/sNiper-Qian/synthicl","label":"Official implementation, README and file/license inspection on 19 October 2026","fields":["links","release","venue"],"note":"Official training implementation includes ICIL package and scripts. Upstream gated DINOv3 weights do not establish a released trained SynthICL checkpoint. Apache-2.0 code; DINOv3 backbone remains subject to its own gated terms."}],"caveats":["RGB-only deployment still assumes approximately front and side workspace views and robot state.","Using object point clouds to synthesize training grasps does not make depth an inference input."],"verified":"2026-10-19"},{"id":"volo","name":"VoLoAgent","title":"VoLo: A Physical Orchestrator for Open-Vocabulary Long-Horizon Manipulation","date":"2026-06-05","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Orchestrates interruptible robot policies and perception tools while monitoring execution and recovering from open-vocabulary manipulation failures.","institution":"NVIDIA; University of Michigan","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2606.07723","project":"https://chicychen.github.io/VoLo/","code":"https://github.com/NVlabs/VoLoAgent"},"policy":{"family":"Hybrid","baseModel":"Claude Opus 4.6 orchestrator with π0.5, SAM3, Molmo2 and grasp / place tools. Alternatives include GPT-5.5, Gemini 2.5 Flash, Qwen3-VL-8B and DreamZero-DROID.","modelSize":"Qwen alternative: 8B; DreamZero alternative: 14B. Hosted VLM sizes are undisclosed.","inputs":"Task, front-camera RGB, recent execution context and longer task memory; depth-projected placement supports the geometric tools.","outputs":"Interruptible VLA / WAM rollouts, perception queries, grasp / place primitives and recovery decisions.","trainingData":"Existing pretrained policies and perception tools; no new jointly trained orchestrator is required by the reported interface.","trainingTime":"Not reported","trainingHardware":"Evaluation uses an L40S-class GPU for simulator / grasp tools and a separate H100 or L40 for π0.5; 14B DreamZero uses two H100s.","controlRate":"VLA / primitives execute at 15 Hz; VLM monitors at 0.2 Hz. These are separate system clocks.","trainingMethod":"Frozen model orchestration with asynchronous policy execution, observation-based interruption and tool-mediated recovery.","evaluation":"RoboVoLo: 126 long-horizon tasks in fifteen categories and four suites, plus real-world validation."},"release":{"code":"Available","weights":"Not applicable","data":"Not verified","license":"Apache-2.0 (code); model, dependency and dataset terms are separate.","notes":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms."},"evidence":[{"url":"https://arxiv.org/abs/2606.07723","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/NVlabs/VoLoAgent/main/README.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."},{"url":"https://raw.githubusercontent.com/NVlabs/VoLoAgent/main/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Code-license text checked directly; this license is not automatically assigned to model weights or third-party data."}],"caveats":["RoboVoLo is also introduced as a benchmark; this record describes the executable agent rather than duplicating an incompletely mapped benchmark role.","The policies, geometric primitives and orchestrator have different input and compute requirements."],"verified":"2026-10-17"},{"id":"wla","name":"WLA-0 / WLA","title":"World-Language-Action Model for Unified World Modeling, Language Reasoning, and Action Synthesis","date":"2026-06-04","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Combines autoregressive subtask reasoning, a training-time visual world expert and flow-matching robot actions; optionally ranks candidate actions using imagined states.","institution":"Shanghai Jiao Tong University / SII","venue":"CoRL 2026 (official repository)","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2606.05979","project":"https://github.com/SJTU-DENG-Lab/WLA","code":"https://github.com/SJTU-DENG-Lab/WLA","data":"https://huggingface.co/datasets/SJTU-DENG-Lab/RoboTwin-LeRobot","weights":"https://huggingface.co/collections/SJTU-DENG-Lab/wla"},"policy":{"family":"World-action model","baseModel":"RynnBrain-2B autoregressive backbone; SANA-600M world expert; flow-matching action expert","modelSize":"3.4B total; components: 2.1B backbone / 900M world including VAE / 390M action; paper reports ≈ 2B active in efficient mode","inputs":"Current/history RGB observations, robot state and language; accumulated subtask text in memory-dependent settings","outputs":"Action chunks: 8 steps LIBERO, 32 elsewhere; optional subtask text and future static frame; released RoboTwin configurations use 16D EE or 14D joint interfaces","trainingData":"RoboTwin: 2,500 clean + 25,000 randomized trajectories; LIBERO 40 tasks × 50 demos; real 60 demos/task","trainingTime":"RoboTwin/LIBERO 100k updates; RMBench 30k/task; real 50k; wall-clock not reported","trainingHardware":"Training accelerator model not reported; released train.sh launches 8 processes; inference RTX5090","controlRate":"Efficient-mode inference ≈ 40 ms on RTX5090; synchronous execution; command/servo rate not reported","trainingMethod":"Joint intention/world/action learning through meta-queries; disable world expert in efficient mode or enable value-ranked test-time scaling","evaluation":"RoboTwin 2.0, LIBERO, RMBench; real Piper dual-arm robust/dynamic tasks"},"release":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 code; model/data terms require individual Hub cards","notes":"Official code includes training/evaluation scripts and LIBERO/RoboTwin/RMBench checkpoints; no local reproduction performed."},"evidence":[{"url":"https://arxiv.org/abs/2606.05979","label":"Primary manuscript and submission history","fields":["date","title","policy","modalities","environments","embodiments"],"note":"Publication date is first arXiv submission; quantitative details retain the reported setting."},{"url":"https://github.com/SJTU-DENG-Lab/WLA","label":"Official model repository","fields":["release","links","policy.inputs","policy.outputs"],"note":"Deployment observations, released checkpoint versions and paper claims are checked separately."},{"url":"https://arxiv.org/html/2606.05979v1","label":"Full paper: Section 4.1 and evaluation settings","fields":["policy.modelSize","policy.trainingData","policy.trainingTime","policy.controlRate"],"note":"Total and active sizes have different scopes; inference latency is measured on RTX5090."},{"url":"https://raw.githubusercontent.com/SJTU-DENG-Lab/WLA/main/train.sh","label":"Official training launcher","fields":["policy.trainingHardware"],"note":"Eight launched processes is not evidence of a particular accelerator model."},{"url":"https://raw.githubusercontent.com/SJTU-DENG-Lab/WLA/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Apache-2.0, code scope only."}],"caveats":["No embodied pretraining means no extra robot pretraining; RynnBrain/SANA foundation components are pretrained.","Efficient mode omits visual world prediction; active-parameter count is the manuscript claim rather than a recomputed sum.","Do not conflate WLA with WorldVLA, T-WLA or WALL-WM."],"verified":"2026-10-17"},{"id":"geoalign","name":"GeoAlign","title":"GeoAlign: Beyond Semantics with State-Guided Spatial Alignment in VLA Models","date":"2026-06-02","year":2026,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"State-conditioned queries select RGB-derived geometry tokens for action generation after robot-domain depth-supervised pretraining.","institution":"Tongji University; Shanghai Innovation Institute; Shanghai Jiao Tong University; collaborating universities and HONOR","venue":"arXiv preprint","modalities":["RGB","Language","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2606.03240","project":"https://chenyizhi123.github.io/geoalign-project/"},"release":{"code":"Not released","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Official project labels code coming soon; upstream GR00T/DA-V2 artifacts do not constitute GeoAlign checkpoints."},"evidence":[{"url":"https://arxiv.org/abs/2606.03240","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://chenyizhi123.github.io/geoalign-project/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."}],"caveats":["Depth is offline supervision; deployment geometry features are inferred from RGB.","Additional RGB-D pretraining data differs from RGB-only baselines."],"verified":"2026-10-17","policy":{"family":"VLA","baseModel":"GR00T N1.6-3B / Eagle-Block2A-2B; frozen DA-V2-S geometry encoder and state-query cross-attention.","modelSize":"GR00T 3B backbone; geometry encoder 24.8M","inputs":"RGB, language and proprioception; no measured or predicted depth maps at policy rollout.","outputs":"Flow-matching action chunks.","trainingMethod":"Metric-depth post-training followed by policy learning; depth head discarded.","trainingData":"580,960 RobbyVLA RGB-D frames for geometry training; separate target robot demonstrations.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"LIBERO, three SimplerEnv-Fractal task families and eight real ALOHA tasks.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"instant-fold","name":"Instant-Fold","title":"Instant-Fold: In-Context Imitation Learning for Deformable Object Manipulation","date":"2026-06-02","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Demonstration-conditioned flow matching learns diverse cloth-folding modes in simulation and transfers to real manipulation without adaptation.","institution":"Imperial College London","venue":"Preprint","modalities":["RGB","Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2606.04269","project":"https://instant-fold.github.io","code":"https://github.com/kelthuzadyl/Instant-Fold"},"policy":{"family":"Flow matching","baseModel":"DINOv3-ViT-S/16+ deformation-aware encoder with LoRA and a 384-D spatial/temporal flow-matching transformer.","modelSize":"Not reported","inputs":"One demonstration and current RGB-D / robot-state observations; depth supports 3D context geometry.","outputs":"Four-step continuous position-delta action horizon; ten rectified-flow denoising steps.","trainingData":"Approximately 80k simulated trajectories over 19 folding modes; Cloth3D split is 300 training and 60 held-out garment meshes.","trainingTime":"Policy: 500 epochs / 225k optimization steps at batch size 64; wall-clock duration not reported.","trainingHardware":"Encoder pretraining explicitly uses one RTX PRO 6000 Blackwell; policy training hardware is not separately established.","controlRate":"Not reported","trainingMethod":"Temporal contrastive visual pretraining, followed by demonstration-conditioned flow matching; simulation-only policy training.","evaluation":"Simulated folding across held-out garments / modes and zero-shot real-world folding."},"release":{"code":"Available","weights":"Not verified","data":"Partial","license":"MIT for repository code; external model, dataset and dependency terms remain separate.","notes":"Public release supports evaluation, local data generation, encoder pretraining and policy training. Exact released checkpoints are checked separately from commands accepting local checkpoint paths."},"evidence":[{"url":"https://arxiv.org/abs/2606.04269","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://instant-fold.github.io","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/kelthuzadyl/Instant-Fold","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."},{"url":"https://github.com/kelthuzadyl/Instant-Fold/blob/HEAD/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Actual license file inspected; code license does not automatically license data or weights."}],"caveats":["RTX PRO 6000 is explicitly reported for representation training; do not apply that hardware claim to the entire pipeline."],"verified":"2026-10-17"},{"id":"galaxea-g05","name":"Galaxea G0.5","title":"Galaxea G0.5: One Autoregressive Stream for Robot Reasoning and Action","date":"2026-06-01","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Uses one autoregressive Qwen transformer for interleaved reasoning/action tokens, with an ActionCodec and visual memory instead of a separate flow expert.","institution":"Galaxea","venue":"Official project / technical release","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Mobile manipulator"],"links":{"paper":"https://opengalaxea.github.io/G05/Galaxea_G0_5.pdf","project":"https://opengalaxea.github.io/G05/","code":"https://github.com/OpenGalaxea/GalaxeaVLA","weights":"https://huggingface.co/OpenGalaxea/G05"},"policy":{"family":"VLA","baseModel":"Qwen3.5-2B autoregressive VLM; residual-vector-quantized ActionCodec and temporal visual memory","modelSize":"2B VLM backbone; full tokenizer/memory-inclusive count not reported","inputs":"Multi-view RGB history, instruction, embodiment identifier and proprioception","outputs":"Reasoning/subtask/box/trace tokens and compact part-specific action codes; ActionCodec decodes shared27D robot commands","trainingData":"Large multi-robot corpus plus VQA; aggregate not disclosed; report includes R1Lite/Pro, DROID and household benchmark adaptation","trainingTime":"Real task adaptation4–10 h per setting under equal wall-clock budgets; full pretraining time not reported","trainingHardware":"Real-task adaptation16 × NVIDIA H20; full pretraining hardware not reported","controlRate":"FlashRT author comparison: 130 ms without CoT, 192 ms with CoT; hardware context must be retained from report","trainingMethod":"Unified next-token action/reasoning learning, active-part tokenization and history injection; optional RL-compatible likelihood","evaluation":"Real R1Lite/Pro, DROID held-out transfer, BEHAVIOR50 tasks, LIBERO, RoboTwin2, SimplerEnv"},"release":{"code":"Available","weights":"Gated — automatic Hugging Face access terms","data":"Not verified","license":"Not verified","notes":"Public project and model release; undisclosed training details remain explicitly unknown. A manuscript is not required to identify the released policy. The current official artifact inventory is gated (automatic access terms); files are listed but anonymous unrestricted download is not claimed."},"evidence":[{"url":"https://opengalaxea.github.io/G05/","label":"Official model project / technical release","fields":["title","date","policy"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://github.com/OpenGalaxea/GalaxeaVLA","label":"Official implementation / author-contributed adapter","fields":["policy.inputs","policy.outputs"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://github.com/OpenGalaxea/GalaxeaVLA","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/OpenGalaxea/G05","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["Distinct from G0/G0Plus flow-based architecture; first public announcementJune1 is used because no arXiv history is supplied."],"verified":"2026-10-19"},{"id":"seetraceact","name":"SeeTraceAct","title":"SeeTraceAct: Visibility-Aware Latent Planning from Cross-Embodiment Demonstration Videos","date":"2026-06-01","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A video-conditioned VLA learns visibility-aware future end-effector traces to ground precise manipulation from cross-embodiment demonstrations.","institution":"Georgia Institute of Technology / Allen Institute for AI","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2606.02745","project":"https://github.com/jaehyeon-son/SeeTraceAct","code":"https://github.com/jaehyeon-son/SeeTraceAct"},"policy":{"family":"VLA","baseModel":"GR00T N1.5 action model with V-JEPA 2 demonstration encoder, Perceiver compression and visual latent-plan query tokens.","modelSize":"Not reported","inputs":"Current RGB camera views, robot state, task instruction and one demonstration video.","outputs":"Continuous robot action chunk; future 2D end-effector trace/visibility decoder provides training supervision.","trainingData":"RoboCasa-DC pairs RoboCasa arm trajectories with humanoid demonstration videos; up to approximately 3,000 robot trajectories per task, with 50 demonstration seeds per task.","trainingTime":"50,000 steps at batch size 128; wall-clock duration not reported.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Flow-matching robot actions with visibility-aware latent-trace supervision.","evaluation":"Four RoboCasa-DC evaluation settings and real Franka tasks conditioned on human demonstrations."},"release":{"code":"Partial","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Official repository is identified by the paper; code/data availability is verified separately from the paper’s release statement."},"evidence":[{"url":"https://arxiv.org/abs/2606.02745","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://github.com/jaehyeon-son/SeeTraceAct","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/jaehyeon-son/SeeTraceAct","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."}],"caveats":["End-effector traces are auxiliary training targets, not measured depth or future observations supplied at deployment."],"verified":"2026-10-17"},{"id":"wall-wm","name":"WALL-WM","title":"WALL-WM: Carving World Action Modeling at the Event Joints","date":"2026-06-01","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"An event-grounded world-action model with coupled video and action diffusion towers. It supports variable-length semantic events and fixed-length chunks conditioned by latent reasoning.","institution":"X Square Robot","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2606.01955","code":"https://github.com/X-Square-Robot/wall-wm"},"policy":{"family":"World-action model","baseModel":"Wan-series video DiT and randomly initialized action DiT; frozen Qwen3.5-9B for language reasoning, T5 text conditioning. The released code uses Wan2.2-TI2V-5B.","modelSize":"The paper describes a family ranging from below 10B to tens of billions of parameters; the exact size of the main evaluated complete system is Not reported. Qwen3.5-9B is the reasoning backbone, not the entire policy.","inputs":"Current multi-view RGB observations, proprioceptive state, and a next-event description. Unified mode uses a global instruction and configurable observation history.","outputs":"Future multi-view video and end-effector trajectories. Event mode predicts a variable-length segment; unified mode predicts a fixed action chunk. The released event interface sets action horizon from video-frame count and skip interval.","trainingData":"Web video including a 1.2M-clip OpenVID slice; HD-VILA, Ego4D, EPIC-KITCHENS, DROID, AgiBot World, and internal teleoperation, UMI-style robot-free collection, and recovery data. Total robot hours and trajectory counts are Not reported.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported. The paper discusses distillation, FP8, and streaming as latency reductions without a complete main-policy hardware/latency measurement.","trainingMethod":"Event-caption video flow matching, followed by action flow matching with the video tower frozen. Later stages train VLM projection heads and a staircase latent-reasoning branch; optional joint adaptation supports fixed-length deployment. Cluster-balanced event sampling and Muon optimization support scaling.","evaluation":"Eighteen real tabletop bimanual tasks across direct manipulation (7), reasoning (5), contact-sensitive manipulation (2), and cluttered generalization (4), scored with task-specific normalized Task Progress. Additional RoboTwin results and video/3D representation evaluations are reported."},"release":{"code":"Available","weights":"Not released","data":"Partial","license":"Apache-2.0 code, verified from LICENSE. Model and source-data terms are separate.","notes":"The author repository contains training, RoboTwin evaluation, and open-loop event examples. Its README explicitly marks pretrained WALL-WM checkpoints as coming soon. A sample event episode is provided; the full mixed training corpus is not released there."},"evidence":[{"url":"https://arxiv.org/abs/2606.01955","label":"Paper and submission history","fields":["date","title","policy"],"note":"First submission 2026-06-01. Metadata uses the current v2 technical report; Sections 3–7 distinguish event and unified modes, data composition, and evaluation."},{"url":"https://github.com/X-Square-Robot/wall-wm","label":"Author repository","fields":["links","release","policy.baseModel","policy.outputs"],"note":"README model-download section marks checkpoints as coming soon and documents the Wan2.2 base and event-horizon interface."},{"url":"https://github.com/X-Square-Robot/wall-wm/blob/main/LICENSE","label":"Code license","fields":["release.license"]}],"caveats":["The reported real-robot metric is Task Progress with task-specific rubrics, rather than binary success rate.","Optional tactile-force signals are discussed conceptually; online tactile or measured-depth inputs are not established for the main evaluated policy.","The event/view ablation changes both cross-view modeling and execution format, so it does not isolate either mechanism alone.","The repository is distinct from the earlier WALL-OSS policy release; shared organizational links do not make the papers identical."],"verified":"2026-10-17"},{"id":"tau0-wm","name":"τ₀-WM","title":"τ₀-WM: A Unified Video-Action World Model for Robotic Manipulation","date":"2026-05-31","year":2026,"collections":["policy"],"categories":["generalist-foundation","agent-rsi-icl"],"resourceType":"policy","summary":"Couples a video-action policy with an action-conditioned visual simulator and progress evaluator for candidate ranking and rectification.","institution":"Shanghai Innovation Institute / AGIBOT Finch","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2606.01027","project":"https://tau0-wm.github.io/","code":"https://github.com/sii-research/tau-0-wm","weights":"https://huggingface.co/sii-research/tau-0-wm"},"policy":{"family":"World-action model","baseModel":"Wan2.2-TI2V-5B and 0.5B ActionDiT; policy VAM and action-conditioned simulator interfaces.","modelSize":"5.5B VAM: 5B video model plus 0.5B action decoder; simulator configuration separately scoped.","inputs":"Multi-view RGB, language and robot state; candidate actions condition the simulator.","outputs":"Continuous actions and future latents; simulator adds multi-view future predictions and dense task-progress scores.","trainingData":"Approximately 27.3k hours: 17.8k robot teleoperation, 6.5k UMI-style and 3k human egocentric hours. Loss masks reflect action/progress-label availability.","trainingTime":"Pretraining and posttraining stages; full wall-clock duration not reported.","trainingHardware":"Training hardware not reported; physical inference on one RTX 5090.","controlRate":"Approximately 220 ms per query, or 180 ms with cached text. Candidate search and simulator rectification add computation.","trainingMethod":"Joint video-action flow matching, modality-specific loss masks, consistency ranking and simulator-based action rectification.","evaluation":"Physical AGIBOT-G01, ARX and dual-Franka packing/assembly tasks requiring fine-grained and long-horizon manipulation."},"release":{"code":"Available","weights":"Available","data":"Partial","license":"Not verified; pretrained backbones and data retain separate terms","notes":"Availability concerns author-linked artifacts; no training or installation reproduced."},"evidence":[{"url":"https://arxiv.org/abs/2606.01027","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/sii-research/tau-0-wm","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://tau0-wm.github.io/","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."}],"caveats":["Candidate planning and rectification add cost beyond base action generation.","First arXiv submission is 31 May 2026 despite the 2606 identifier prefix."],"verified":"2026-10-17"},{"id":"wall-oss05","name":"Wall-OSS-0.5","title":"Wall-OSS-0.5 Technical Report","date":"2026-05-29","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"A deployable VLA bridges discrete action supervision and continuous flow actions through a shared vision-language/action mixture of transformers.","institution":"X Square Robot","venue":"Technical report","modalities":["RGB","Proprioception","Language"],"environments":["Real world","Simulation"],"embodiments":["Parallel-jaw gripper","Bimanual","Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2605.30877","project":"https://x2robot.com/en/oss","code":"https://github.com/X-Square-Robot/wall-x","weights":"https://huggingface.co/x-square-robot/wall-oss-0.5"},"policy":{"family":"VLA","baseModel":"Qwen2.5-VL-3B-Instruct VL expert plus an action expert in a Mixture-of-Transformers backbone, with vision-aligned RVQ action-token supervision.","modelSize":"Over 4B total reported; original 3B VLM retained as the VL expert. Action expert/projection count is not a separately verified exact total.","inputs":"Three-view RGB, language instruction and proprioception. Discrete action tokens primarily supervise training; deployment defaults to the continuous flow pathway.","outputs":"Continuous embodiment-specific action chunks covering approximately one second of motion; number of action frames depends on the source robot control frequency.","trainingData":"Self-collected multi-platform robot data plus public RoboMIND, AgiBot, RoboCOIN, RoboChallenge, Galaxea, DROID, Bridge and Fractal subsets. Multimodal corpus approximately 90M samples, including 12M robot-grounded bridge samples; these are not 90M robot trajectories.","trainingTime":"Wall-clock duration not reported. Paper evaluates pretraining checkpoints through 400K steps; real downstream fine-tuning uses a uniform 6 epochs.","trainingHardware":"Training accelerator model/count not reported. Inference profiling uses one RTX 5090 and is a separate scope.","controlRate":"Optimized model inference approximately 21 Hz at 224px and 15 Hz at 448px with three RGB views and 10 denoising steps on one RTX 5090; not a low-level servo frequency.","trainingMethod":"Joint multimodal CE, discrete action-token CE and continuous flow matching; RVQ action supervision bridges backbone gradients. Distributed Muon/AdamW training and optimized CUDA-graph inference.","evaluation":"17-task real-robot zero-shot suite and 15-task downstream real fine-tuning evaluation; task-progress metric is distinct from binary success."},"release":{"code":"Available","weights":"Available","data":"Partial","license":"Apache-2.0 original code; released checkpoint and constituent corpus terms must be checked separately.","notes":"Wall-X repository provides Wall-OSS-0.5 fine-tuning/inference/LIBERO tooling and checkpoint links. Proprietary robot data is not established as fully released."},"evidence":[{"url":"https://arxiv.org/abs/2605.30877","label":"Paper and first-public arXiv record","fields":["title","date","institution","policy","modalities","embodiments","environments"],"note":"Technical fields follow the inspected full manuscript and appendices; date is arXiv v1, not the current revision."},{"url":"https://x2robot.com/en/oss","label":"Official project","fields":["summary","imageCandidates","release"],"note":"Current author project and release links."},{"url":"https://github.com/X-Square-Robot/wall-x","label":"Official repository","fields":["release","policy.baseModel","policy.inputs","policy.evaluation","imageCandidates"],"note":"Current implementation/release scope can differ from paper experiments."},{"url":"https://github.com/X-Square-Robot/wall-x/blob/main/LICENSE","label":"Apache-2.0 code license","fields":["release.license"]}],"caveats":["Do not conflate Wall-OSS-0.5 with the original Wall-OSS or separate WALL-WM paper.","Pretrained zero-shot capability, downstream fine-tuning and simulation settings have separate data/protocol scopes.","90M multimodal examples include robot-grounded VL data and are not independent manipulation episodes."],"verified":"2026-10-17"},{"id":"qwen-vla","name":"Qwen-VLA","title":"Qwen-VLA: Unifying Vision-Language-Action Modeling across Tasks, Environments, and Robot Embodiments","date":"2026-05-28","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"A Qwen-based embodied model shares vision-language representations and a continuous-action decoder across manipulation, navigation and trajectory prediction.","institution":"Qwen / Alibaba","venue":"Technical report","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2605.30280","project":"https://qwen.ai/blog?id=qwenvla","repository":"https://github.com/QwenLM/Qwen-VLA"},"policy":{"family":"VLA","baseModel":"Qwen3.5-4B vision-language backbone with a 16-block single-stream flow-matching DiT action expert and embodiment-aware text prompts.","modelSize":"4B vision-language backbone; approximately 1.15B in the action expert. Complete instantiated system total is not separately reported.","inputs":"RGB images/history, language task and embodiment/control-convention prompt, and embodiment-specific proprioceptive state for manipulation. Depth and tactile are not reported deployed inputs.","outputs":"Masked continuous action/trajectory chunks in a shared tensor interface, with native embodiment control semantics retained; textual responses for vision-language tasks. Horizon and active action dimensions depend on the dataset.","trainingData":"Joint mixture of open robot corpora exceeding 10000 hours, over 1000 hours of proprietary robot trajectories, egocentric demonstrations, synthetic trajectories, navigation and auxiliary VL data. These reported source scales overlap and are not summed.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Embodiment prompt retains dataset-native control FPS and chunk size; no universal model-inference or robot-servo rate reported.","trainingMethod":"Stage-I text-to-action decoder pretraining, then visual/action joint pretraining; multi-task supervised adaptation and simulation RL produce Qwen-VLA-Instruct.","evaluation":"Manipulation on LIBERO, Simpler-WidowX, RoboTwin Easy/Hard, real ALOHA OOD and DOMINO dynamic tasks; R2R/RxR navigation evaluated separately."},"release":{"code":"Not released","weights":"Not verified","data":"Partial","license":"Executable policy/model license not verified; public source datasets retain their own terms.","notes":"Official GitHub repository provides report and demonstrations, not a released executable training/inference implementation. Public component datasets do not establish release of the complete curated mixture."},"evidence":[{"url":"https://arxiv.org/abs/2605.30280","label":"Paper and first-public arXiv record","fields":["title","date","institution","policy","modalities","embodiments","environments"],"note":"Technical fields follow the inspected full manuscript and appendices; date is arXiv v1, not the current revision."},{"url":"https://qwen.ai/blog?id=qwenvla","label":"Official project","fields":["summary","imageCandidates","release"],"note":"Current author project and release links."},{"url":"https://github.com/QwenLM/Qwen-VLA","label":"Official informational repository","fields":["release","policy.baseModel"],"note":"Repository description and contents expose report/demo resources; not an executable policy-code release."}],"caveats":["Navigation and manipulation share a model formulation but have distinct action semantics and evaluation protocols.","1.15B is the action expert count, not the whole policy; no tactile or measured-depth stream is implied by spatial tasks."],"verified":"2026-10-17"},{"id":"tabero","name":"Tabero / Tabero-VTLA","title":"Tabero: Learning Gentle Manipulation with Closed-Loop Force Feedback from Vision, Touch, and Language","date":"2026-05-27","year":2026,"collections":["benchmark","policy"],"categories":["tactile-benchmarks","robustness-evaluation","tactile-foundation"],"resourceType":"policy","summary":"Pairs a language-conditioned gentleness benchmark with a VLA that predicts separate position and contact-force targets.","institution":"HKUST(GZ)","venue":"ICML 2026","modalities":["RGB","Tactile","Force/Torque","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2605.27886","project":"https://nathanwu7.github.io/Tabero/","code":"https://github.com/NathanWu7/Tabero","data":"https://huggingface.co/datasets/NathanWu7/Isaaclab_Libero","weights":"https://huggingface.co/NathanWu7/pi0_lora_tacfield_tabero"},"policy":{"family":"VLA","baseModel":"π0 JAX plus tactile tokenizer and LoRA","modelSize":"Not reported","inputs":"Two RGB views, language, state and tactile marker fields; force/image variants evaluated separately","outputs":"13 semantic action dimensions padded to 32: position/gripper and force setpoints for fixed hybrid controller","trainingData":"LIBERO trajectory migration into Isaac Lab; main policy study uses nine object tasks and paired firm/gentle variants","trainingTime":"50k optimization steps, batch 32; wall-clock not reported","trainingHardware":"Not reported","controlRate":"Synchronized observations/actions20 Hz; policy-query rate not separately reported","trainingMethod":"Language/force-augmented behavior cloning; LoRA and auxiliary force supervision; admittance low-level controller","evaluation":"Nine-object firm/gentle simulation subset plus language-adverb generalization"},"release":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 repository; upstream simulator/model/data terms separate","notes":"Environment/data/inference clients and companion Tabero-VTLA training repository; LoRA weights linked."},"evidence":[{"url":"https://arxiv.org/abs/2605.27886","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/NathanWu7/Tabero","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://github.com/NathanWu7/Tabero","label":"Official release reproduction notes","fields":["benchmark.simulatorVersion","release"],"note":"Explicit paper-versus-minicase rerun settings and contact binding caveat."},{"url":"https://arxiv.org/abs/2605.27886","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Published policy evaluation is simulation; project physical cube/cup examples are paired demonstration recordings, not a real-policy benchmark.","Main policy consumes RGB and tactile representations; exported RGBD does not establish depth policy input.","Paper uses Sim5.0/Lab2.2 gelpad contact binding; Lab2.3/Sim5.1 housing workaround changes force measurements/performance."],"verified":"2026-10-17","benchmark":{"simulator":"Isaac Sim / Isaac Lab with TacEx,Taxim and FOTS-derived tactile rendering","simulatorVersion":"Paper:Isaac Sim5.0 / Isaac Lab2.2,force sensor on gelpad;repo Lab2.3/Sim5.1 uses minicase workaround.","taskCount":"Nine Object-suite tasks in main model evaluation;broader data-migration checks span four LIBERO suites.","tasks":["pick up the alphabet soup and place it in the basket","pick up the cream cheese and place it in the basket","pick up the salad dressing and place it in the basket","pick up the bbq sauce and place it in the basket","pick up the tomato sauce and place it in the basket","pick up the butter and place it in the basket","pick up the milk and place it in the basket","pick up the chocolate pudding and place it in the basket","pick up the orange juice and place it in the basket"],"taskListScope":"Complete nine-task main experimental subset (AppendixC/Table11);not all migrated LIBERO configurations.","capabilities":["Language-conditioned force regulation","Gentle grasping","Force/pose coordination"],"metrics":"Success rate;average grip/applied force;maximum transient grip/applied force (mean of top5% samples).","robots":"Franka Panda with tactile-integrated parallel gripper","sensors":"Two RGB-D cameras;dual GelSight-style320x240 tactile RGB and11x9 marker fields;fingertip3D contact force;state/language/actions20 Hz.","dataCollection":"Replay and re-collect source LIBERO trajectories across simulators, with controlled gripper-force scaling and success filtering; tactile observations are regenerated.","data":"Replay source trajectories under100%,25%,10% gripper-controller scaling;keep successful replays;dataset totals source/version dependent.","protocol":"Compare firm/gentle prompts on matched object tasks;DatasetsA/B continuous force25%/10%,DatasetC binary gripper10%;controller ablations isolate tracking.","baselines":"Vision-only π0;image,marker-field andforce tactile injection variants;force-supervision and fixed-controller ablations."},"releaseByCollection":{"benchmark":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 repository; upstream simulator/model/data terms separate","notes":"Environment/data/inference clients and companion Tabero-VTLA training repository; LoRA weights linked."},"policy":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 repository; upstream simulator/model/data terms separate","notes":"Environment/data/inference clients and companion Tabero-VTLA training repository; LoRA weights linked."}},"relatedIds":[],"benchmarkSummary":"Nine simulated force-sensitive manipulation tasks evaluate task completion and interaction quality with tactile observations and force-position control.","policySummary":"Pairs a language-conditioned gentleness benchmark with a VLA that predicts separate position and contact-force targets."},{"id":"colosseum-v2","name":"Colosseum V2","title":"Colosseum V2: Benchmarking Generalization for Vision-Language-Action Models","date":"2026-05-26","year":2026,"collections":["benchmark"],"categories":["robustness-evaluation","general-manipulation"],"resourceType":"benchmark","summary":"GPU-parallel visual/language/action robustness evaluation across 28 single-arm and bimanual tasks, with selected physical validation.","institution":"University of Southern California","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2605.27759","project":"https://colosseum-v2.github.io/","code":"https://github.com/jstmn/ColosseumV2"},"benchmark":{"simulator":"ManiSkill / SAPIEN","simulatorVersion":"Exact ManiSkill/SAPIEN build Not verified; record release dependency snapshot.","taskCount":"28 tasks: 16 single-arm + 12 bimanual, spanning 13 categories","tasks":["RaiseCube","PickSodaFromCabinet","PickDishFromRack","StackCube","PlaceBookInShelf","PlaceDishInRack","LiftPegUpright","RotateArrow","PegInsertionSide","PlugCharger","HammerNail","ScoopBanana","OpenDrawer","OpenCabinet","PlaceCubeInDrawer","CookItemInPan","DualArmCubeHandover","DualArmBottleHandover","DualArmLiftPot","DualArmLiftTray","DualArmPushBox","DualArmPourPot","DualArmThreading","DualArmPenCap","DualArmDrawerPlace","DualArmDrawerOpen","DualArmStackCube","DualArmStackTwoCubes"],"taskListScope":"Complete 28-task official project catalogue.","capabilities":["Visual/language/action generalization","Tool use","Insertion","Bimanual coordination","Articulated interaction"],"metrics":"In-domain/OOD task success; relative success degradation; physical-vs-sim correlation.","robots":"Single Franka Panda and dual Franka Panda parallel-gripper configurations.","sensors":"Visual observations with optional framework depth/point-cloud channels; proprioception and language; baseline actual channel setting in implementation.","dataCollection":"Simulation: expert motion-planning solvers with privileged state. The physical study reports demonstrations, but its collection interface is Not reported.","data":"100 clean demonstrations/task for simulated baselines; physical ACT study uses 60 demonstrations/task on three selected tasks.","protocol":"Train separately for single/bimanual suites; 200 evaluation episodes per task/perturbation. Physical validation: RaiseCube, RotateArrow, LiftPegUpright and selected shifts.","baselines":"ACT and π0.5."},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"Apache-2.0 for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Official code, task/perturbation catalogue and appendix public."},"evidence":[{"url":"https://arxiv.org/abs/2605.27759","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://colosseum-v2.github.io/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/jstmn/ColosseumV2","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://arxiv.org/abs/2605.27759","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."},{"url":"https://github.com/jstmn/ColosseumV2/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["Paper states 19 perturbation factors; current homepage displays 16 labels/counts including aggregate conditions. Keep source version with any factor count.","Physical validation covers three single-arm tasks, not all 28."],"verified":"2026-10-19","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not verified","data":"Available","license":"Apache-2.0 for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Official code, task/perturbation catalogue and appendix public."}},"relatedIds":[]},{"id":"taco-sensor-benchmark","name":"TacO","title":"TacO: Benchmarking Tactile Sensors for Object Manipulation","date":"2026-05-21","year":2026,"collections":["benchmark"],"categories":["tactile-benchmarks"],"resourceType":"benchmark","summary":"Real-robot task-driven comparison of optical, acoustic, magnetic and resistive tactile hardware under a shared imitation-learning setup.","institution":"University of California San Diego / Carnegie Mellon University / Seoul National University","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2605.21976","project":"https://anyazorin.com/projects/taco","code":"https://github.com/TacObench/TacO"},"benchmark":{"simulator":"Not applicable — physical robot sensor benchmark","simulatorVersion":"Not applicable","taskCount":"3 physical manipulation tasks","tasks":["Pick-and-place with unknown mass","Object reorientation","Plug insertion"],"taskListScope":"Complete three-task paper evaluation; sensor/task combinations vary.","capabilities":["Mass-adaptive grasping","In-hand reorientation","Insertion","Sensor selection"],"metrics":"Policy task success; analysis of spatial resolution, shear, sensor representation, friction and sensor durability.","robots":"Franka Panda 7-DoF arms; experiments distributed across institutions.","sensors":"Four tactile modalities: optical, acoustic, magnetic, resistive. Named devices include FlexiTac, eGain, Daimon, eFlesh and FSR configurations; consult paper sensor matrix for tested combinations.","dataCollection":"GELLO joint-space teleoperation; sensor configurations share the demonstration and imitation-learning pipeline.","data":"GELLO teleoperation; paper specifies task-dependent demonstration budgets (e.g., 60 demonstrations for pick-and-place, 50 per sensor for plug insertion).","protocol":"Train separate ACT policies per sensor, task and modality configuration; compare outcomes under matched task setup with sensor material properties documented.","baselines":"ACT with sensor-specific tactile representations and vision-only comparisons."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Official ACT training/deployment code public; author project page still describes forthcoming sensor, hardware and data release."},"evidence":[{"url":"https://arxiv.org/abs/2605.21976","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://anyazorin.com/projects/taco","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/TacObench/TacO","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://arxiv.org/abs/2605.21976","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Measures complete sensor-plus-material systems; differences cannot always be attributed to transduction alone.","TacO is unrelated to TACO (CVPR 2024 human tool-action-object dataset)."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Official ACT training/deployment code public; author project page still describes forthcoming sensor, hardware and data release."}},"relatedIds":[]},{"id":"demo-jepa","name":"Demo-JEPA","title":"Demo-JEPA: Joint-Embedding Predictive Architecture for One-shot Cross-Embodiment Imitation","date":"2026-05-20","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A demonstration-to-goal predictor translates source video into target-compatible latent goals for model-based control.","institution":"Peking University / Beijing Innovation Center of Humanoid Robotics / University of Washington","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2605.20811","project":"https://log2r.github.io/Demo-JEPA/"},"policy":{"family":"Model-based planning","baseModel":"Cross-embodiment latent-goal imitation and CEM planning. V-JEPA 2.1 action-conditioned dynamics and embodiment-aware Dreamer Predictor.","modelSize":"Not reported","inputs":"Source visual frame pair, current target RGB observation, robot state and target dynamics model; physical policies use one fixed third-person RGB view.","outputs":"Target action sequence optimized by CEM in latent space, executed with goal updates.","trainingData":"Simulation: goal predictor 86 tasks/13,444 trajectories and action co-training 39 tasks/8,324. Physical: 22 tasks/4,508 and 19 tasks/3,903 respectively.","trainingTime":"Stage 0 world model: 7 days; Stage 1 goal predictor: 2.5 days; Stage 2 co-training: 1 day.","trainingHardware":"8 NVIDIA A100 GPUs for each reported stage.","controlRate":"Video sampling is 5 fps; complete deployed robot control/query rate not reported.","trainingMethod":"Target dynamics pretraining, cross-embodiment latent goal prediction and action co-training; no target-future trajectory oracle at deployment.","evaluation":"RLBench seen-task, unseen-action and unseen-configuration suites plus six physical tasks, UR source and Franka target."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No author-linked implementation was verified in the inspected primary manuscript/project. This is an evidence gap, not proof that no implementation exists."},"evidence":[{"url":"https://arxiv.org/abs/2605.20811","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://log2r.github.io/Demo-JEPA/","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."}],"caveats":["The oracle baseline receives target future states that the proposed deployment does not receive.","RGB-D-capable camera hardware is used, but the stated policy observation is third-person visual RGB."],"verified":"2026-10-17"},{"id":"gem-4d","name":"GEM-4D","title":"GEM-4D: Geometry-Enhanced Video World Models for Robot Manipulation","date":"2026-05-20","year":2026,"collections":["policy"],"categories":["rgbd-3d","geometry-models"],"resourceType":"policy","summary":"Geometry-supervised video generation is paired with a geometric inverse-dynamics system to recover executable manipulation trajectories.","institution":"Harvard University; MIT; Princeton University; MIT-IBM Watson AI Lab","venue":"arXiv preprint","modalities":["RGB","Language","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2605.22882","project":"https://gem-4d.github.io/","code":"https://github.com/kaichen-z/GEM-4d","weights":"https://huggingface.co/datasets/zhouk777/gem4d-weight"},"release":{"code":"Partial — geometry-enhanced video world model","weights":"Partial — CogVideoX world-model checkpoints","data":"Not verified","license":"No root project LICENSE found in the inspected code tree; bundled DA3/CogVideoX and other pretrained components retain their own terms. A blanket project/weight license was not independently verified.","notes":"Actual Geometry DiT, frozen-teacher, training and inference source is released. The official HF weight inventory contains two CogVideoX checkpoint exports with safetensors shards. The adaptive inverse-dynamics action module and VGGT/depth-target ablations are explicitly absent; the added Wan2.2-TI2V-5B backbone has no released GEM-4D weights."},"evidence":[{"url":"https://arxiv.org/abs/2605.22882","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://gem-4d.github.io/","label":"Official author project","fields":["institution","imageCandidates"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://github.com/kaichen-z/GEM-4d","label":"Official implementation and release scope","fields":["links"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://github.com/kaichen-z/GEM-4d/tree/main","label":"Actual repository content inventory","fields":["links"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://github.com/kaichen-z/GEM-4d","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/datasets/zhouk777/gem4d-weight","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["The geometry branch is training-only; the action-extraction pipeline has separate geometric assumptions.","Generated-video success judgements and actual robot-execution success are separate measurements."],"verified":"2026-10-19","policy":{"family":"World-action model","baseModel":"Video DiT plus training-only geometry flow branch; adaptive inverse-dynamics tracking, pose fallback and action synthesis.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Initial RGB observation, language and geometry/calibration used by the inverse-dynamics stage.","outputs":"Future video and reconstructed 6-DoF gripper trajectory.","trainingMethod":"Flow-matching video generation regularized by frozen geometry features; geometric rollout-to-action conversion.","trainingData":"Robot manipulation video data with dense geometry teacher features.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Video/geometry evaluation, RLBench execution and real manipulation; distinguish human-rated generated DROID videos from physical trials.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"dexora","name":"Dexora","title":"Dexora: Open-source VLA for High-DoF Bimanual Dexterity","date":"2026-05-18","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Trains a bimanual dexterous diffusion policy with simulation/real demonstrations and discriminator-weighted data quality.","institution":"Tsinghua University / BAAI","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Bimanual","Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2605.18722","project":"https://dexoravla.github.io","code":"https://github.com/dexoravla/Dexora","data":"https://huggingface.co/datasets/Dexora/Dexora_Real-World_Dataset","weights":"https://huggingface.co/Dexora"},"policy":{"family":"VLA","baseModel":"Diffusion transformer with T5 language and SigLIP image encoders","modelSize":"Released Dexora-1B action model; full encoder-inclusive aggregate not reported","inputs":"Four RGB views, 36-DoF proprioceptive state and language","outputs":"36-D joint-space actions; action chunk 32 in reported comparison","trainingData":"100k embodiment-matched simulated trajectories / 6.5M frames plus 10k real episodes / 2.92M frames","trainingTime":"Downstream comparisons: 50k steps per task with 100 demonstrations; full pretraining wall-clock not reported","trainingHardware":"Comparison fine-tuning: 4 × L20 with LoRA; inference: one RTX4090","controlRate":"Recorded control 20 Hz; policy replanning latency not reported","trainingMethod":"Simulation pretraining, kinematic/replay data validation, learned discriminator and quality-weighted diffusion post-training","evaluation":"Real dual-arm dual-hand basic/dexterous tasks, OOD settings and cross-embodiment projection"},"release":{"code":"Available","weights":"Available","data":"Available","license":"MIT","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2605.18722","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/dexoravla/Dexora","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."},{"url":"https://raw.githubusercontent.com/dexoravla/Dexora/main/LICENSE","label":"Repository license file","fields":["release.license"],"note":"Code scope; model, data and third-party component terms can differ."}],"caveats":[],"verified":"2026-10-17"},{"id":"tactile-fusion-survey","name":"Tactile Multimodal Fusion — Survey","title":"Tactile-based Multimodal Fusion in Embodied Intelligence: A Survey of Vision, Language, and Contact-Driven Paradigms","date":"2026-05-17","year":2026,"collections":["policy"],"categories":["tactile-foundation","tactile-representation"],"resourceType":"survey","summary":"Separates tactile multimodal datasets from perception, cross-modal generation and interactive manipulation methods.","institution":"Xi’an Jiaotong University; HKUST(GZ); Nanjing University; Purple Mountain Laboratory; Wuhan University; James Cook University; BUPT; Fudan University; Linkerbot; Swinburne University of Technology","venue":"Preprint · Survey","modalities":[],"environments":["Literature review"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2605.17336"},"survey":{"scope":"Tactile fusion with at least one additional modality in embodied learning, covering datasets, sensor representations, methods and evaluation through Q1 2026.","taxonomy":["Data: tactile–vision, tactile–language, tactile–vision–language, tactile–vision–other","Methods: multimodal perception and recognition","Methods: cross-modal generation and transformation","Methods: multimodal interaction and manipulation"],"adaptationRegimes":["Supervised and transfer learning","Contrastive shared-space alignment","Curriculum learning","Reinforcement learning and hybrid training"],"contextSources":["Tactile sensing","Scene vision","Language descriptions/instructions","Additional physical or interaction modalities"],"evaluationAxes":["Recognition and retrieval accuracy","Generation and alignment metrics","Interaction/task success","Generalization across objects, scenes, sensors and modalities","Dataset annotation and sensing coverage"],"corpus":"20-page paper with eight figures; stated literature horizon through the first quarter of 2026. The rendered bibliography is separately available in the citation audit.","reviewMethod":"Collects studies from robotics, computer vision, haptics and multimodal venues, selecting learning-based methods combining touch with another modality; Section II-B6 describes selection scope.","coverageLimits":"A broad narrative overview with limited detail on reproducible search/exclusion decisions. Generated tactile images, property QA and physical closed-loop control have different validation requirements."},"release":{"code":"Not applicable — survey; no new robot implementation","weights":"Not applicable — survey","data":"Not verified — no separate coded corpus linked","license":"Paper and third-party figures retain their stated original rights","notes":"Literature inventories and review tables are not robot demonstration data or deployable model weights."},"evidence":[{"url":"https://arxiv.org/abs/2605.17336","label":"Original survey and first submission","fields":["title","date","summary","institution","survey"],"note":"Manuscript structure, tables and bibliography inspected; underlying method specifications require their original paper."},{"url":"https://arxiv.org/html/2605.17336","label":"Primary full-text survey and references","fields":["survey"],"note":"Citation extraction preserves original bibliography identities, including background and adjacent resources."}],"caveats":["Survey, not an executable policy or model; deployment modalities, training costs and control rates do not apply."],"relatedIds":["sparsh","unitouch","t3","tvl","anytouch","anytouch2","sparsh-x","sparsh-skin"],"verified":"2026-10-17","surveyAuditId":"tactile-fusion-survey-references"},{"id":"dexjoco","name":"DexJoCo","title":"DexJoCo: A Benchmark and Toolkit for Task-Oriented Dexterous Manipulation on MuJoCo","date":"2026-05-15","year":2026,"collections":["benchmark"],"categories":["dexterous-benchmarks"],"resourceType":"benchmark","summary":"Task-oriented arm-hand benchmark emphasizing tool use, bimanual coordination, long horizons and reasoning, with demonstration collection and modern policy adapters.","institution":"CAS Institute of Automation","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Simulation"],"embodiments":["Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2605.16257","project":"https://dexjoco.github.io/","code":"https://github.com/brave-eai/dexjoco","data":"https://huggingface.co/datasets/DexJoCo/DexJoCo-Datasets-LeRobot"},"benchmark":{"simulator":"MuJoCo","simulatorVersion":"Current official environment-dexjoco.yaml and package pyproject both pin MuJoCo 3.4.0.","taskCount":"11 tasks; 100 demonstrations/task","tasks":["Water Plant","Hammer Nail","Click Mouse","Pick Bucket","Pinch Tongs","Fold Glasses","Bimanual Microwave","Bimanual Unlock iPad","Bimanual Hanoi","Bimanual Assembly","Bimanual Photograph"],"taskListScope":"Complete 11-task published gallery.","capabilities":["Functional tool use","Dexterous grasping","Bimanual coordination","Long-horizon execution","Reasoning"],"metrics":"Task success across object-only and full visual/dynamics randomization; multitask and action-head adaptation results.","robots":"Franka Panda + Allegro Hand; single-arm and dual-arm configurations.","sensors":"Third-person and wrist RGB, proprioception and actions. Depth/tactile inputs are not established as evaluated baseline modalities.","dataCollection":"Motion-capture teleoperation with Rokoko gloves and HTC Vive trackers; hand retargeting and randomized-scene replay expand the demonstrations.","data":"1,100 human demonstrations captured with Rokoko gloves / HTC Vive trackers; replay-based randomized scene expansion.","protocol":"Shared asynchronous policy server/client interface; single-task vs multitask training; object placement/table height and visual/dynamic randomization.","baselines":"ACT, Diffusion Policy CNN/Transformer, π0.5, GR00T N1.5."},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"MIT main code; GeoRT non-commercial, OpenPI Apache-2.0 and Gemma terms apply to bundled components","notes":"Source, teleoperation and LeRobot datasets public; third-party components have distinct licenses."},"evidence":[{"url":"https://arxiv.org/abs/2605.16257","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://dexjoco.github.io/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/brave-eai/dexjoco","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://github.com/brave-eai/dexjoco/blob/main/dexjoco/pyproject.toml","label":"Official source","fields":["benchmark.simulatorVersion"],"note":"MuJoCo==3.4.0 pin read from actual package dependency."},{"url":"https://arxiv.org/abs/2605.16257","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Dexterous embodiment does not imply tactile observations.","DexJoCo-X is a separate matched cross-hand protocol with six tasks and seven hands."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not verified","data":"Available","license":"MIT main code; GeoRT non-commercial, OpenPI Apache-2.0 and Gemma terms apply to bundled components","notes":"Source, teleoperation and LeRobot datasets public; third-party components have distinct licenses."}},"relatedIds":[]},{"id":"evo-depth","name":"Evo-Depth","title":"Evo-Depth: A Lightweight Depth-Enhanced Vision-Language-Action Model","date":"2026-05-14","year":2026,"collections":["policy"],"categories":["rgbd-3d","generalist-foundation"],"resourceType":"policy","summary":"A compact VLA uses implicit depth features and spatial modulation to ground RGB-based flow-matching actions without an additional depth-sensor input.","institution":"Shanghai Jiao Tong University / KAUST / Nanyang Technological University","venue":"NeurIPS 2026","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2605.14950","code":"https://github.com/MINT-SJTU/Evo-Depth","weights":"https://huggingface.co/MINT-SJTU/EVO-Depth-LIBERO"},"policy":{"family":"VLA","baseModel":"InternVL3-1B backbone (InternViT-300M plus Qwen2.5-0.5B, retaining the first 14 language layers), with Implicit Depth Encoding Module, Spatial Enhancement Module and flow-matching action expert.","modelSize":"Reported complete Evo-Depth model0.9B parameters; backbone identifier InternVL3-1B is a model-family name, not an extra additive parameter budget.","inputs":"Multi-view RGB images, language instruction and robot state. IDEM infers compact geometric/depth features internally, without measured depth or point-cloud input.","outputs":"Continuous action chunks; representative Meta-World configuration uses horizon 50 with padded action/state dimension 24 (Table4); actual actuator dimensions depend on embodiment.","trainingData":"Per-benchmark downstream robot demonstrations for Meta-World, VLA-Arena, LIBERO and LIBERO-Plus, plus separate real-robot data. No single aggregated training-trajectory total reported.","trainingTime":"Wall-clock duration not reported; three-stage alignment/training lengths depend on benchmark configuration.","trainingHardware":"NVIDIA A100 GPUs; representative Meta-World run8 GPUs (AppendixF). Real inference efficiency measured on RTX4090D.","controlRate":"12.3 Hz model inference in real-robot profiling on RTX4090D; this is not a low-level servo frequency.","trainingMethod":"Progressive alignment: initial action/SEM alignment, spatial alignment including IDEM, then joint optimization; action expert trained with flow matching.","evaluation":"Meta-World MT50, VLA-Arena, LIBERO and LIBERO-Plus; 3 real tasks (Orange Placement, Tennis Deposit, Cup Stacking) and visual/spatial disturbance settings."},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Root code license file not found at review; model/data terms not verified. Third-party backbones and dependencies retain separate terms.","notes":"Official training scripts, websocket server and four benchmark clients are public; checkpoint links for LIBERO, MetaWorld and VLA-Arena are listed."},"evidence":[{"url":"https://arxiv.org/abs/2605.14950","label":"Paper and first-public arXiv record","fields":["title","date","institution","policy","modalities","embodiments","environments"],"note":"Technical fields follow the inspected full manuscript and appendices; date is arXiv v1, not the current revision."},{"url":"https://github.com/MINT-SJTU/Evo-Depth","label":"Official repository","fields":["release","policy.baseModel","policy.inputs","policy.evaluation","imageCandidates"],"note":"Current implementation/release scope can differ from paper experiments."}],"caveats":["Depth enhancement is RGB-inferred geometry, not evidence of a measured depth stream at deployment.","0.9B is the authors’ reported model size; do not add the backbone family label 1B as another component.","Representative Meta-World padded action dimensions do not describe all robot embodiments."],"verified":"2026-10-17"},{"id":"longact-holomind","name":"HoloMind / LongAct","title":"When Robots Do the Chores: A Benchmark and Agent for Long-Horizon Household Task Execution","date":"2026-05-14","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Combines a task DAG, spatial memory, episodic experience and a critic to execute compound household routines through discrete simulator skills.","institution":"Institute of Automation, Chinese Academy of Sciences; Zhongguancun Academy; University of Chinese Academy of Sciences","venue":"Preprint","modalities":["RGB","Depth","Language"],"environments":["Simulation"],"embodiments":["Simulated household agent"],"links":{"paper":"https://arxiv.org/abs/2605.14504"},"policy":{"family":"Robot agent","baseModel":"GPT-5 / GPT-5-mini and Qwen3-VL alternatives from 2B to 32B instantiate the planner, executor and critic.","modelSize":"Qwen3-VL alternatives: 2B–32B; hosted GPT sizes are undisclosed.","inputs":"Task, RGB-D, semantic segmentation, retrieved spatial / episodic memory and critic feedback.","outputs":"Task-DAG updates, navigation actions and object interaction actions: pick / place, open / close, toggle and slice.","trainingData":"LongAct contains 300 episodes across more than 100 ProcTHOR house layouts; these are evaluation episodes, not a new motor-policy training dataset.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen VLM planning and retrieval with reflective supervision; the skill library abstracts embodiment-specific low-level control.","evaluation":"LongAct household routines in AI2-THOR / ProcTHOR, with detailed / concise instructions and goal-completion / full-task metrics."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The original paper is public; code, model and dataset release scopes are verified separately where established. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms."},"evidence":[{"url":"https://arxiv.org/abs/2605.14504","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."}],"caveats":["Semantic segmentation is supplied to decouple perception from planning; this richer observation interface must be considered when comparing RGB-only agents.","Manipulation is through discrete household actions and does not establish continuous-arm precision.","The paper also introduces LongAct; a separate benchmark role needs its own complete metadata."],"verified":"2026-10-17"},{"id":"agentchord","name":"AgentChord","title":"From Reaction to Anticipation: Proactive Failure Recovery through Agentic Task Graph for Robotic Manipulation","date":"2026-05-12","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Compiles anticipated failure branches and executable monitors into a task graph so a robot can recover without a fresh high-latency model query.","institution":"Chinese University of Hong Kong, Shenzhen; DexForce Technology; Shenzhen Loop Area Institute","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Real world"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2605.11951","project":"https://shengxu.net/AgentChord/","code":"https://github.com/Jasonxu1225/AgentChord"},"policy":{"family":"Robot agent","baseModel":"GPT-5 supplies task-structuring, recovery-augmentation and monitor-compilation agents; shared perception and motion-optimization tools.","modelSize":"Not reported","inputs":"Task, calibrated multi-view RGB-D, measured joint / gripper state, semantic goals, constraints and execution outcomes.","outputs":"Nominal / recovery graph nodes, executable constraint monitors and receding-horizon end-effector trajectories.","trainingData":"Robot trials provide execution feedback; recovery-augmented trajectories are additionally evaluated as downstream imitation-learning data.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Calibrated RGB-D cameras capture at 10 Hz; this is not a measured model-query or arm-servo rate.","trainingMethod":"Frozen foundation model compiles programs and recovery graph branches before / during execution; geometric monitoring triggers local recovery. Downstream policy training is a separate experiment.","evaluation":"Six real-world tasks on an AgileX CobotMagic dual-arm platform with object-instance variation."},"release":{"code":"Available","weights":"Not applicable","data":"Not verified","license":"Apache-2.0 (code); model, dependency and dataset terms are separate.","notes":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms."},"evidence":[{"url":"https://arxiv.org/abs/2605.11951","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/Jasonxu1225/AgentChord/main/README.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."},{"url":"https://raw.githubusercontent.com/Jasonxu1225/AgentChord/main/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Code-license text checked directly; this license is not automatically assigned to model weights or third-party data."}],"caveats":["Low-latency recovery relies on compiled constraints and local motion tools rather than a VLM operating at camera frequency."],"verified":"2026-10-17"},{"id":"si-diff","name":"SI-Diff","title":"SI-Diff: A Framework for Learning Search and High-Precision Insertion with a Force-Domain Diffusion Policy","date":"2026-05-12","year":2026,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"A mode-conditioned force-domain diffusion policy learns both lateral search and tight-clearance insertion from successful teacher trajectories.","institution":"Not reported","venue":"Preprint","modalities":["Force/Torque","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2605.12247"},"policy":{"family":"Diffusion","baseModel":"TacDiffusion-style denoiser with learned search/insertion mode tokens","modelSize":"Not reported","inputs":"Current/previous external wrench, internal wrench and end-effector velocity; 36D total plus mode","outputs":"6D feed-forward desired wrench for impedance control","trainingData":"300 successful search examples plus 1,500 TacDiffusion insertion demonstrations; one0.1-mm-clearance cuboid training geometry","trainingTime":"2,000 epochs; wall-clock not reported","trainingHardware":"One NVIDIA RTX A6000 48GB","controlRate":"Not reported","trainingMethod":"Mode embedding, stochastic teacher data and balanced batch sampling","evaluation":"Real Franka peg search/insertion; initial lateral offsets up to 5 mm and zero-shot unseen geometries"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."},"evidence":[{"url":"https://arxiv.org/abs/2605.12247","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://arxiv.org/html/2605.12247","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["“Tactile” in the paper denotes wrench feedback, not optical or skin tactile sensing.","Search data is filtered to successful and efficient teacher trajectories."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."}},"relatedIds":[]},{"id":"embodiskill","name":"EmbodiSkill","title":"EmbodiSkill: Skill-Aware Reflection for Self-Evolving Embodied Agents","date":"2026-05-11","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Distinguishes faulty skill content from failures to follow valid guidance, then makes targeted revisions to an external procedural skill.","institution":"Nanjing University; Huazhong University of Science and Technology; USTC; Microsoft Research; Tsinghua University AIR","venue":"Preprint","modalities":["RGB","Language"],"environments":["Simulation"],"embodiments":["Simulated household agent"],"links":{"paper":"https://arxiv.org/abs/2605.10332","code":"https://github.com/air-embodied-brain/EmbodiSkill"},"policy":{"family":"Robot agent","baseModel":"ALFWorld executors: Qwen2.5-14B-Instruct / Qwen3.5-27B. Visual executors: Qwen3-VL-8B / 32B. GPT-5.2 or Gemini 3 Flash evolves skills.","modelSize":"Executor variants: 14B / 27B text models and 8B / 32B visual models; these are alternative backbones, not a summed system size.","inputs":"Task, current text / RGB observation, within-episode history and an evolving skill body / appendix.","outputs":"Discrete embodied interaction actions and evidence-targeted revisions of reusable procedural skill instructions.","trainingData":"Task execution trajectories supply reflection / skill-revision evidence; no executor parameter training in the method.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen executor and evolution-model weights; content-changing evidence revises the skill body while execution-lapse evidence preserves / emphasizes valid instructions.","evaluation":"ALFWorld Put / Clean / Heat / Cool / Examine / Puttwo tasks, EB-Habitat household interaction and separately reported EB-Navigation."},"release":{"code":"Available","weights":"Not applicable","data":"Not verified","license":"MIT (code); model, dependency and dataset terms are separate.","notes":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms."},"evidence":[{"url":"https://arxiv.org/abs/2605.10332","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/air-embodied-brain/EmbodiSkill/main/README.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."},{"url":"https://raw.githubusercontent.com/air-embodied-brain/EmbodiSkill/main/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Code-license text checked directly; this license is not automatically assigned to model weights or third-party data."}],"caveats":["Household object interaction uses discrete / symbolic simulator actions; this is not continuous robot-arm control.","Navigation results are a separate evaluation and do not substitute for manipulation precision."],"verified":"2026-10-17"},{"id":"stereopolicy","name":"StereoPolicy","title":"StereoPolicy: Improving Robotic Manipulation Policies via Stereo Perception","date":"2026-05-11","year":2026,"collections":["policy"],"categories":["rgbd-3d","visual-precision-rl"],"resourceType":"policy","summary":"Stereo image features are fused with a Stereo Transformer and supplied to diffusion policies or pretrained VLAs, without constructing an explicit depth map.","institution":"Stanford University / Northwestern University / Lambda","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2605.09989","project":"https://stereopolicy.github.io/"},"policy":{"family":"Hybrid","baseModel":"StereoPolicy-DP: 2D vision encoder + external-view DINOv2 + Stereo Transformer + diffusion head. StereoPolicy-VLA integrates with π0.5 and GR00T-N1.5.","modelSize":"StereoPolicy-DP profiling configuration: 112M parameters, including 10M in the Stereo Transformer (Appendix D.7). VLA backbone sizes are separate.","inputs":"Synchronized left/right RGB image pairs from external and wrist views, robot state, and language for VLA variants. Stereo correspondence is implicit; no explicit depth-map or point-cloud policy input.","outputs":"Continuous robot action sequences; DP prediction horizon 16 and observation horizon 2. π0.5 evaluation uses action horizon 16 and executes the full chunk.","trainingData":"Simulated DP tasks use 100–300 demonstrations depending on task/condition. Real tabletop: 200 demos per task; mobile: 75 per task. RoboCasa VLA evaluation covers 24 tasks and 30/100-demo settings. Corpora overlap across variants.","trainingTime":"Wall-clock duration not reported. DP: 500 epochs on RoboMimic, 1000 on OmniGibson; GR00T-N1.5 fine-tuning60 K steps; π0.5 fine-tuning80 K steps.","trainingHardware":"Appendix C.2 reports 8 H100 GPUs for VLA training settings; Appendix D.7 profiles DP inference on 1 H100. Training and inference scopes are separate.","controlRate":"Appendix D.7/Table10: end-to-end action-chunk latency 698 ms median / 736 ms p95 on one H100, batch 1, observation horizon 2, prediction horizon 16, action dimension 7 and 100 denoising steps. This profiling latency is not a physical servo frequency.","trainingMethod":"Diffusion imitation learning or VLA fine-tuning with alternating stereo self/cross attention; 2D pretrained encoders retained.","evaluation":"RoboMimic, OmniGibson and 24-task RoboCasa-Kitchen subsets; 7 real tasks: 5 tabletop tasks including transparent cup hanging, plus 2 bimanual mobile tasks."},"release":{"code":"Not released","weights":"Not released","data":"Not verified","license":"No policy code/weights license verified; source figures retain author rights.","notes":"Project lists Code / soon at review cutoff; experiment disclosure does not establish a complete code or checkpoint release."},"evidence":[{"url":"https://arxiv.org/abs/2605.09989","label":"Paper and first-public arXiv record","fields":["title","date","institution","policy","modalities","embodiments","environments"],"note":"Technical fields follow the inspected full manuscript and appendices; date is arXiv v1, not the current revision."},{"url":"https://stereopolicy.github.io/","label":"Official project","fields":["summary","imageCandidates","release"],"note":"Current author project and release links."}],"caveats":["Stereo RGB is recorded as RGB, with implicit binocular geometry; it is not a measured Depth modality.","DP112M profiling configuration is not the size or cost of the VLA variants."],"verified":"2026-10-17"},{"id":"at-vla","name":"AT-VLA","title":"AT-VLA: Adaptive Tactile Injection for Enhanced Feedback Reaction in Vision-Language-Action Models","date":"2026-05-08","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Adaptively gates tactile injection and uses a faster tactile stream to adjust actions within a slower VLA architecture.","institution":"Peking University / PrimeBot","venue":"CVPR 2026","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2605.07308","project":"https://sites.google.com/view/at-vla","code":"https://github.com/clorislili/AT-VLA"},"policy":{"family":"VLA","baseModel":"AgiBot GO-1; Intern-VL-2B visual-language model and DiT action expert","modelSize":"2B VLM component; total not reported","inputs":"RGB, instruction, state and tactile-force signals","outputs":"Action chunks with adaptive faster tactile adjustment","trainingData":"Not reported","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Paper reports ≈40 ms tactile feedback path; distinct from full vision-language inference","trainingMethod":"Adaptive tactile gate plus slow–fast tactile integration","evaluation":"Contact-rich tasks on Genie 1 hardware"},"release":{"code":"Partial","weights":"Not verified","data":"Not verified","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Official repository primarily exposes inference; external GO-1 backbone required."},"evidence":[{"url":"https://arxiv.org/abs/2605.07308","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/clorislili/AT-VLA","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2605.07308","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."},{"url":"https://github.com/clorislili/AT-VLA/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["Repository pi0 naming is explicitly a historical naming artifact; this is GO-1, not Physical Intelligence π0."],"verified":"2026-10-19","releaseByCollection":{"policy":{"code":"Partial","weights":"Not verified","data":"Not verified","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Official repository primarily exposes inference; external GO-1 backbone required."}},"relatedIds":[]},{"id":"consisvla4d","name":"ConsisVLA-4D","title":"ConsisVLA-4D: Advancing Spatiotemporal Consistency in Efficient 3D-Perception and 4D-Reasoning for Robotic Manipulation","date":"2026-05-06","year":2026,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Adds cross-view semantics, geometric token fusion and future-scene reasoning to an OpenVLA-style manipulation policy.","institution":"Harbin Institute of Technology, Shenzhen","venue":"CVPR 2026","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2605.05126","code":"https://github.com/iLearn-Lab/CVPR26-ConsisVLA-4D"},"policy":{"family":"VLA","baseModel":"OpenVLA + VGGT geometric features; CV-Aligner, CO-Fuser, CS-Thinker","modelSize":"7B base plus approximately 2B added, mainly VGGT; exact full count not reported","inputs":"Multi-view RGB, language and robot state; geometry inferred by VGGT","outputs":"Parallel continuous action chunks; eight steps single arm, 25-step bimanual setting","trainingData":"LIBERO, ManiSkill2/RoboTwin demonstrations; four real R1 Lite tasks","trainingTime":"Table: 8.6 h simulation / 10.1 h real-world fine-tuning setting","trainingHardware":"4 A800 training GPUs; one RTX 5090 real inference","controlRate":"Table reports 110/231 ms query latency and 72.7/108.2 actions/s sim/real; throughput is chunked","trainingMethod":"LoRA VLA fine-tuning with semantic/geometric consistency objectives","evaluation":"LIBERO, ManiSkill2, RoboTwin2 and Galaxea R1 Lite bimanual tasks"},"release":{"code":"Partial","weights":"Not verified","data":"Not verified","license":"Not verified (code); model and data terms not verified","notes":"Official repository releases LIBERO fine-tuning/evaluation examples; full experimental artifacts not verified."},"evidence":[{"url":"https://arxiv.org/abs/2605.05126","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/iLearn-Lab/CVPR26-ConsisVLA-4D","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."}],"caveats":["3D representations are inferred from RGB; no measured-depth deployment input.","Current release explicitly provides LIBERO examples; full real-world checkpoints/data not verified."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Partial","weights":"Not verified","data":"Not verified","license":"Not verified (code); model and data terms not verified","notes":"Official repository releases LIBERO fine-tuning/evaluation examples; full experimental artifacts not verified."}},"relatedIds":[]},{"id":"molmoact2","name":"MolmoAct2","title":"MolmoAct2: Action Reasoning Models for Real-world Deployment","date":"2026-05-04","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Uses an embodied-reasoning VLM, discrete-action pretraining and a continuous flow expert, with optional depth/trajectory reasoning.","institution":"Allen Institute for AI / University of Washington","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2605.02881","project":"https://allenai.org/blog/molmoact2","code":"https://github.com/allenai/molmoact2","data":"https://huggingface.co/collections/allenai/molmoact2-datasets-69f81e316ec3daafe3f9555c","weights":"https://huggingface.co/collections/allenai/molmoact2-models-69f81e05242e2499606b1be6"},"policy":{"family":"VLA","baseModel":"Molmo2-ER-4B backbone derived from Molmo2; FAST action tokenizer and flow-matching expert","modelSize":"4B VLM backbone; full action-expert-inclusive total not reported","inputs":"Multi-view RGB, task language and embodiment-specific state; Think variant predicts its own depth/trajectory tokens","outputs":"Continuous absolute-joint or relative-EEF chunks: 30 steps YAM/SO100, 15 DROID, 10 LIBERO","trainingData":"Open robot mixture including 34.5k bimanual YAM demos /720 h, filtered DROID and 38,059 SO100/101 demos /184 h; embodied-reasoning data","trainingTime":"Action pretraining 200k steps/90 h; post-training 100k/36 h; main embodiment fine-tunes100k/36 h; distinct earlier Molmo2-ER cost not included","trainingHardware":"Pretraining64 × H100 (5,760 GPU-h); post-training64 × H100 (2,304 GPU-h); fine-tuning32/64 × H100 (1,152/2,304 GPU-h)","controlRate":"Action timelines30 Hz YAM/SO100, 15 Hz DROID, 10 Hz LIBERO; chunks execute open-loop before policy re-query","trainingMethod":"Embodied reasoning specialization → discrete FAST action pretraining → joint continuous/discrete action post-training → embodiment fine-tuning","evaluation":"MolmoSpace, LIBERO, RoboCasa and real YAM/Franka/SO100/101; separate Think reasoning variant"},"release":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2605.02881","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/allenai/molmoact2","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."},{"url":"https://raw.githubusercontent.com/allenai/molmoact2/main/LICENSE","label":"Repository license file","fields":["release.license"],"note":"Code scope; model, data and third-party component terms can differ."}],"caveats":["Depth reasoning tokens are generated from RGB; they do not establish measured depth-camera input.","The action training compute excludes the earlier VLM foundation and embodied-reasoning backbone training."],"verified":"2026-10-17"},{"id":"hyper-dp3","name":"Hyper-DP3","title":"Hyper-DP3: Frequency-Aware Right-Sizing of 3D Diffusion Policies for Visuomotor Control","date":"2026-05-02","year":2026,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Uses a small Diffusion Mixer and two DDIM steps, motivated by the low-frequency structure of demonstrated action trajectories.","institution":"Harbin Institute of Technology","venue":"Preprint","modalities":["Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2605.01581"},"policy":{"family":"Diffusion","baseModel":"DP3-style point encoder with a six-layer, width-128 Diffusion Mixer decoder","modelSize":"2.52M parameters in the reported policy comparison","inputs":"Single-view robot-centric point cloud and proprioceptive state; physical point cloud from RealSense D455","outputs":"RoboTwin horizon eight, execute six; Adroit/MetaWorld horizon 16, execute eight; physical six-DoF joint positions","trainingData":"50 demonstrations per RoboTwin task; 10 per Adroit/MetaWorld task; 50 physical Piper trajectories","trainingTime":"3,000 epochs; wall-clock Not reported","trainingHardware":"One RTX 5880 Ada for training; physical action inference on RTX 4060","controlRate":"4.50 ms inference in the reported two-step comparison; physical low-level control rate Not reported","trainingMethod":"Diffusion behavior cloning with lightweight token/channel mixing and two-step DDIM; no consistency distillation required","evaluation":"RoboTwin 2.0, Adroit, MetaWorld and physical AgileX Piper tasks"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified; pretrained backbones and data retain separate terms","notes":"Availability concerns author-linked artifacts; no training or installation reproduced."},"evidence":[{"url":"https://arxiv.org/abs/2605.01581","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."}],"caveats":["Top-five checkpoint success averaging is used for Adroit/MetaWorld; it is not a final-checkpoint-only protocol.","Frequency-based simplification is studied on these action distributions, not a proof for every contact-dynamics setting."],"verified":"2026-10-17"},{"id":"wm-robot-learning-survey","name":"World Models for Robot Learning — Survey","title":"World Model for Robot Learning: A Comprehensive Survey","date":"2026-04-30","year":2026,"collections":["policy"],"categories":["geometry-models","generalist-foundation","agent-rsi-icl"],"resourceType":"survey","summary":"A robot-learning review separates predictive models used inside policies, learned simulators and robotic-video generation.","institution":"Nanyang Technological University; UC Berkeley; Stanford; University of Tokyo; Oxford; Microsoft; ETH Zurich; Princeton; Harvard","venue":"Preprint · Survey","modalities":[],"environments":["Literature review"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2605.00080","project":"https://ntumars.github.io/wm-robot-survey/","code":"https://github.com/NTUMARS/Awesome-World-Model-for-Robotics-Policy"},"release":{"code":"Available — maintained literature list, not a new executable policy","weights":"Not applicable — survey","data":"Not applicable — literature review","license":"Original article and companion repository retain their respective terms; not independently verified as one shared license.","notes":"Paper, project taxonomy and bibliography inspected; survey citations discover methods, original sources establish method specifications."},"evidence":[{"url":"https://arxiv.org/abs/2605.00080","label":"Original survey and first manuscript date","fields":["title","date","survey","institution"]},{"url":"https://ntumars.github.io/wm-robot-survey/","label":"Author taxonomy and timeline","fields":["survey","release.code"]},{"url":"https://github.com/NTUMARS/Awesome-World-Model-for-Robotics-Policy","label":"Author-maintained bibliography","fields":["survey.reviewMethod","release.code"]}],"caveats":["A learned environment, a policy using one and a policy trained with a prediction objective are separate roles."],"verified":"2026-10-17","survey":{"scope":"Predictive world models for robot learning, with connections to navigation and driving.","taxonomy":["World model for policy","World model as simulator","Robotic video world model"],"adaptationRegimes":"Prediction–action coupling; learned-simulator policy improvement/evaluation; controllable or structured video prediction.","contextSources":"Observations, instructions and action conditioning; geometric and contact structure are distinguished from generic video prediction.","evaluationAxes":["Predictive fidelity","Action consistency","Downstream task utility","Simulator reliability","Runtime and rollout stability"],"corpus":"43-page author manuscript with six figures; project timeline explicitly ends March 31, 2026, while the live companion list can evolve.","reviewMethod":"Architectural and functional synthesis with datasets, benchmark tables and an author-maintained bibliography.","coverageLimits":"Not a manipulation-only or measured-depth-only inventory. A listed video model is not automatically an executable robot controller; the timeline date is not the review date of every later repository entry."}},{"id":"x-wam","name":"X-WAM","title":"Unified 4D World Action Modeling from Video Priors with Asynchronous Denoising","date":"2026-04-29","year":2026,"collections":["policy"],"categories":["rgbd-3d","generalist-foundation"],"resourceType":"policy","summary":"Predicts RGB-D futures and robot actions from RGB context, using a depth branch and asynchronous denoising schedules.","institution":"Tsinghua University / Xiaomi Robotics","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2604.26694","project":"https://sharinka0715.github.io/X-WAM/","code":"https://github.com/sharinka0715/X-WAM","data":"https://huggingface.co/datasets/sharinka0715/X-WAM-RoboTwin","weights":"https://huggingface.co/sharinka0715/X-WAM-checkpoints"},"policy":{"family":"World-action model","baseModel":"Wan2.2-TI2V-5B, ten replicated depth blocks, action/state MLPs and asynchronous noise sampling.","modelSize":"5B video backbone; complete aggregate with the depth branch not reported in inspected source.","inputs":"Initial multi-view RGB, language and proprioceptive state. Depth is predicted or supervised, not a measured deployment input.","outputs":"Future RGB, depth, states and end-effector actions; the depth branch can be disabled during action decoding.","trainingData":"Approximately 1.49M episodes / 5,874 hours of real and simulated data. Video Depth Anything supplies pretraining pseudo-depth; simulator replay supplies fine-tuning depth.","trainingTime":"40k pretraining, 20k benchmark fine-tuning and 40k physical posttraining steps; wall-clock duration not reported.","trainingHardware":"256 H20 GPUs for pretraining; 32 H20 for benchmark fine-tuning; 64 H20 for physical posttraining.","controlRate":"Physical AC One: approximately 300 ms per chunk on RTX 5090D; RTC executes 15 actions per chunk at 15 Hz. Query latency and execution frequency differ.","trainingMethod":"Joint flow matching, unilateral auxiliary depth branch and correlated noise sampling matched to asynchronous inference.","evaluation":"RoboCasa 24-task subset, RoboTwin 2.0 and physical earphone packing; RGB/depth/3D prediction metrics."},"release":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 code; weights/data/backbone terms separate","notes":"Availability concerns author-linked artifacts; no training or installation reproduced."},"evidence":[{"url":"https://arxiv.org/abs/2604.26694","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/sharinka0715/X-WAM","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://sharinka0715.github.io/X-WAM/","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."}],"caveats":["Despite predicted RGB-D futures, policy conditioning uses RGB; no measured-depth deployment tag.","Physical deployment and RTX 3090 ablation latencies use different settings."],"verified":"2026-10-17"},{"id":"moss","name":"MoSS","title":"Modular Sensory Stream for Integrating Physical Feedback in Vision-Language-Action Models","date":"2026-04-25","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Adds modular sensory streams to VLA action experts to combine tactile, force and joint-torque feedback.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Tactile","Force/Torque","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2604.23272"},"policy":{"family":"VLA","baseModel":"π0 and GR00T N1.5 with separate sensory-expert streams","modelSize":"Not reported","inputs":"RGB, language, proprioception and hardware-dependent tactile/force/joint-torque signals","outputs":"Absolute joint-position action chunks; auxiliary future physical signals during training","trainingData":"100 demos each: Unstack Cup, PnP Egg, Board Erase, Plug Insertion, Pick Paper; 60 Sort by Weight","trainingTime":"GR00T60k /π0 30k iterations; wall-clock not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Freeze base for physical alignment, then joint fine-tuning with future-signal supervision","evaluation":"Six real tasks on gripper and bimanual dexterous setups"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."},"evidence":[{"url":"https://arxiv.org/abs/2604.23272","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://arxiv.org/html/2604.23272","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Stereo egocentric cameras are used without depth input.","Hardware physical channels and the trainable portion differ between the two backbones."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."}},"relatedIds":[]},{"id":"bicicle","name":"BiCICLe","title":"Bimanual Robot Manipulation via Multi-Agent In-Context Learning","date":"2026-04-22","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Frozen language-model agents coordinate bimanual keyposes through a leader-follower in-context interface.","institution":"Sapienza University of Rome / TU Darmstadt","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Bimanual"],"links":{"paper":"https://arxiv.org/abs/2604.20348","project":"https://alesspalma.github.io/bicicle","code":"https://github.com/alesspalma/icl_bimanual","data":"https://huggingface.co/datasets/alesspalma/icl_bimanual_data"},"policy":{"family":"Robot agent","baseModel":"GPT-5-mini in main experiments; Qwen2.5-7B local-language-model variant.","modelSize":"7B Qwen variant; hosted GPT-5-mini parameters not disclosed.","inputs":"Ten serialized demonstration examples plus discretized object/keypoint observations; real object poses are estimated with FoundationPose.","outputs":"Two sequentially coordinated seven-dimensional end-effector pose/gripper keyposes, executed using motion planning.","trainingData":"Ten prompt demonstrations per task; no method-specific gradient training.","trainingTime":"No robot-policy gradient training for BiCICLe.","trainingHardware":"Not applicable to frozen hosted-model use; local inference hardware not reported.","controlRate":"Not reported","trainingMethod":"Training-free leader/follower in-context prediction with inter-arm conditioning.","evaluation":"13 TWIN simulated bimanual tasks, three seeds × 100 episodes per task; three real bimanual Franka tasks."},"release":{"code":"Available","weights":"Not applicable","data":"Available","license":"MIT for repository code; external model, dataset and dependency terms remain separate.","notes":"Code, prompt templates, TWIN integrations and task dataset are public. Included learned-policy baseline checkpoints are not BiCICLe-trained weights."},"evidence":[{"url":"https://arxiv.org/abs/2604.20348","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://alesspalma.github.io/bicicle","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/alesspalma/icl_bimanual","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."},{"url":"https://github.com/alesspalma/icl_bimanual/blob/HEAD/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Actual license file inspected; code license does not automatically license data or weights."}],"caveats":["Simulation uses benchmark state / keypoint observations; real perception and MoveIt execution are separate components.","No claim of direct high-rate torque control by the LLM."],"verified":"2026-10-17"},{"id":"fingereye","name":"FingerEye Policy","title":"FingerEye: Learning Dexterous Manipulation with Continuous Vision-Tactile Sensing","date":"2026-04-22","year":2026,"collections":["policy"],"categories":["tactile-il","tactile-foundation"],"resourceType":"policy","summary":"Continuous fingertip RGB carries both pre-contact stereo cues and post-contact deformation for dexterous policy learning.","institution":"National University of Singapore / Robo Science / Huazhong University of Science and Technology / South China University of Technology","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2604.20689","project":"https://nus-lins-lab.github.io/FingerEyeWeb/","code":"https://github.com/ariszxxu/FingerEye"},"policy":{"family":"Transformer","baseModel":"Transformer action-chunk policy with4 encoder and4 decoder layers, width512,16 heads; group-local observation encoding and grouped action cross-attention.","modelSize":"Not reported","inputs":"Wrist RGB, proprioception andper-fingertip binocular RGB. Main policies do not use estimated wrench/plate pose.","outputs":"Dexterous action chunks with grouped modality fusion.","trainingData":"Task-specific demonstrations: simulation Coin50, Nut50, Peg-in-Hole200; real Coin60, Chip30, Letter60, Syringe40. Table8 counts are separate simulation/real task settings.","trainingTime":"Simulation Coin/Nut500 epochs each, Peg-in-Hole300; each real task500 epochs. Wall-clock time Not reported.","trainingHardware":"Not reported","controlRate":"10 Hz collection and policy execution.","trainingMethod":"Demonstration-based policy learning with controlled fusion interface comparisons.","evaluation":"Coin standing, chip picking, letter retrieving, syringe manipulation, nut picking, peg-in-hole pushing and related settings;64 sim rollouts/evaluation seed, 20 real rollouts/method/task."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Not verified; no unambiguous repository license identified.","notes":"Author-linked repository and actual LICENSE inspected; code, checkpoints anddatasets have independent availability."},"evidence":[{"url":"https://arxiv.org/abs/2604.20689","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://nus-lins-lab.github.io/FingerEyeWeb/","label":"Official author project","fields":["links","release"],"note":"Live artifact status reviewed separately from paper claims."},{"url":"https://github.com/ariszxxu/FingerEye","label":"Official released implementation and LICENSE","fields":["links","release"],"note":"Repository metadata,README and actualNone inspected; availability scoped to the listed component."}],"caveats":["Main learned policy sees RGB contact deformation, not explicit measured depth, plate pose or wrench; these are sensor characterization/auxiliary studies.","Digital twin matches compliant surface pose approximately; nonlinear silicone/deformable mechanics not validated.","Four/six FingerEye RGB views mean two/three binocular modules, not four/six physical sensors; Table8 also includes one wrist camera.","Four/six FingerEye RGB views mean two/three binocular modules, not four/six physical sensors; Table8 also includes one wrist camera."],"verified":"2026-10-17"},{"id":"joyai-ra01","name":"JoyAI-RA 0.1","title":"JoyAI-RA 0.1: A Foundation Model for Robotic Autonomy","date":"2026-04-22","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"A multi-source VLA combines semantic/subtask prediction with a Perceiver-style flow action expert and shared cross-embodiment action representation.","institution":"Joy Future Academy / JD","venue":"Technical report","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2604.20100","project":"https://joyai-ra.github.io/"},"policy":{"family":"VLA","baseModel":"Pretrained 4B VLM (exact initialization name not disclosed) and a 600M Perceiver-style perception/action expert trained with flow matching.","modelSize":"Figure 3 labels a 4B VLM and 600M action expert; complete deployed parameter accounting not separately reported.","inputs":"Multi-view RGB observations, language instruction and proprioceptive state. Camera-relative actions require reliable extrinsics for applicable sources; no deployed tactile or measured-depth stream reported.","outputs":"Intermediate textual semantic/subtask predictions followed by continuous action chunks in a masked unified action space, including arms/grippers and dexterous-hand slots where supported.","trainingData":"General/spatial VL data, in-house EgoLive human video, simulated trajectories and multi-platform robot data. EgoLive covers 1969 object categories and 1796 action categories; these are category counts, not demonstrations. RoboTwin post-training: 2500 clean plus 25000 randomized demos.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"VLM co-pretraining, VLA co-pretraining with flow action learning and language objectives, then end-to-end domain-specific post-training.","evaluation":"50-task RoboTwin2.0 Easy/Hard, RoboCasa GR1 tabletop tasks, physical AgileX and AgiBot G1 multi-scenario tasks. Overall pretraining-corpus size and compute not disclosed."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Executable code, weights and complete training-data licenses not verified.","notes":"Primary technical report accessible. The paper-linked project URL currently serves a rebuild-template shell; no policy-code/weight release can be established from that page."},"evidence":[{"url":"https://arxiv.org/abs/2604.20100","label":"Paper and first-public arXiv record","fields":["title","date","institution","policy","modalities","embodiments","environments"],"note":"Technical fields follow the inspected full manuscript and appendices; date is arXiv v1, not the current revision."},{"url":"https://joyai-ra.github.io/","label":"Paper-linked project URL, release unresolved","fields":["release.notes"],"note":"Inspected shell identifies itself as JoyAI-RA Local Rebuild Template; substantive method facts come from the primary manuscript."}],"caveats":["Reported object/action category counts must not be interpreted as independent trajectories or hours.","Paper-linked project URL availability is distinct from a verified implementation or checkpoint release.","4B and 600M are separately labeled architecture components; exact full-system accounting is not disclosed."],"verified":"2026-10-17"},{"id":"gated-memory-policy","name":"Gated Memory Policy (GMP)","title":"Gated Memory Policy: In-Context Memorization and Adaptation","date":"2026-04-21","year":2026,"collections":["policy","benchmark"],"categories":["agent-rsi-icl","general-manipulation"],"resourceType":"policy","summary":"A diffusion policy learns when to recall visual/action history and what context matters for within-trial memory and cross-trial adaptation.","institution":"Stanford University","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2604.18933","project":"https://gated-memory-policy.github.io/","code":"https://github.com/real-stanford/gated-memory-policy","data":"https://huggingface.co/datasets/yihuai-gao/gated-memory-policy","weights":"https://huggingface.co/yihuai-gao/gated-memory-policy"},"policy":{"family":"Diffusion","baseModel":"Transformer Diffusion Policy with gated history cross-attention, memory encoder and a calibrated memory gate.","modelSize":"Not reported","inputs":"Current RGB image, robot proprioception, historical images / actions and trial context.","outputs":"Continuous robot action chunks; appendix simulation setting uses a 16-step horizon.","trainingData":"MemMimic task demonstrations plus RoboMimic and MIKASA-Robo experiments; public simulation and real deployment datasets.","trainingTime":"Iterative Pushing example: 1.2 H100 GPU-hours baseline, 6.7 memory policy, 6.4 rollouts, 1.3 gate training, 31.2 GMP; these are separate pipeline stages, not wall-clock time.","trainingHardware":"H100 GPU-hour training budgets; RTX 3080 latency comparison and RTX 5090 deployment timing are separate inference measurements.","controlRate":"Reported real-context policy inference: 0.16 s with eight denoising steps on RTX 5090. Example simulation commands run at 10 Hz with 500 Hz physics.","trainingMethod":"Diffusion behavior cloning, history-action noise, cross-attention memory and gate calibration from rollouts.","evaluation":"Six MemMimic task families across simulation / real robots; RoboMimic and MIKASA-Robo memory tasks."},"release":{"code":"Available","weights":"Available","data":"Available","license":"MIT for repository code; external model, dataset and dependency terms remain separate.","notes":"Official training/evaluation, task data and in-the-wild deployment checkpoint are public; robot deployment uses the separately linked iPhUMI implementation."},"evidence":[{"url":"https://arxiv.org/abs/2604.18933","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats","benchmark"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://gated-memory-policy.github.io/","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/real-stanford/gated-memory-policy","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."},{"url":"https://github.com/real-stanford/gated-memory-policy/blob/HEAD/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Actual license file inspected; code license does not automatically license data or weights."},{"url":"https://arxiv.org/abs/2604.18933","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["No tactile or wrist force stream is claimed from RGB/history-based memory.","The training-cost breakdown is for one challenging task, not the total complete benchmark."],"verified":"2026-10-17","benchmark":{"simulator":"MuJoCo","simulatorVersion":"Not reported","taskCount":"6 headline task families (four simulated, two real), plus the appendix-only T3′ Continuous Place Back real setting.","tasks":["Match Color","Discrete Place Back","In-the-wild Flip and Place Back","Iterative Pushing","Iterative Flinging","Iterative Casting","Continuous Place Back (appendix T3′)"],"taskListScope":"Six headline T1–T6 families plus the auxiliary appendix T3′ real setting; excludes external RoboMimic / MIKASA-Robo evaluation tasks.","capabilities":["Within-trial memorization","Cross-trial adaptation","Dynamics-sensitive manipulation"],"metrics":"Task success rate; mean and standard deviation over training seeds.","robots":"Simulation grippers; real UR5 with WSG50 / fin-ray fingers and ARX X5 in-the-wild UMI setup.","sensors":"RGB and robot proprioception.","dataCollection":"Task-dependent heuristic/scripted collectors for MemMimic, plus PPO-oracle trajectories for MIKASA tasks. The real-world collection interface is Not verified.","data":"Task-specific demonstrations; appendix example Match Color has 300 training episodes. Complete task-specific datasets are released.","protocol":"For simulation, each checkpoint evaluated on 100 episodes, best-checkpoint mean/std over three training seeds; real protocols are task-specific.","baselines":"History-free / mid / long-history Diffusion Policy, Past-Token Prediction, history attention variants and GMP."},"benchmarkName":"MemMimic","policyName":"Gated Memory Policy (GMP)"},{"id":"grootn17","name":"GR00T N1.7","title":"GR00T N1.7: An Open Foundation Model for Generalist Robots","date":"2026-04-18","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"GR00 T update replaces Eagle with Cosmos-Reason2 and aligns robot/human actions in a relative end-effector space.","institution":"NVIDIA","venue":"Model release","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Dexterous hand","Humanoid upper body"],"links":{"paper":"https://huggingface.co/nvidia/GR00T-N1.7-3B","project":"https://github.com/NVIDIA/Isaac-GR00T","code":"https://github.com/NVIDIA/Isaac-GR00T","weights":"https://huggingface.co/nvidia/GR00T-N1.7-3B"},"policy":{"family":"VLA","baseModel":"Cosmos-Reason2-2B (Qwen3-VL) + flow-matching diffusion action head","modelSize":"3B total (official model card)","inputs":"RGB views, language instruction, proprioception and embodiment ID","outputs":"Continuous relative end-effector action chunks; embodiment-specific conversion to motor commands","trainingData":"20k h EgoScale human-video pretraining alongside robot data; DROID-specific checkpoint uses ~76k trajectories /350 h","trainingTime":"Model-card carbon estimate assumes 120 h; full pretraining duration is not separately specified","trainingHardware":"Carbon estimate: 64 nodes × 4 GB200; fine-tuning minimum 40 GB GPU, recommended 4–8 H100/L40","controlRate":"Four denoising steps/one camera: H100 11.7 Hz eager /35.9 Hz TensorRT; RTX Pro6000 12.8/35.9 Hz","trainingMethod":"Cross-embodiment pretraining and projector/action-head fine-tuning; relative action convention","evaluation":"RoboLab, RoboCasa, SimplerEnv and real/humanoid deployment examples"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 (code); NVIDIA Open Model License Agreement (N1.7 weights); gated Cosmos-Reason2 backbone access required","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://huggingface.co/nvidia/GR00T-N1.7-3B","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/NVIDIA/Isaac-GR00T","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://github.com/NVIDIA/Isaac-GR00T","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://github.com/NVIDIA/Isaac-GR00T/blob/main/getting_started/hardware_recommendation.md","label":"Official deployment/hardware guide","fields":["policy.controlRate","policy.trainingHardware"],"note":"End-to-end inference frequencies and minimum fine-tuning VRAM."}],"caveats":["April 18 early access preceded July 7 general release.","20k h human video is not 20k h robot action-labelled data.","Published Hz table is end-to-end inference under the stated camera/denoising setting, not all robot servo rates."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 (code); NVIDIA Open Model License Agreement (N1.7 weights); gated Cosmos-Reason2 backbone access required","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"hist-at","name":"HiST-AT","title":"A Hierarchical Spatiotemporal Action Tokenizer for In-Context Imitation Learning in Robotics","date":"2026-04-16","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Hierarchical spatiotemporal action tokenization improves in-context robot imitation through multi-level action and time reconstruction.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2604.15215"},"policy":{"family":"Autoregressive policy","baseModel":"Two-level vector quantization, spatial/temporal reconstruction and a demonstration-conditioned action-token policy.","modelSize":"Not reported","inputs":"Demonstration observation/action context and current visual/robot-state observations.","outputs":"Hierarchical action codes decoded to continuous manipulation controls.","trainingData":"Seven RoboCasa tasks, three ManiSkill tasks and real manipulation demonstrations.","trainingTime":"Policy protocols: 500k iterations RoboCasa / 30k ManiSkill. Tokenizer run-time study: about 44 hours to 500-epoch convergence, 5.3 min/epoch.","trainingHardware":"NVIDIA A100 for training/evaluation and the tokenizer run-time study.","controlRate":"Not reported","trainingMethod":"Hierarchical spatial vector quantization with timestamp reconstruction, integrated into in-context imitation.","evaluation":"Simulated RoboCasa / ManiSkill and real robotic manipulation."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/2604.15215","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."}],"caveats":["Tokenizer convergence timing is not the complete robot-policy training budget.","Policy-stage iterations and tokenizer-stage epochs refer to different experiments."],"verified":"2026-10-17"},{"id":"parts-shape-warping","name":"Parts-based Shape Warping","title":"One-Shot Cross-Geometry Skill Transfer through Part Decomposition","date":"2026-04-16","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Part-specific shape priors preserve relevant local geometry and relations when transferring a one-shot object-manipulation skill.","institution":"Brown University / Northeastern University","venue":"Preprint","modalities":["RGB","Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2604.15455"},"policy":{"family":"Geometric transfer","baseModel":"Part-decomposed cross-geometry interaction transfer. PCA generative shape models per part, relational descriptors and interaction warping.","modelSize":"Not reported","inputs":"Parts-segmented source/target point clouds and one skill transformation demonstration; real setup uses four RGB-D cameras and user point prompts.","outputs":"Transferred pre-place and final object/gripper poses, executed with motion planning.","trainingData":"Five to ten pose-aligned examples per part-category shape prior; a single interaction demonstration per target skill.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Learn reusable per-part geometry, identify relevant inter-object part relationships and warp interaction points.","evaluation":"Two simulated rearrangement tasks; real mug-on-rack, bowl-on-mug and teapot pre-pouring alignment."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No author-linked implementation was verified in the inspected primary manuscript/project. This is an evidence gap, not proof that no implementation exists."},"evidence":[{"url":"https://arxiv.org/abs/2604.15455","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."}],"caveats":["Demonstrator provides two to five segmentation point prompts per part/object in one view.","The real teapot experiment evaluates pre-pouring alignment; do not equate it with a complete measured liquid-pouring policy."],"verified":"2026-10-17"},{"id":"r3d","name":"R3D","title":"R3D: Revisiting 3D Policy Learning","date":"2026-04-16","year":2026,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Revisits training stability and augmentation in 3 D imitation learning, preserving dense point tokens for spatially aware diffusion decoding.","institution":"Zhejiang University / ShanghaiTech University","venue":"Preprint","modalities":["RGB","Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2604.15281","project":"https://r3d-policy.github.io/","code":"https://github.com/Wushr-Lance/R3D-Policy","data":"https://huggingface.co/datasets/eddie-cui/r3d","weights":"https://huggingface.co/eddie-cui/r3d-weights"},"policy":{"family":"Diffusion","baseModel":"Pretrained PointSAM/Uni3D-style encoder + four-block diffusion Transformer","modelSize":"Encoder storage: tiny 53 MB / small 115 MB / base 366 MB; not a parameter count","inputs":"Measured RGB-D point clouds and proprioception; single/multi-camera","outputs":"Arm/joint action chunks with auxiliary end-effector pose prediction","trainingData":"RoboTwin2: 50 demos/task; ManiSkill2: 1,000; real xArm6: 50 demos/task; 3D pretraining on ScanNet/ARKitScenes/PartNeXt","trainingTime":"1,000 epochs for reported real policies; wall-clock not reported","trainingHardware":"8 H20 training/pretraining server; inference RTX 3090","controlRate":"65.26 ms policy latency on RTX 3090; online point-cloud preprocessing updates ~30 Hz","trainingMethod":"3D augmentation, corrected normalization/EMA handling, dense cross-attention diffusion BC","evaluation":"RoboTwin2, ManiSkill2 and six real manipulation task families"},"release":{"code":"Available","weights":"Partial","data":"Available","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2604.15281","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/Wushr-Lance/R3D-Policy","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://r3d-policy.github.io/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/Wushr-Lance/R3D-Policy/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["September 2026 v2 attributes original BN collapse to stale EMA buffers; the project page retains the earlier explanation.","Reported encoder MB are storage sizes, not millions of parameters.","Released weights link is for pretrained encoders; full downstream policy weights not verified."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Partial","data":"Available","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"pi07","name":"π0.7","title":"π0.7: a Steerable Generalist Robotic Foundation Model with Emergent Capabilities","date":"2026-04-16","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Steerable generalist policy uses language, goal images and execution-quality/speed context to improve transfer without per-task fine-tuning.","institution":"Physical Intelligence","venue":"Technical report","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Mobile manipulator"],"links":{"paper":"https://www.pi.website/download/pi07.pdf","project":"https://www.pi.website/blog/pi07"},"policy":{"family":"VLA","baseModel":"Gemma3 4B VLM + temporal/spatial history encoder + 860M flow-matching expert","modelSize":"Approximately 5B total; 860M action expert","inputs":"Multi-view RGB/history, proprioception, language/coaching; optional goal images and execution metadata","outputs":"Continuous action chunks and high-level/subgoal-conditioned robot behavior","trainingData":"Large heterogeneous robot experience with outcomes, interventions and mixed embodiments; exact full scale not reported","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Minimal variant: 38 ms/chunk on one H100, three cameras/five denoising steps; 50 Hz robot execution","trainingMethod":"Context-conditioned generalist training with experience/outcome labels; real-time action chunking","evaluation":"Broad real tasks including novel tools and precise manipulation; reported comparison to task-specific specialists"},"release":{"code":"Not released","weights":"Not released","data":"Not released","license":"Not verified","notes":"Public report; Pi API beta announced 2026-10-13. No downloadable π0.7 author weights verified."},"evidence":[{"url":"https://www.pi.website/download/pi07.pdf","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://www.pi.website/blog/pi07","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."}],"caveats":["API access is not equivalent to an open reproducible local policy release.","Report demonstrates generalization but does not isolate measured depth/tactile benefits."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not released","weights":"Not released","data":"Not released","license":"Not verified","notes":"Public report; Pi API beta announced 2026-10-13. No downloadable π0.7 author weights verified."}},"relatedIds":[]},{"id":"kinematic-intelligence","name":"Kinematic Intelligence","title":"Demonstrate once, execute on many: Kinematic intelligence for cross-robot skill transfer","date":"2026-04-15","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Stable demonstrated workspace dynamics are embedded in robot-specific joint-space structures to transfer behavior across kinematic morphologies.","institution":"EPFL","venue":"Science Robotics 2026","modalities":["Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://doi.org/10.1126/scirobotics.aea1995","project":"https://sthithpragya.com/projects/kinematic-intelligence/","code":"https://zenodo.org/records/19078877","data":"https://zenodo.org/records/19078877"},"policy":{"family":"Geometric transfer","baseModel":"Stable dynamical-system cross-robot skill transfer. Analytical positional kinematic classification, demonstrated stable dynamical system and constraint-aware joint-space controller.","modelSize":"Not reported","inputs":"One or multiple workspace motion demonstrations, robot joint state and explicit link/joint-limit/singularity/connectivity model; no image-conditioned policy is claimed.","outputs":"Robot-specific constraint-aware joint-space motion preserving the demonstrated workspace behavior.","trainingData":"One or multiple motion demonstrations; exact full experimental demonstration count not verified from accessible primary materials.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Fit stable demonstrated dynamics, then ground them in each robot’s kinematic structure without per-robot demonstration recollection.","evaluation":"Author project reports simulated/physical cross-robot transfer and an assembly-line sequence with pushing, pick-and-place and throwing."},"release":{"code":"Available","weights":"Not applicable","data":"Partial","license":"EPFL Non-Commercial License Agreement (software)","notes":"Zenodo archive explicitly restricts software to academic, non-commercial research; publisher figure rights are separate."},"evidence":[{"url":"https://doi.org/10.1126/scirobotics.aea1995","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Publisher DOI and author project consulted. Full publisher manuscript could not be retrieved; quantitative counts/compute are left unverified. Date independently checked against publisher-deposited Crossref metadata."},{"url":"https://sthithpragya.com/projects/kinematic-intelligence/","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://zenodo.org/records/19078877","label":"Official code","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://api.crossref.org/works/10.1126/scirobotics.aea1995","label":"Publisher-deposited Crossref record","fields":["date","venue"],"note":"Article publication date is April 15, 2026, distinct from the earlier software archive."}],"caveats":["Full publisher manuscript was not retrievable; details here are limited to the author’s project, original software archive and publisher-deposited Crossref metadata.","Analytical guarantees address represented positional kinematics; obstacle/self-collision/contact-force feasibility needs complementary mechanisms.","Crossref records April 15, 2026 for the article; supporting software was public March 18, 2026.","No verifiable public policy architecture figure was available from the retrieved primary sources; task photographs are not substituted."],"verified":"2026-10-17"},{"id":"vga","name":"Vision-Geometry-Action (VGA)","title":"Robotic Manipulation is Vision-to-Geometry Mapping: Vision-Geometry Backbones over Language and Video Models","date":"2026-04-14","year":2026,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Conditions actions on a native geometric backbone and trains geometry/action objectives jointly.","institution":"Sun Yat-sen University","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2604.12908","project":"https://hcplab-sysu.github.io/VisionGeometryActionModel/","code":"https://github.com/HCPLab-SYSU/VisionGeometryActionModel"},"policy":{"family":"VLA","baseModel":"VGGT + Qwen-GTE language encoding + progressive volumetric modulation/action head","modelSize":"3,609.3M total / 562.2M trainable in appendix (main text rounds trainable count to ~500M)","inputs":"Two RGB views, instruction and proprioception; depth/camera supervision used during training","outputs":"Eight-step continuous action chunks; full chunk executed before requery","trainingData":"LIBERO/RoboTwin2 training sets and real demonstrations; ~400 demos per LIBERO 10-task suite as described","trainingTime":"Longest reported training run ≤60 GPU-hours, not 60 wall-clock hours","trainingHardware":"4 A100-SXM4 80 GB GPUs, batch 32","controlRate":"Approximately 100 ms model latency (~10 queries/s); eight-step open-loop chunk","trainingMethod":"LoRA joint action/camera/depth training; geometry heads bypassed at inference","evaluation":"LIBERO, RoboTwin2, LIBERO-Plus and real viewpoint transfer"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2604.12908","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/HCPLab-SYSU/VisionGeometryActionModel","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://hcplab-sysu.github.io/VisionGeometryActionModel/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/HCPLab-SYSU/VisionGeometryActionModel/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["Measured depth is training supervision, not a deployment observation.","Ground-truth simulator camera/depth labels should not be counted as sensing modality at test time."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"starvla-alpha","name":"StarVLA-α","title":"StarVLA-$\\alpha$: Reducing Complexity in Vision-Language-Action Systems","date":"2026-04-13","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Reassesses backbone initialization, action heads, robot pretraining and action normalization under a shared VLA training protocol.","institution":"StarVLA Community","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2604.11757","code":"https://github.com/starVLA/starVLA-alpha","weights":"https://huggingface.co/StarVLA/Qwen3-VL-OFT-LIBERO-4in1"},"policy":{"family":"VLA","baseModel":"Default Qwen3-VL-4B; FAST/OFT/PI/GR00T action-head comparisons; 2B/4B/8B backbone scale study","modelSize":"Default4B VLM backbone; action head size depends on compared configuration","inputs":"RGB, task language and normalized robot state/action representations","outputs":"Discrete FAST or continuous MLP/flow action chunks; shared embodiment-padding conventions","trainingData":"Default models train directly on target benchmark demonstrations without action-specific pretraining; separate robot-pretraining ablations","trainingTime":"Maximum100k steps with per-GPU batch 16; wall-clock not reported for the final study models","trainingHardware":"8 × A100 LIBERO; 16 × A100 RoboTwin-clean; 48 × A100 clean + random; 64 × A100 joint benchmarks","controlRate":"Not reported; configuration-specific inference rather than a shared servo rate","trainingMethod":"Unified one-stage benchmark training and controlled comparisons of backbone, head, normalization and pretraining","evaluation":"LIBERO/Plus, SimplerEnv, RoboCasa-GR1, RoboTwin2, RoboChallenge Table 30 and real OOD tasks"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"MIT","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2604.11757","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/starVLA/starVLA","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."},{"url":"https://raw.githubusercontent.com/starVLA/starVLA/master/LICENSE","label":"Repository license file","fields":["release.license"],"note":"Code scope; model, data and third-party component terms can differ."}],"caveats":["RoboDojo paper explicitly names StarVLA-α; newer plain StarVLA leaderboard labels require variant qualification."],"verified":"2026-10-17"},{"id":"tax-dpd","name":"TAX-DPD","title":"Disentangled Point Diffusion for Precise Object Placement","date":"2026-04-13","year":2026,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Hierarchical point diffusion separates global placement location from local object/frame geometry for precise placement.","institution":"Carnegie Mellon University; ABB Inc.","venue":"arXiv preprint","modalities":["Depth","Point cloud"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2604.11793"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2604.11793","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."}],"caveats":["The placement predictor is modular and requires object/scene segmentation and downstream motion execution."],"verified":"2026-10-17","policy":{"family":"Diffusion","baseModel":"Dense Gaussian-mixture global placement prior and disentangled point-cloud diffusion.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Segmented scene/object point clouds and placement context.","outputs":"Object placement configuration converted to executable robot poses.","trainingMethod":"Supervised placement-goal learning with global and local geometric objectives.","trainingData":"RPDiff-style placement demonstrations and task-specific industrial insertion data; exact scope in manuscript.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Simulated multimodal placement, physical industrial insertion and simulated cloth hanging.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"robolab","name":"RoboLab-120","title":"RoboLab: A High-Fidelity Simulation Benchmark for Analysis of Task Generalist Policies","date":"2026-04-10","year":2026,"collections":["benchmark"],"categories":["general-manipulation","robustness-evaluation"],"resourceType":"benchmark","summary":"Photorealistic task-generalist evaluation with 120 tasks and controlled language, scene and observation variations.","institution":"NVIDIA","venue":"RSS 2026","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2604.09860","code":"https://github.com/NVlabs/RoboLab"},"benchmark":{"simulator":"NVIDIA Isaac Sim / Isaac Lab","simulatorVersion":"Current install extras: isaac50 = Isaac Sim 5.0 / Isaac Lab 2.2.0; isaac51 = Isaac Sim 5.1 / Isaac Lab 2.3.2.post1. Compare on matching stack.","taskCount":"120 core task definitions; separate RoboVoLo extension also available","tasks":["AnimalsInBinTask","AppleAndYogurtInBowlTask","BagelsOnPlateTask","BananaInBowlTask","BananaOnPlateTask","BananasInBinOneMoreTask","BananasInBinThreeTotalTask","BananasInCrateTask","BananasOutOfBinTask","BananaThenRubiksCubeTask","BBQSauceInBinTask","BigPumpkinInBinTask","BlackItemsInBinTask","BlocksInBinTask","BlockStackingOrderAgnosticTask","BlockStackingSpecifiedOrderTask","BowlInBinTask","BowlStackingLeftOnRightTask","BowlStackingRightOnLeftTask","ButterAboveRaisinTask","CannedFoodInBinTask","ClampInRightBinTask","CleanUpToysTask","ClearOrganicObjectsTask","ClutterPlasticTask","ClutterPumpkinTask","CoffeePotInBinTask","CondimentsInBinTask","CookingClearPlateTask","CookingPickPastaToolTask","CubesAndBlocksInBinTask","DishesInBinTask","ElectronicsInBinTask","FoodPacking1BoxesTask","FoodPacking1CansTask","FoodPacking2BoxesTask","FoodPacking2CansTask","FoodPacking3BoxesTask","FoodPacking3CansTask","FoodPackingByColorTask","FruitsGreenLimesOnPlateTask","FruitsMovingOrangeOrLimeTask","FruitsMovingTask","FruitsOnionTask","FruitsOnionToPlateTask","FruitsOnPlate3Task","FruitsOnPlateTask","FruitsOrangesOnPlateTask","GrabABagelTask","GrabAFruitTask","GreenSpoonsInPotTask","HammersInLeftBinTask","JugsOnShelfTask","KeyboardOutOfBinTask","LargerObjectRaisinBoxInBinTask","MarkerInMugTask","MouseOnKeyboardTask","MoveBananaToBagelPlateTask","MustardAboveRaisinTask","MustardInLeftBinTask","MustardInRightBinTask","NonHammerToolsInRightBinTask","OneBottleInSquarePailTask","OneBottleOnShelfTask","PhoneOrRemoteInBinTask","PickDrillTask","PickGlassesTask","PickOrangeObjectTask","PickUpBluePitcherTask","PickUpGreenObjectTask","PinkSpoonInPotTask","PlasticBottlesInSquarePailTask","PutBowlOnShelfTopTask","PutMugsOnShelfTask","PutTwoMugsOnShelfTask","RecycleCartonsOnBoxTask","RecycleCartonsVerticalCrateTask","RecycleCartonTask","RedDishesInBinTask","RedItemsInBinTask","ReorientAllMugsTask","ReorientJugTask","ReorientRedMugTask","ReorientWhiteMugsTask","RubiksCubeAndBananaTask","RubiksCubeBehindBowlTask","RubiksCubeInFrontOfBowlTask","RubiksCubeLeftOfBowlTask","RubiksCubeOrBananaTask","RubiksCubeRightOfBowlTask","RubiksCubesInBinTask","RubiksCubeTask","RubiksCubeThenBananaTask","SauceBottlesCrateTask","SmallerObjectButterInBinTask","SmallPumpkinInBinTask","SmartphoneInBinTask","SpoonInMugTask","SpoonsInPotTask","Stack3RubiksCubeTask","StackWhiteMugsTask","StackYellowOnRedTask","TakeMeasuringSpoonOutTask","TakeMugsOffOfShelfTask","TakeSpatulaOffShelfTask","ThrowAwayAppleTask","ThrowAwaySnacksTask","ToolOrganizationBothTask","ToolOrganizationTask","ToolsPickingAllHammersTask","ToolsPickingDrillTask","ToolsPickingHammerTask","ToyInBinTask","UnstackRubiksCubeTask","UtensilsInMugTask","WhiteMugInCenterOfTableTask","WhiteMugsInBinTask","WoodSpatulaToBowlTask","YellowAndWhiteObjectsInBinTask","YogurtInBowlTask"],"taskListScope":"Complete 120-task core task_table.csv snapshot; later extensions excluded.","capabilities":["Visual competency","Procedural competency","Relational competency","Instruction specificity","Controlled perturbations"],"metrics":"Binary success plus task progress / motion quality diagnostics; performance sensitivity to controlled external factors.","robots":"Default Franka/DROID-style setup; registration supports additional robots including Kinova; task definitions designed robot-agnostically.","sensors":"RGB / depth camera configuration, proprioception and language; privileged evaluator predicates. Model adapters choose actual channels.","dataCollection":"Not applicable as a common training-data collector — evaluation/task toolkit without a uniform benchmark-wide demonstration corpus.","data":"Task/scene assets and evaluation toolset; no uniform 120-task human training demonstration corpus claimed.","protocol":"Three competency axes and three difficulty levels; server-client policy interface; multi-environment evaluation; configurable language specificity and scene/observation perturbations.","baselines":"Generalist VLA / world-action policies and policy clients; see versioned paper/leaderboard for exact checkpoint scope."},"release":{"code":"Available","weights":"Not applicable","data":"Partial","license":"Apache-2.0 code; third-party assets separate","notes":"Framework, task assets and results dashboard public; model weights belong to policy providers."},"evidence":[{"url":"https://arxiv.org/abs/2604.09860","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://github.com/NVlabs/RoboLab","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://github.com/NVlabs/RoboLab/blob/main/robolab/tasks/_metadata/task_table.csv","label":"Official implementation / documentation","fields":["benchmark.tasks"],"note":"Complete 120-row task catalogue parsed from source."},{"url":"https://arxiv.org/abs/2604.09860","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Isaac Sim 5.0/5.1 contact dynamics are not invariant; recorded rollouts compare best on the original stack.","120 core tasks and RoboVoLo extension must be counted separately."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not applicable","data":"Partial","license":"Apache-2.0 code; third-party assets separate","notes":"Framework, task assets and results dashboard public; model weights belong to policy providers."}},"relatedIds":[]},{"id":"tamen","name":"TAMEn","title":"TAMEn: Tactile-Aware Manipulation Engine for Closed-Loop Data Collection in Contact-Rich Tasks","date":"2026-04-08","year":2026,"collections":["policy"],"categories":["tactile-il","tactile-foundation"],"resourceType":"policy","summary":"Feasibility-aware bimanual data collection and tactile recovery interventions refine contact-rich policies.","institution":"Fudan University / Shanghai Innovation Institute / OpenDriveLab, HKU / Shanghai Jiao Tong University and collaborators","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2604.07335","project":"https://opendrivelab.com/TAMEn"},"policy":{"family":"ACT","baseModel":"ACT with separate Res Net 18 encoders, 4-layer encoder/7-layer decoder; contrastive tactile pretraining.","modelSize":"Not reported","inputs":"Two wrist RGB views and two tactile observations.","outputs":"16 Ddual-arm joint/gripper commands.","trainingData":"Free Tac Man base>10k trajectories/50 tasks; separate task-specific bimanual demonstrations and recovery interventions.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Tactile representation pretraining, supervised bimanual imitation and DAgger-style AR recovery refinement.","evaluation":"Four real bimanual contact-rich tasks with nominal/recovery ablation."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2604.07335","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://opendrivelab.com/TAMEn","label":"Official author project","fields":["links","release"],"note":"Live artifact status reviewed separately from paper claims."}],"caveats":["Reused Free Tac Man prior and new bimanual/recovery data are distinct stages, not one uniform dataset."],"verified":"2026-10-17"},{"id":"a1","name":"A1","title":"A1: A Fully Transparent Open-Source, Adaptive and Efficient Truncated Vision-Language-Action Model","date":"2026-04-07","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Uses adaptive backbone early exit and truncated flow inference to reduce the cost of a multi-robot manipulation policy.","institution":"Sun Yat-sen University / MBZUAI / Spatialtemporal AI","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2604.05672","code":"https://github.com/ATeam-Research/A1","weights":"https://huggingface.co/spatialtemporal-ai/a1-pretrain"},"policy":{"family":"VLA","baseModel":"Molmo-7B VLM (Qwen2-7B language backbone); Qwen3-derived flow expert or MLP action head","modelSize":"7B VLM backbone and approximately 400M flow expert; aggregate including all components not reported","inputs":"RGB views, language and robot state; state masking is a training augmentation","outputs":"Action chunks; computational analysis uses 8 steps × 7 action dimensions; embodiment-specific adapters","trainingData":"DROID, AgiBot, RoboCOIN, RoboMIND and other public robot data plus 15,951 in-house trajectories","trainingTime":"Pretraining 200k steps, global batch 1,024; downstream LIBERO/VLABench 50k; wall-clock not reported","trainingHardware":"Not reported","controlRate":"Adaptive early-exit latency varies with layer/exit threshold; a single fixed deployment frequency is not reported","trainingMethod":"Multi-robot pretraining; flow-matching or MLP actions; offline-calibrated layer-consistency early exit","evaluation":"LIBERO, VLABench, RoboChallenge Table 30 and real UR5/Franka/AgiBot/OpenArm manipulation"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Not reported","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2604.05672","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/ATeam-Research/A1","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."}],"caveats":["A1 is distinct from InternVLA-A1; backbone and flow-head sizes are separate scopes."],"verified":"2026-10-17"},{"id":"starvla","name":"StarVLA codebase","title":"StarVLA: A Lego-like Codebase for Vision-Language-Action Model Developing","date":"2026-04-06","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Provides composable VLA backbones, action heads, data loaders and training recipes, including executable FAST/OFT/PI/GR00T variants.","institution":"StarVLA Community","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2604.05014","code":"https://github.com/starVLA/starVLA","weights":"https://huggingface.co/StarVLA"},"policy":{"family":"VLA","baseModel":"Configurable Qwen2.5-VL/Qwen3-VL and other backbones; FAST, OFT, PI, GR00T-style heads","modelSize":"Default efficiency study uses Qwen3-VL-4B; aggregate depends on selected head and backbone","inputs":"RGB views, language and optional robot state under a unified input contract","outputs":"Autoregressive action tokens, direct regression or flow/diffusion chunks depending on selected policy","trainingData":"Benchmark-specific and joint heterogeneous robot datasets; no single universal pretraining corpus","trainingTime":"GR00T/Qwen3-VL-4B throughput study: 100k steps takes19h32m–66h47m on 8 × A100 depending on batch","trainingHardware":"Single-node8 × A10080 GB; multi-node efficiency study up to 256 × A100","controlRate":"Not a single model/control rate; training throughput is not policy feedback frequency","trainingMethod":"Modular imitation-learning codebase supporting specialist and joint generalist training","evaluation":"LIBERO, SimplerEnv, RoboCasa-GR1, RoboTwin2 and real-robot evaluation recipes"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"MIT","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2604.05014","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/starVLA/starVLA","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."},{"url":"https://raw.githubusercontent.com/starVLA/starVLA/master/LICENSE","label":"Repository license file","fields":["release.license"],"note":"Code scope; model, data and third-party component terms can differ."}],"caveats":["This is the codebase paper2604.05014; StarVLA-α2604.11757 is a separate controlled modeling study.","StarVLA-OFT/GR00T/PI_v3 leaderboard names denote configurations, not four independent papers."],"verified":"2026-10-17"},{"id":"spatialvam","name":"SpatialVAM","title":"SpatialVAM: Spatial-Aware Multi-View Video Diffusion as a Data-Efficient Robot Policy","date":"2026-04-03","year":2026,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Predicts RGB videos and end-effector heatmap videos from depth-derived virtual views, then decodes continuous manipulation actions.","institution":"CASIA / UCAS / FiveAges / Tsinghua University","venue":"NeurIPS2026 (author project)","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2604.03181","project":"https://spatialvam.github.io/home_page.html"},"policy":{"family":"World-action model","baseModel":"Wan2.2 5B video diffusion with view attention; separate rotation/gripper predictor","modelSize":"5B video model plus 170M rotation/gripper predictor; total includingVAE not separately reported","inputs":"Colored RGB-D point clouds, current end-effector pose, camera calibration and task text conditioning; three orthographic RGB/pose-heatmap views","outputs":"24-step end-effector action chunks and futureRGB/heatmap videos","trainingData":"Meta-World: 5 demos/task across 7 tasks; RoboCasa: 10/task across 10 tasks; real: ≈10/task for 3 base tasks","trainingTime":"Meta-World21.8ksteps: video42h, decoder36h; real 8.6ksteps: video11h, decoder10h; stages reported separately","trainingHardware":"Meta-World32H20; real 32H200; inferenceA100 with>30GBVRAM","controlRate":"AppendixC: 4.6s per24-action chunk onA100; Section4.4 separately states5 Hz; closed-loop interpretation unresolved","trainingMethod":"LoRA video/heatmap denoising plus separately supervised rotation/gripper decoder; heatmap back-projection","evaluation":"Seven Meta-World tasks, ten RoboCasa tasks; Franka Research 3 pick/place, Push-T, scooping and 4 unseen settings"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Inspected author page has placeholder Code/HuggingFace buttons without download URLs; no runnable release verified."},"evidence":[{"url":"https://arxiv.org/abs/2604.03181","label":"Canonical identity and first-submission date","fields":["title","date"],"note":"First submission2026-04-03; details usev2 updated2026-08-13."},{"url":"https://arxiv.org/html/2604.03181v2","label":"Full paper:Sections3–4 andAppendixC","fields":["policy","modalities","environments","embodiments"],"note":"PhysicalZED2iRGB-D input is distinct from inferred monocular geometry; model rates contain the noted unresolved scope inconsistency."},{"url":"https://spatialvam.github.io/home_page.html","label":"Author project and release buttons","fields":["venue","release","imageCandidates","summary"],"note":"Architecture image isFig2; Code/HuggingFace buttons are placeholders onverification date."},{"url":"https://spatialvam.github.io/static/images/Method.png","label":"Independent architecture-figure review:Fig2 text-conditioning path","fields":["policy.inputs","modalities"],"note":"Text-conditioning nodes are explicit in both the diffusion pipeline and Transformer diagram."}],"caveats":["The 5 Hz sentence does not reconcile with 4.6s measured fullchunk latency; do not label this as verified 5 Hz closed-loop feedback.","No additional robot pretraining means adaptation starts from a pretrainedWan2.2video model; it does not mean training from scratch.","RGB-D geometry is measured from calibrated stereo cameras; no tactile input reported."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Inspected author page has placeholder Code/HuggingFace buttons without download URLs; no runnable release verified."}},"relatedIds":[]},{"id":"vitatouch","name":"VitaTouch / VitaSet","title":"VitaTouch: Property-Aware Vision-Tactile-Language Model for Robotic Quality Inspection in Manufacturing","date":"2026-04-02","year":2026,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Combines visual and tactile prefix tokens for material-property reasoning and defect recognition, integrated into an industrial inspection/sorting demonstration.","institution":"Beijing University of Posts and Telecommunications; Beijing Institute of Technology; Zhongguancun Academy","venue":"Preprint","modalities":["Tactile","RGB","Language"],"environments":["Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2604.03322","project":"https://vitatouch.github.io/","data":"https://huggingface.co/datasets/Bupt-Joy/VitaSet"},"model":{"role":"Reusable representation or perception model; downstream controllers are separate systems","family":"Tactile-language model","baseModel":"Modality-specific encoders and dual Q-Formers with frozen Vicuna 7B; LoRA for defect adaptation","modelSize":"Vicuna 7B language base; encoders and Q-Formers are additional","inputs":"RGB view, GelSight tactile observation and language prompt","outputs":"Material/hardness/roughness descriptions and defect labels; separate robot workflow uses the recognition result","trainingMethod":"Cross-modal contrastive and pair-matching alignment, property-reasoning training, then LoRA adaptation with few-shot defect labels","trainingData":"VitaSet: 186 objects, 52K images and 5.1K human-verified instruction–answer pairs. The TVL comparison follows the original TVL split.","trainingTime":"Stage 1: approximately 2 hours; stage 2: approximately 10 hours; stage 3: within one hour","trainingHardware":"Stages 1/2: 8 NVIDIA A800 GPUs; stage 3: 1 NVIDIA RTX 5090","inference":"Not reported","evaluation":"TVL/HCT/SSVTP description benchmark, property classification, defect recognition and 100 laboratory robotic sorting trials."},"release":{"code":"Not verified — no official implementation link found on inspected project","weights":"Not verified — no official model checkpoint linked on inspected project","data":"Available — official resource link","license":"VitaSet dataset LICENSE is MIT; code/model weights are not verified and Vicuna/base-model terms apply separately.","notes":"Code, pretrained encoders and downstream task data are separate artifacts; no policy checkpoint is implied by an encoder release."},"evidence":[{"url":"https://arxiv.org/abs/2604.03322","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Method, experiments and implementation sections; specifications retain component and evaluation scope."},{"url":"https://vitatouch.github.io/","label":"Official project","fields":["links","release"],"note":"Author project; author-reported release scope is distinguished from an executed reproduction."},{"url":"https://huggingface.co/datasets/Bupt-Joy/VitaSet/resolve/main/LICENSE","label":"Actual VitaSet dataset MIT license","fields":["release.license"],"note":"Dataset license checked independently of code and model status."}],"caveats":["The model produces descriptions and defect decisions. The 100 physical trials evaluate an integrated sorting workflow, rather than a learned continuous-action policy.","VitaSet image and QA totals differ; 52K images are not 52K independent robot demonstrations."],"relatedIds":["tvl","octopi"],"verified":"2026-10-17"},{"id":"vtam","name":"VTAM","title":"VTAM: Video-Tactile-Action Models for Complex Physical Interaction Beyond VLAs","date":"2026-03-24","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"A video-action backbone receives tactile streams with attention regularization for force-sensitive manipulation.","institution":"University of Illinois Urbana-Champaign / Stanford University / Shanghai Jiao Tong University","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2603.23481"},"policy":{"family":"World-action model","baseModel":"Genie Envisioner GE-base / LTX-Video transformer with 28 layers and parallel action expert.","modelSize":"Not reported","inputs":"Multi-view RGB, GelSight Mini tactile images, language/task conditioning and robot state.","outputs":"54-step continuous action chunks coupled to predicted video/tactile dynamics.","trainingData":"266 real demonstrations: 100 chip pick-and-place, 105 whiteboard wiping, 61 cucumber peeling.","trainingTime":"50k video-backbone +20k action-head updates; appendix reports~3.4s per optimization step. Total wall-clock time Not reported.","trainingHardware":"4 NVIDIA A100 GPUs, 40 GB each; Deep Speed ZeRO-2 / bf 16.","controlRate":"30 Hz collection and execution.","trainingMethod":"50 k video-backbone fine-tuning steps followed by 20 k action-head steps; tactile regularization balances visual/tactile attention.","evaluation":"xArm 6 / parallel gripper; fragile chip transport, cucumber peeling and whiteboard wiping."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2603.23481","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."}],"caveats":["RealSense D455 cameras supply RGB streams; no runtime measured-depth input is established."],"verified":"2026-10-17"},{"id":"cap-x","name":"CaP-X / CaP-Agent0","title":"CaP-X: A Framework for Benchmarking and Improving Coding Agents for Robot Manipulation","date":"2026-03-23","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Provides robot-programming interfaces and evaluates coding agents across abstraction and perception-grounding tiers; CaP-Agent0 and trained CaP-RL variants are distinct policies.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Depth","Force/Torque","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2603.22435","project":"https://capgym.github.io"},"policy":{"family":"Hybrid","baseModel":"Twelve coding-model backbones in the benchmark, including Gemini 3 Pro and GPT / Claude-family agents; CaP-Agent0 is training-free and CaP-RL adds training.","modelSize":"Not reported","inputs":"Task, Python robot APIs and tier-specific observations. The physical API can return RGB / depth / intrinsics, wrist RGB, end-effector force wrenches and joint configuration.","outputs":"Executable Python programs invoking perception, geometry, inverse-kinematics and motion APIs.","trainingData":"CaP-Agent0 uses prompts and interaction rather than weight training; CaP-RL has separately reported training data / objectives.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Training-free coding-agent control and reinforcement-trained coding variants are evaluated under explicitly different interface tiers.","evaluation":"CaP-Bench: seven principal manipulation tasks and twelve model backbones; additional LIBERO-Pro, Robosuite, BEHAVIOR and physical studies."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2603.22435","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["Observation access ranges from privileged state to visual grounding; tiers are not interchangeable.","The API exposing a depth image or force wrench does not mean every benchmark tier supplies those signals."],"verified":"2026-10-17"},{"id":"omnivta","name":"OmniVTA","title":"OmniVTA: Visuo-Tactile World Modeling for Contact-Rich Robotic Manipulation","date":"2026-03-19","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Combines tactile representation learning, short-horizon visuotactile world modeling and a reflexive closed-loop controller.","institution":"Tsinghua University / TARS","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2603.19201","project":"https://mrsecant.github.io/OmniVTA/","code":"https://github.com/MrSecant/OmniVTA","data":"https://huggingface.co/datasets/tars-robotics/OmniVitac"},"policy":{"family":"World-action model","baseModel":"TactileVAE, two-stream world model, adaptive fusion policy and RLTC controller","modelSize":"600.52M total: VAE1.96M + world model271.57M + policy326.89M + RLTC0.10M","inputs":"RGB, tactile observations and robot proprioception","outputs":"Manipulation actions and future contact dynamics; reflexive corrections","trainingData":"OmniViTac: 21,879 trajectories, 86 tasks, 126 objects; VAE trains on ≈1.2M tactile samples","trainingTime":"VAE50 epochs; world model100k updates; wall-clock not reported","trainingHardware":"VAE: 8 A100 GPUs; other training hardware not reported","controlRate":"Reflexive controller60 Hz; policy/world-model rate separate","trainingMethod":"Self-supervised tactile encoding, predictive dynamics and contact-aware action/reflex learning","evaluation":"Six interaction categories, unseen objects/geometries and external disturbances"},"release":{"code":"Not released — repository contains license and website material only","weights":"Not verified","data":"Available","license":"MIT for currently published repository material, verified from LICENSE; no policy implementation release was found, and data/model terms remain separate.","notes":"The current main branch contains only LICENSE; the webpage branch contains project-site material and there are no GitHub release assets. This does not establish the advertised learning/hardware implementation. The separately linked OmniVitac dataset remains independently available; code, data and weights are distinct."},"evidence":[{"url":"https://arxiv.org/abs/2603.19201","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/MrSecant/OmniVTA","label":"Official repository","fields":["links"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://arxiv.org/html/2603.19201","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."},{"url":"https://github.com/MrSecant/OmniVTA/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."},{"url":"https://github.com/MrSecant/OmniVTA","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/datasets/tars-robotics/OmniVitac","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["Not a language-conditioned VLA despite its similar name to OmniVTLA.","World-model, policy and reflex-controller components have distinct update rates."],"verified":"2026-10-19","releaseByCollection":{"policy":{"code":"Not released — repository contains license and website material only","weights":"Not verified","data":"Available","license":"MIT for currently published repository material, verified from LICENSE; no policy implementation release was found, and data/model terms remain separate.","notes":"The current main branch contains only LICENSE; the webpage branch contains project-site material and there are no GitHub release assets. This does not establish the advertised learning/hardware implementation. The separately linked OmniVitac dataset remains independently available; code, data and weights are distinct."}},"relatedIds":[]},{"id":"rlt","name":"RL Token (RLT)","title":"RL Token: Bootstrapping Online RL with Vision-Language-Action Models","date":"2026-03-19","year":2026,"collections":["policy"],"categories":["visual-precision-rl","agent-rsi-icl"],"resourceType":"policy","summary":"Compresses a frozen VLA representation into an RL token and trains a small actor-critic to improve the critical precision stage.","institution":"Physical Intelligence","venue":"Technical report","modalities":["RGB","Proprioception"],"environments":["Real world"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://www.pi.website/download/rlt.pdf","project":"https://www.pi.website/research/rlt"},"policy":{"family":"RL","baseModel":"π0.6 VLA + learned encoder-decoder token bottleneck + small MLP actor/critic","modelSize":"Base: Gemma 4B + SigLIP 400M + action expert 860M; RL head is 2 × 256 or 3 × 512 MLP, total not separately reported","inputs":"RGB, language and robot state through frozen VLA; RL token and VLA reference action to actor","outputs":"10-step, 14-dimensional action chunks edited by RL actor","trainingData":"Base VLA training plus task demonstrations/interventions and minutes-to-hours of real experience","trainingTime":"Minutes to a few hours of real robot online practice; excludes base-VLA training","trainingHardware":"Not reported","controlRate":"50 Hz robot execution; ten-step chunks imply a separate VLA/RL query cadence","trainingMethod":"Bottleneck adaptation followed by off-policy RL, reference-action regularization/dropout and optional human corrections","evaluation":"Four precision stages: M3 screw alignment/driving, zip-tie fastening, Ethernet insertion and power-cord insertion"},"release":{"code":"Not released","weights":"Not released","data":"Not released","license":"Not verified","notes":"Official research page and report are public; original RLT training artifacts not verified. Third-party implementations are not the author release."},"evidence":[{"url":"https://www.pi.website/download/rlt.pdf","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://www.pi.website/research/rlt","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."}],"caveats":["Stage-specific fine-tuning rather than full-task generalist RL.","No measured depth or tactile input in the reported system."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not released","weights":"Not released","data":"Not released","license":"Not verified","notes":"Official research page and report are public; original RLT training artifacts not verified. Third-party implementations are not the author release."}},"relatedIds":[]},{"id":"vitac-tracing","name":"ViTac-Tracing","title":"ViTac-Tracing: Visual-Tactile Imitation Learning of Deformable Object Tracing","date":"2026-03-19","year":2026,"collections":["policy"],"categories":["tactile-il","tactile-foundation"],"resourceType":"policy","summary":"A unified visual-tactile imitation policy traces both linear and planar deformable objects.","institution":"King's College London / Imperial College London","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2603.18784"},"policy":{"family":"ACT","baseModel":"Action-chunking policy with separate ResNet 18 vision/tactile encoders, local center loss and global task-progress loss.","modelSize":"Not reported","inputs":"RGB, tactile images and robot kinematic/gripper states.","outputs":"60-step joint-space or Cartesian end-effector action chunks.","trainingData":"100 demos:25 each for flat shoelace, braided cable, face towel and microfiber cloth.","trainingTime":"15,000 epochs; wall-clock Not reported.","trainingHardware":"NVIDIA RTX4090workstation for training.","controlRate":"30 Hz data recording; inference frequency Not reported.","trainingMethod":"Behavior cloning with contact-center, KL and task-progress losses; checkpoint chosen by validation loss.","evaluation":"Real tracing across four training objects and two unseen objects."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2603.18784","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."}],"caveats":["Do not conflate this gripper tracing method with tactile-only dexterous Touch 2 Trace."],"verified":"2026-10-17"},{"id":"gigaworld-policy","name":"GigaWorld-Policy","title":"GigaWorld-Policy: An Efficient Action-Centered World--Action Model","date":"2026-03-18","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Action-centered WAM learns from visual dynamics but uses a causal action-only decoding path at deployment.","institution":"GigaAI","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2603.17240","project":"https://gigaai-research.github.io/GigaWorld-Policy/","code":"https://github.com/open-gigaai/giga-world-policy"},"policy":{"family":"World-action model","baseModel":"Wan2.2-5B diffusion Transformer with action-centered causal masking.","modelSize":"5B backbone; complete policy parameter count not reported.","inputs":"Multi-view RGB, robot state and language.","outputs":"48-action chunks; future observations supervised every 12 steps. Future-video decoding is optional.","trainingData":"Approximately 10,000 hours of robot and egocentric videos, including AgiBot, RDT, RoboMIND, ATARA, EgoDex and Ego4D.","trainingTime":"6,000 GPU-hours for pretraining; elapsed duration and GPU model not disclosed in that cost statement.","trainingHardware":"Pretraining GPU model not reported; inference benchmark uses one A100.","controlRate":"Action-only decoding latency measured on A100; execution frequency depends on the chunk schedule.","trainingMethod":"Action prediction plus action-conditioned future-video generation; causal masking blocks future-video influence on action tokens.","evaluation":"RoboTwin 2.0 and physical multi-step manipulation. Real evaluation uses 20 trials per task and permits up to five attempts per trial."},"release":{"code":"Partial","weights":"Not verified","data":"Not verified","license":"Not verified; pretrained backbones and data retain separate terms","notes":"Current author repository centers GigaWorld-Policy-0.5; original checkpoint/version availability remains unresolved."},"evidence":[{"url":"https://arxiv.org/abs/2603.17240","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/open-gigaai/giga-world-policy","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://gigaai-research.github.io/GigaWorld-Policy/","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."}],"caveats":["Real trials permit up to five attempts, unlike one-attempt evaluation.","The current main repository and linked model center the separately cataloged 0.5 version."],"verified":"2026-10-17"},{"id":"fast-wam","name":"Fast-WAM","title":"Fast-WAM: Do World Action Models Need Test-time Future Imagination?","date":"2026-03-17","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Uses future-video prediction as training supervision while allowing action-only execution without test-time future video synthesis.","institution":"Tsinghua University / Galaxea AI","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2603.16666","project":"https://yuantianyuan01.github.io/FastWAM/","code":"https://github.com/yuantianyuan01/FastWAM","data":"https://huggingface.co/datasets/yuanty/robotwin2.0-fastwam","weights":"https://huggingface.co/yuanty/fastwam"},"policy":{"family":"World-action model","baseModel":"Wan2.2-5B video DiT, 1B action expert and structured asymmetric attention.","modelSize":"6B total: 5B video backbone plus 1B action expert.","inputs":"Multi-view RGB, proprioception and language.","outputs":"32-action horizon; multiple camera views are concatenated. Controlled joint-video and action-only variants are evaluated.","trainingData":"RoboTwin: 2,500 clean plus 25,000 randomized demonstrations. Physical Galaxea R1 Lite towel-folding data: 60 hours.","trainingTime":"30k steps for RoboTwin and physical towel-folding runs; wall-clock duration not reported.","trainingHardware":"Training hardware not reported; latency measured on one RTX 5090D V2 with 32 GB.","controlRate":"Approximately 190 ms action-only latency in the reported comparison; chunk prediction and low-level execution rates are distinct.","trainingMethod":"Flow matching with causal masking that prevents action tokens from reading future-video targets; ten inference denoising steps.","evaluation":"LIBERO, RoboTwin 2.0 and real towel folding; success and completion time."},"release":{"code":"Available","weights":"Available","data":"Available","license":"MIT code; weights/data/backbone terms separate","notes":"Availability concerns author-linked artifacts; no training or installation reproduced."},"evidence":[{"url":"https://arxiv.org/abs/2603.16666","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/yuantianyuan01/FastWAM","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://yuantianyuan01.github.io/FastWAM/","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."}],"caveats":["Several attention/inference variants are compared; action-only deployment does not generate future video."],"verified":"2026-10-17"},{"id":"kinema4d","name":"Kinema4D","title":"Kinema4D: Kinematic 4D World Modeling for Spatiotemporal Embodied Simulation","date":"2026-03-17","year":2026,"collections":["policy"],"categories":["geometry-models","rgbd-3d"],"resourceType":"model","summary":"A neural simulator combines exact robot kinematics with generative 4D environmental reactions.","institution":"S-Lab, Nanyang Technological University; CUHK Shenzhen","venue":"arXiv preprint","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2603.16669","project":"https://mutianxu.github.io/Kinema4D-project-page/","code":"https://github.com/mutianxu/Kinema4D","weights":"https://huggingface.co/Minoday/Kinema4D","data":"https://huggingface.co/datasets/Minoday/Robo4D-200k"},"release":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 repository, model and dataset cards; upstream pretrained backbones retain separate terms","notes":"Official repository includes training, inference and preprocessing. Kinema4D checkpoint files and Robo4D-200k dataset shards were verified independently."},"evidence":[{"url":"https://arxiv.org/abs/2603.16669","label":"Original manuscript and first submission history","fields":["title","date","summary","model","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://github.com/mutianxu/Kinema4D","label":"Official implementation, README and file/license inspection on 19 October 2026","fields":["links","release"],"note":"Official repository includes training, inference and preprocessing. Kinema4D checkpoint files and Robo4D-200k dataset shards were verified independently. Apache-2.0 repository, model and dataset cards; upstream pretrained backbones retain separate terms."},{"url":"https://huggingface.co/Minoday/Kinema4D","label":"Official released artifact inventory and model/dataset card","fields":["release.weights","links"],"note":"File inventory and artifact-specific terms checked independently; no upstream artifact is counted as this work’s trained release."},{"url":"https://huggingface.co/datasets/Minoday/Robo4D-200k","label":"Official released artifact inventory and model/dataset card","fields":["release.data","links"],"note":"File inventory and artifact-specific terms checked independently; no upstream artifact is counted as this work’s trained release."}],"caveats":["A separate Diffusion Policy supplies actions; Kinema4D predicts outcomes rather than selecting actions.","Real OOD predicted success remains higher than observed robot success, limiting simulator calibration."],"verified":"2026-10-19","model":{"role":"Kinematically controlled 4D neural simulator","family":"Predictive world model","baseModel":"URDF-driven robot pointmap control and Wan2.1/4DNeX-derived RGB/pointmap generative backbone.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Robot kinematics, candidate actions, reconstructed robot geometry and initial scene.","outputs":"Synchronized future RGB and pointmap sequences.","trainingMethod":"Action-conditioned generative world modeling on 4D-annotated simulation/robot interaction episodes.","trainingData":"Robo4D-200K: 201,426 episodes; 2% held out for validation.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Generated reaction accuracy and policy-evaluation comparisons for three simulation/three real scenarios.","inference":"Not reported in the inspected primary-source sections"}},{"id":"forcevla2","name":"ForceVLA2","title":"ForceVLA2: Unleashing Hybrid Force-Position Control with Force Awareness for Contact-Rich Manipulation","date":"2026-03-16","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Force prompts and a cross-scale mixture-of-experts action expert perform closed-loop hybrid force-position control.","institution":"Shanghai AI Laboratory / Shanghai Jiao Tong University and collaborators","venue":"Preprint","modalities":["RGB","Force/Torque","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2603.15169","project":"https://sites.google.com/view/force-vla2/home"},"policy":{"family":"VLA","baseModel":"π0 / PaliGemma-SigLIP vision-language expert; flow-matching action expert with cross-scale MoE.","modelSize":"Not reported","inputs":"Multi-view RGB, task and subtask-force prompts, end-effector pose and six-axis force/torque.","outputs":"Hybrid force-position action chunks and subtask-progress predictions.","trainingData":"1,000 demonstrations over five tasks: press bottle, clean vase, clean board, retrieve plate, assemble gears.","trainingTime":"30,000 steps, approximately 10 h (appendix); model-name inconsistency retained.","trainingHardware":"8 NVIDIA A100 GPUs (appendix calls the trained model Foca-VLA).","controlRate":"Appendix reports 15 Hz inference on RTX4090with 30-action chunks; force sensing 300 Hz.","trainingMethod":"Supervised force-prompt segmentation and flow-matching hybrid control learning.","evaluation":"Flexiv Rizon 4 s / DH AG-95 on five real contact-rich tasks."},"release":{"code":"Not released","weights":"Not verified","data":"Not released","license":"Not verified","notes":"Official project says code and dataset coming soon; generic GitHub-home link is not a release."},"evidence":[{"url":"https://arxiv.org/abs/2603.15169","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://sites.google.com/view/force-vla2/home","label":"Official author project","fields":["links","release"],"note":"Live artifact status reviewed separately from paper claims."}],"caveats":["Wrist/end-effector six-axis F/T is not skin tactile. RGB-D camera hardware does not establish runtime depth input.","Appendix training paragraph uses Foca-VLA while the manuscript title/method use ForceVLA2; reported cost is explicitly qualified."],"verified":"2026-10-17"},{"id":"hapticvla","name":"HapticVLA","title":"HapticVLA: Contact-Rich Manipulation via Vision-Language-Action Model without Inference-Time Tactile Sensing","date":"2026-03-16","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Tactile reward-weighted training and distillation produce a deployable VLA that does not require tactile sensors at inference.","institution":"Skolkovo Institute of Science and Technology","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2603.15257","project":"https://advanced-robotic-manipulation.github.io/websites/websites/hapticvla/"},"policy":{"family":"VLA","baseModel":"SmolVLA flow-matching teacher/student; safety-aware reward-weighted flow matching and offline tactile distillation.","modelSize":"Not reported","inputs":"Deployed student: RGB, language and proprioception. Tactile feedback is used by teacher/reward construction during training only.","outputs":"Robot action chunks for contact-sensitive manipulation.","trainingData":"310 real episodes: each of two single-arm tasks 70 successful +20 faulty; bimanual task 100 successful +30 faulty.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Safety-aware reward-weighted flow matching; offline teacher action targets blended with demonstration targets; student removes tactile encoder.","evaluation":"Three real tasks, including fragile/deformable-object pick-and-place and bimanual manipulation."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2603.15257","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://advanced-robotic-manipulation.github.io/websites/websites/hapticvla/","label":"Official author project","fields":["links","release"],"note":"Live project artifact links inspected on2026-10-17."}],"caveats":["Catalog deployment modalities exclude tactile: full teacher tactile states and reward labels are unavailable to the final student at runtime."],"verified":"2026-10-17"},{"id":"roco-aaai-2026","name":"RoCo AAAI 2026","title":"RoCo Challenge at AAAI 2026: Benchmarking Robotic Collaborative Manipulation for Assembly Towards Industrial Automation","date":"2026-03-16","year":2026,"collections":["benchmark"],"categories":["precision-assembly"],"resourceType":"benchmark","summary":"Gearbox assembly challenge with from-scratch, partial-state continuation and error-recovery scenarios, followed by physical robot competition.","institution":"Nanyang Technological University / A*STAR","venue":"AAAI 2026 challenge / technical report","modalities":["RGB","Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2603.15469","project":"https://rocochallenge.github.io/RoCo-AAAI2026/","code":"https://github.com/rocochallenge/gearboxAssembly"},"benchmark":{"simulator":"NVIDIA Isaac Sim","simulatorVersion":"Current public README: Isaac Sim 5.1.0 / Isaac Lab 2.3.0 (submodule tag v2.3.0); paper original exact stack Not reported.","taskCount":"3 core scenario families around one planetary gearbox assembly workflow","tasks":["Assembly from Scratch","Resume from Partial State","Error Detection and Recovery"],"taskListScope":"Complete three scenario-family definitions; component-level stages are not three separate product families.","capabilities":["Bimanual precision assembly","State recognition","Recovery from partial assembly","Human-like error correction"],"metrics":"Stage/component completion and final assembly score under simulation/onsite rules.","robots":"Galaxea R1 Lite physical platform; simulated bimanual assembly setup.","sensors":"RGB-D / robot state in simulation; physical sensor configuration defined by competition APIs. No optical tactile input established.","dataCollection":"Simulation: automatically generated expert trajectories and released generation scripts. Physical track: robot teleoperation using the R1 Lite interface.","data":"Simulation successful-attempt trajectories and about 300 hardware demonstrations described by report; exact split depends on stage.","protocol":"Online qualification then onsite physical evaluation; varied initial states encode collaboration and recovery; common assembly plan and robot API.","baselines":"ACT / diffusion / VLA-style policy baselines and challenge systems; exact table scope in report."},"release":{"code":"Available","weights":"Not verified","data":"Partial","license":"Not verified","notes":"Official simulator code, task docs, onsite API references and leaderboards public."},"evidence":[{"url":"https://arxiv.org/abs/2603.15469","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://rocochallenge.github.io/RoCo-AAAI2026/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/rocochallenge/gearboxAssembly","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://arxiv.org/abs/2603.15469","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["The later IROS 2026 challenge is a separate edition; this entry covers AAAI 2026.","Recovery scenarios share a gearbox task family rather than a broad general-purpose object set."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not verified","data":"Partial","license":"Not verified","notes":"Official simulator code, task docs, onsite API references and leaderboards public."}},"relatedIds":[]},{"id":"r3dp","name":"R3DP","title":"R3DP: Real-Time 3D-Aware Policy for Embodied Manipulation","date":"2026-03-15","year":2026,"collections":["policy"],"categories":["rgbd-3d","visual-precision-rl"],"resourceType":"policy","summary":"An asynchronous VGGT/TFPNet encoder injects predicted 3D features into an RGB diffusion policy while limiting heavy geometry inference to sparse keyframes.","institution":"Shanghai Jiao Tong University / D-Robotics / collaborating institutions","venue":"ECCV 2026 Oral","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2603.14498","project":"https://dazazh.github.io/r3dp-project-page/","code":"https://github.com/dazazh/R3DP","weights":"https://huggingface.co/wxdong/R3DP"},"policy":{"family":"Diffusion","baseModel":"Diffusion Policy + frozen VGGT + task-specific distilled Temporal Feature Prediction Network (TFPNet), with a projective multi-view feature fuser.","modelSize":"Not reported","inputs":"Multi-view RGB frames, camera intrinsics/extrinsics and robot state/history. Geometry is predicted from RGB; the method does not require a deployed depth sensor.","outputs":"Continuous action chunks; released policy-training table uses horizon 8, 1 observation and 8 executed actions. Embodiment-specific action space.","trainingData":"Simulation: 100 trajectories per task; TFPNet derives approximately 200K four-frame sequences per task from those same trajectories, not extra independent demonstrations. Real dual-arm100 demos/task, single-arm50 demos/task.","trainingTime":"Wall-clock duration not reported. TFPNet100 epochs; R3DP policy600 epochs (Appendix D, TablesF/G).","trainingHardware":"TFPNet: 8 × NVIDIA A800 (Appendix Table F). R3DP policy: main Implementation Details states 4 × RTX4090, whereas Appendix Table G lists 8 × RTX4090; the paper contains this internal discrepancy. Real inference profiling uses one RTX4090.","controlRate":"Real profiling separates62.18 ms observation encoding and 69.78 ms action expert on RTX4090. Scheduling parameterτ controls VGGT refresh; these component timings are not a guaranteed closed-loop servo rate.","trainingMethod":"First distill TFPNet from frozen VGGT features; then freeze both geometry modules and train the action policy/MVFF with imitation learning. Heavy VGGT keyframes alternate with fast predicted features.","evaluation":"RoboTwin bimanual simulated tasks; 4 real task settings with both dual-arm and single-arm manipulation. Task-specific success and inference latency are reported."},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Repository README claims MIT but root LICENSE was not found; code license unresolved. Released checkpoints include VGGT parameters and are explicitly noncommercial-research-only under VGGT terms.","notes":"Official code and R3DP/TFPNet checkpoints available; pretrained VGGT and DINOv2 dependencies retain their terms."},"evidence":[{"url":"https://arxiv.org/abs/2603.14498","label":"Paper and first-public arXiv record","fields":["title","date","institution","policy","modalities","embodiments","environments"],"note":"Technical fields follow the inspected full manuscript and appendices; date is arXiv v1, not the current revision."},{"url":"https://dazazh.github.io/r3dp-project-page/","label":"Official project","fields":["summary","imageCandidates","release"],"note":"Current author project and release links."},{"url":"https://github.com/dazazh/R3DP","label":"Official repository","fields":["release","policy.baseModel","policy.inputs","policy.evaluation","imageCandidates"],"note":"Current implementation/release scope can differ from paper experiments."},{"url":"https://huggingface.co/wxdong/R3DP","label":"Official checkpoint card and noncommercial license notice","fields":["release.weights","release.license"],"note":"Card states the full checkpoints include VGGT parameters and inherit its noncommercial research terms."}],"caveats":["RGB-inferred3D is distinct from measured depth.","TFPNet sequence counts reuse the imitation demonstrations and must not be added to trajectory counts.","README code-license claim and missing root LICENSE remain qualified; checkpoint terms are separately disclosed.","Policy-training accelerator count differs between the main text (4 × RTX4090) and Appendix Table G (8 × RTX4090); retain this discrepancy when estimating reproduction cost."],"verified":"2026-10-17"},{"id":"tacfilm","name":"TacFiLM","title":"Tactile Modality Fusion for Vision-Language-Action Models","date":"2026-03-15","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Modulates VLA visual features with pretrained tactile embeddings using FiLM, comparing multiple fusion schemes.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2603.14604"},"policy":{"family":"VLA","baseModel":"OpenVLA-OFT; fused DINOv2/SigLIP, Llama2-7B; T3 or Sparsh tactile encoders","modelSize":"7B language-model component; total not reported","inputs":"RGB, DIGIT tactile images, language and proprioception","outputs":"OpenVLA-OFT action chunks","trainingData":"80 real demonstrations per task, ≈70 steps each","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Data10 Hz; low-level Franka interface1 kHz; policy query rate not reported","trainingMethod":"Pretrained tactile FiLM conditioning versus concatenation/cross-attention","evaluation":"Real peg insertion variants and drawer opening"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."},"evidence":[{"url":"https://arxiv.org/abs/2603.14604","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://arxiv.org/html/2603.14604","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["A 1 kHz low-level controller does not establish high-rate tactile policy feedback."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."}},"relatedIds":[]},{"id":"transdex","name":"TransDex","title":"TransDex: Pre-training Visuo-Tactile Policy with Point Cloud Reconstruction for Dexterous Manipulation of Transparent Objects","date":"2026-03-14","year":2026,"collections":["policy"],"categories":["tactile-il","rgbd-3d"],"resourceType":"policy","summary":"Pretrains point-cloud reconstruction and tactile fusion to manipulate transparent objects despite missing/noisy depth.","institution":"Not reported","venue":"IEEE RA-L 2026","modalities":["Depth","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2603.13869","project":"https://transdex.github.io/","code":"https://github.com/LFGfg/TransDex"},"policy":{"family":"Diffusion","baseModel":"Point-cloud reconstruction pretraining + hierarchical visual–tactile fusion diffusion policy","modelSize":"Not reported","inputs":"Depth-derived point clouds, Paxini tactile arrays and robot state","outputs":"Arm and 16-DoF hand action targets","trainingData":"PyBullet synthetic reconstruction pretraining; real transparent-object demonstrations; scale not reported","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Contrastive/geometric reconstruction pretraining followed by diffusion imitation learning","evaluation":"Real transparent-object dexterous manipulation with noisy and incomplete depth"},"release":{"code":"Available","weights":"Not verified","data":"Not released","license":"Not verified","notes":"Model and preprocessing code public; README says example pretraining dataset will be released later."},"evidence":[{"url":"https://arxiv.org/abs/2603.13869","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/LFGfg/TransDex","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2603.13869","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Main setting does not consume RGB colors.","Real setup requires two calibrated RealSense D435i depth cameras."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Not released","license":"Not verified","notes":"Model and preprocessing code public; README says example pretraining dataset will be released later."}},"relatedIds":[]},{"id":"dream2fix","name":"Dream2Fix","title":"Learning Actionable Manipulation Recovery via Counterfactual Failure Synthesis","date":"2026-03-13","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","generalist-foundation"],"resourceType":"policy","summary":"Counterfactual failures generated from successful demonstrations train a model to diagnose and emit executable manipulation corrections.","institution":"Texas A&M University","venue":"Preprint","modalities":["RGB","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2603.13528"},"policy":{"family":"Hybrid","baseModel":"Generative world-model failure synthesis and a Qwen3-VL-based structured recovery predictor.","modelSize":"Not reported","inputs":"Task instruction, robot execution images/clips and current recovery context.","outputs":"Failure type, execution stage and discrete directional/magnitude correction fields consumed by the robot executor.","trainingData":"Over 120k synthesized failure/correction pairs; real source corpus includes 100 trajectories across ten manipulation tasks.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Perturb actions in a generative world model, filter synthesized failures for validity/coherence/kinematics, then fine-tune recovery predictions.","evaluation":"Real manipulation diagnosis and closed-loop recovery across object interaction tasks."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/2603.13528","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."}],"caveats":["Generated counterfactuals are training data, not measured deployment outcomes supplied in advance.","Structured recovery corrections are executed by a controller rather than interpreted as raw joint torques."],"verified":"2026-10-17"},{"id":"remem-vla","name":"ReMem-VLA","title":"ReMem-VLA: Empowering Vision-Language-Action Model with Memory via Dual-Level Recurrent Queries","date":"2026-03-13","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Dual-level recurrent queries retain short- and long-term context for memory-dependent manipulation.","institution":"Technical University of Munich","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2603.12942"},"policy":{"family":"VLA","baseModel":"Frozen Qwen3-VL-2B vision-language backbone, frame/chunk recurrent queries and an action diffusion model.","modelSize":"2B VLM backbone; full policy aggregate not reported.","inputs":"Third-person and wrist RGB views, instruction, recurrent memory state and optional robot proprioception.","outputs":"30-step chunks of absolute joint positions and binary gripper states; 20-step DDIM inference.","trainingData":"Not reported","trainingTime":"MemoryBench setting trains 150k steps; batch size 64. Wall-clock duration not reported.","trainingHardware":"Eight A100 GPUs for training.","controlRate":"About 10 Hz model inference on one RTX 4090; reusing chunks can yield nearly 20 Hz effective action playback.","trainingMethod":"End-to-end recurrent query/action learning with past-observation reconstruction supervision; truncated recurrent training.","evaluation":"Memory-centric simulation and real manipulation across spatial, sequential, episodic, temporal and visual memory."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/2603.12942","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."}],"caveats":["Past-observation prediction is a training objective, not extra observed future information.","Model call frequency and action playback differ."],"verified":"2026-10-17"},{"id":"tacvla","name":"TacVLA","title":"TacVLA: Contact-Aware Tactile Fusion for Robust Vision-Language-Action Manipulation","date":"2026-03-13","year":2026,"collections":["policy"],"categories":["tactile-foundation","tactile-il"],"resourceType":"policy","summary":"A contact-gated tactile prefix adapts π0.5 to constrained disassembly and picking under occlusion.","institution":"Purdue University / Istituto Italiano di Tecnologia / Università di Genova","venue":"Preprint","modalities":["RGB","Tactile","Pressure","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2603.12665","project":"https://sites.google.com/view/tacvla"},"policy":{"family":"VLA","baseModel":"OpenPI pi05_base (π0.5) with SigLIP visual encoding and a flow-matching action expert.","modelSize":"Not reported for the augmented policy.","inputs":"Front and wrist RGB, instruction, robot state and one finger-mounted 15×8 array of normal contact force readings; tactile encoder produces 36 tokens.","outputs":"Continuous robot action chunks from a flow-matching expert; action dimensionality Not reported.","trainingMethod":"Task-specific LoRA fine-tuning for 10k steps with the tactile encoder jointly optimized; threshold-based contact detection gates tactile embeddings and attention.","trainingData":"Five real tasks, 50 teleoperated demonstrations each: tight-shaft removal, press-clip removal, shaft rotation, slide-pull disassembly and in-box picking. All streams recorded at 10 Hz.","trainingTime":"10,000 gradient steps per task; wall-clock time Not reported.","trainingHardware":"Not reported.","controlRate":"Synchronized demonstrations at 10 Hz; achieved policy control frequency Not reported.","evaluation":"Franka Panda with one 120-taxel normal-force array and two RGB cameras; 20 trials per task, plus blocked-camera disassembly and qualitative human disturbance recovery."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No retrievable official policy code/checkpoint/data release was verified."},"evidence":[{"url":"https://arxiv.org/abs/2603.12665","label":"Original paper and reviewed full text","fields":["title","date","institution","summary","policy","caveats"],"note":"Full primary text, including available supplementary implementation details; first public date retained separately from reviewed revision."},{"url":"https://sites.google.com/view/tacvla","label":"Official author project","fields":["links","release"],"note":"Current project links and release claims checked independently of paper prose."}],"caveats":["Reviewed arXiv v4; original date remains 2026-03-13.","Fixed tactile-token topology is gated by a binary contact heuristic; it does not dynamically add/remove tokens.","No measured depth is reported for TacVLA, irrespective of its 3D-Diffusion-Policy baseline."],"verified":"2026-10-17"},{"id":"roboclaw","name":"RoboClaw","title":"RoboClaw: An Agentic Framework for Scalable Long-Horizon Robotic Tasks","date":"2026-03-12","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Unifies autonomous collection, policy learning and long-horizon execution under a VLM controller, using forward–inverse skill pairs to reset the workspace.","institution":"AgiBot; National University of Singapore; Shanghai Jiao Tong University","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2603.11558","code":"https://github.com/RoboClaw-Robotics/RoboClaw"},"policy":{"family":"Hybrid","baseModel":"VLM meta-controller with π0.5 learned manipulation policies; the paper does not identify one universal VLM checkpoint.","modelSize":"Not reported","inputs":"Task, multimodal robot observations, skill / MCP tools, file-based memory and policy / collection feedback.","outputs":"Calls to learned forward and inverse-reset policies, curated interaction trajectories and updated robot-policy checkpoints.","trainingData":"Iterative real-world collection adds fifty data samples per round in the reported policy-improvement experiment, over one to five rounds.","trainingTime":"Reported π0.5 LoRA setting: 10,000 steps, batch size 16, rank / alpha 16; elapsed duration is not reported.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen-model contextual orchestration is coupled to actual learned-policy updates. Entangled Action Pairs provide forward manipulation and inverse reset behaviors for continuous collection.","evaluation":"Real-world autonomous collection, four policy-improvement subtasks and multi-policy long-horizon execution."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact. The paper includes trained component / policy variants; a complete method-specific checkpoint release has not been established."},"evidence":[{"url":"https://arxiv.org/abs/2603.11558","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/RoboClaw-Robotics/RoboClaw/main/README.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."}],"caveats":["The VLM orchestrator and trainable robot policies are distinct components; in-context orchestration does not make the whole pipeline training-free.","Reset policies are learned skills whose effectiveness is separately evaluated."],"verified":"2026-10-17"},{"id":"sail","name":"SAIL","title":"SAIL: Test-Time Scaling for In-Context Imitation Learning with VLM","date":"2026-03-09","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Monte Carlo tree search refines complete demonstration-conditioned trajectories using visual scoring and step-level feedback.","institution":"The University of Tokyo / Sakana AI","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2603.08269"},"policy":{"family":"Robot agent","baseModel":"gemini-robotics-er-1.5-preview with trajectory-level MCTS and successful-trajectory archive retrieval.","modelSize":"Not reported","inputs":"Overhead RGB observation, demonstration/archive context and trajectory rollout feedback.","outputs":"Complete robot trajectories refined through MCTS; keypoint detection and trajectory scoring use the VLM.","trainingData":"Successful demonstrations populate the retrieval archive; default retrieves one demonstration.","trainingTime":"No task-specific VLM gradient training described.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Training-free trajectory search with branching factor three, VLM scoring and aligned step feedback.","evaluation":"Six simulated manipulation tasks, 20 evaluation seeds per setting, with real-world validation."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"Hosted VLM dependency; no SAIL-trained neural checkpoint implied."},"evidence":[{"url":"https://arxiv.org/abs/2603.08269","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."}],"caveats":["Trajectory-level search uses rollout feedback and should not be compared with a single policy call as equal compute."],"verified":"2026-10-17"},{"id":"iclr-visual-reasoning","name":"ICLR: Visual Reasoning Imitation","title":"ICLR: In-Context Imitation Learning with Visual Reasoning","date":"2026-03-08","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Augments demonstration-conditioned sensorimotor prediction with explicit image-space future keypoint traces that are generated before robot actions.","institution":"Not reported","venue":"IROS 2026","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2603.07530","project":"https://toannguyen1904.github.io/ICLR","code":"https://github.com/toannguyen1904/ICLR"},"policy":{"family":"In-context policy","baseModel":"Llama2-style causal transformer with frozen pretrained visual encoder, state / reasoning / action MLP encoders and attention pooling.","modelSize":"Not reported","inputs":"Third-view / wrist RGB, end-effector proprioception, demonstration trajectories and five-keypoint visual reasoning traces.","outputs":"An image-space visual reasoning trace followed by delta end-effector and gripper actions.","trainingData":"Task-grouped sensorimotor demonstrations augmented with future keypoint traces; simulation uses known camera geometry, while real traces are obtained using Molmo2.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Offline next-token prediction with teacher forcing and reasoning-token dropout; novel-task demonstrations condition the trained policy without additional weight updates.","evaluation":"Simulation and real-world imitation tasks, with ten real trials per task and reasoning-dropout ablations."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Apache-2.0","notes":"Official IROS 2026 implementation contains LIBERO and physical-robot training/evaluation scripts."},"evidence":[{"url":"https://arxiv.org/abs/2603.07530","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/toannguyen1904/ICLR","label":"Official implementation, README and file/license inspection on 19 October 2026","fields":["links","release","venue"],"note":"Official IROS 2026 implementation contains LIBERO and physical-robot training/evaluation scripts. Apache-2.0."}],"caveats":["ICLR here names an imitation-learning method, not the conference acronym.","Future keypoint annotations are training / demonstration representations; they are not future observations available to the executing robot."],"verified":"2026-10-19"},{"id":"contact-grounded-policy","name":"Contact-Grounded Policy","title":"Contact-Grounded Policy: Dexterous Visuotactile Policy with Generative Contact Grounding","date":"2026-03-05","year":2026,"collections":["policy"],"categories":["tactile-il","tactile-foundation"],"resourceType":"policy","summary":"A diffusion generator predicts coupled actual-state/tactile futures, then a learned contact-consistency mapping translates them into compliance-controller targets.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2603.05687","project":"https://contact-grounded-policy.github.io/"},"policy":{"family":"Diffusion","baseModel":"U-Net diffusion policy; KL-regularized tactile VAE; contact-consistency MLP.","modelSize":"Not reported","inputs":"RGB, tactile arrays or four Digit 360 images, and robot proprioceptive state.","outputs":"Future robot-state/tactile trajectories converted to executable target states.","trainingData":"Teleoperated demonstrations across three simulation and two real tasks; exact training counts Not reported in inspected text.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Observation 2, prediction 16, execution 8 steps; physical Hz Not reported.","trainingMethod":"Separate tactile compression and contact-consistency training, then conditional trajectory diffusion.","evaluation":"Three simulated and two real dexterous tasks, including dish wiping, in-hand box flipping and jar opening; single-task training/evaluation."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2603.05687","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://contact-grounded-policy.github.io/","label":"Official author project","fields":["links","release"],"note":"Live project artifact links inspected on2026-10-17."}],"caveats":["Reported A10080GB measurements are inference timing, not evidence of traininghardware.","No cross-task generalist transfer is established by single-task evaluations."],"verified":"2026-10-17"},{"id":"manipulationnet","name":"ManipulationNet","title":"ManipulationNet: An Infrastructure for Benchmarking Real-World Robot Manipulation with Physical Skill Challenges and Embodied Multimodal Reasoning","date":"2026-03-04","year":2026,"collections":["benchmark"],"categories":["precision-assembly","general-manipulation"],"resourceType":"benchmark","summary":"Distributed physical benchmarking infrastructure with standardized kits, server-issued trials, audit evidence and separate physical-skill and embodied-reasoning tracks.","institution":"Rice University / NIST / collaborating institutions","venue":"Preprint","modalities":["RGB","Depth","Language"],"environments":["Real world"],"embodiments":["Embodiment-agnostic"],"links":{"paper":"https://arxiv.org/abs/2603.04363","project":"https://mnet-client.readthedocs.io/ros_2/"},"benchmark":{"simulator":"Not applicable — real-world benchmark infrastructure","simulatorVersion":"Not applicable","taskCount":"Multiple hosted benchmarks in Physical Skills and Embodied Reasoning tracks; task count is infrastructure-dependent.","tasks":["Peg-in-hole assembly","Cable management","Grasping in clutter"],"taskListScope":"Published Physical Skills task families; not a complete evolving infrastructure catalogue.","capabilities":["Precision insertion","Cable routing","Clutter grasping","Multimodal reasoning","Distributed reproducibility"],"metrics":"Physical skill success rate and average successful completion time; reasoning goal accuracy / normalized efficiency; uploaded evidence audited.","robots":"Robot-agnostic clients connected to standardized physical task kits.","sensors":"Method-dependent visual / depth / tactile / force sensors; the infrastructure does not require RGBDT for every entry.","dataCollection":"Not applicable — standardized physical task/evaluation protocol; participants choose their own policy-training data and collection method.","data":"Task kits, machine-readable instructions and execution video/metadata; not one fixed demonstration corpus.","protocol":"Server specifies trial configuration; local human resets standard kit; robot executes; video/results uploaded for audit. Peg geometries and nominal clearances vary.","baselines":"Baseline systems and task-specific submissions; no single policy architecture mandated."},"release":{"code":"Available","weights":"Not applicable","data":"Partial","license":"Not verified","notes":"Client documentation and standardized-kit protocols public; inspect task-specific kits and access."},"evidence":[{"url":"https://arxiv.org/abs/2603.04363","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://mnet-client.readthedocs.io/ros_2/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://arxiv.org/abs/2603.04363","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Infrastructure task list evolves; do not treat later policy six-configuration subsets as the whole benchmark.","Sensors are policy choices rather than universally provided observation channels."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not applicable","data":"Partial","license":"Not verified","notes":"Client documentation and standardized-kit protocols public; inspect task-specific kits and access."}},"relatedIds":[]},{"id":"mem-pi06","name":"MEM / π0.6-MEM","title":"MEM: Multi-Scale Embodied Memory for Vision Language Action Models","date":"2026-03-04","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Multi-scale embodied memory combines compressed short-term video and long-term text memory inside a manipulation policy.","institution":"Physical Intelligence","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2603.03596","project":"https://pi.website/research/memory"},"policy":{"family":"VLA","baseModel":"π0.6 VLA with a video encoder using spatial/temporal compression and text-based high-level memory.","modelSize":"Not reported","inputs":"Multi-camera RGB history, proprioception, task goal and retained semantic text memory.","outputs":"Continuous robot action chunks; high-level subtasks and updated text memory.","trainingData":"Diverse robot and non-robot pretraining data; task demonstrations receive language-memory supervision. Exact total corpus scale for MEM not reported.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Inference-latency study uses four camera streams on one H100; the 300 ms line in the figure is a latency threshold, not a measured policy period.","trainingMethod":"Train video-memory encoding and memory-conditioned action prediction, with language summaries of task history.","evaluation":"Real long-horizon manipulation up to 15 minutes, including kitchen cleanup, recipe preparation and grilled cheese; partial observability and in-context adaptation."},"release":{"code":"Not released","weights":"Not released","data":"Not released","license":"Not verified","notes":"Paper and author demonstrations available; no MEM-specific policy checkpoint or training corpus identified."},"evidence":[{"url":"https://arxiv.org/abs/2603.03596","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://pi.website/research/memory","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."}],"caveats":["Distinct memory-method paper built on π0.6; the base π0.6 record is not duplicated.","The H100 figure measures inference, not MEM training hardware."],"verified":"2026-10-17"},{"id":"robocasa365","name":"RoboCasa365","title":"RoboCasa365: A Large-Scale Simulation Framework for Training and Benchmarking Generalist Robots","date":"2026-03-04","year":2026,"collections":["benchmark"],"categories":["general-manipulation"],"resourceType":"benchmark","summary":"Kitchen manipulation framework with 365 tasks, 2,500 environment variations and large human/synthetic demonstration collections; public multitask leaderboard uses 50 target tasks.","institution":"University of Texas at Austin","venue":"ICLR 2026","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Mobile manipulator","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2603.04356","project":"https://robocasa.ai/","code":"https://github.com/robocasa/robocasa","data":"https://robocasa.ai/docs/build/html/datasets/datasets_overview.html"},"benchmark":{"simulator":"MuJoCo via robosuite / RoboCasa","simulatorVersion":"Current benchmark note: RoboCasa 1.0.1. Exact MuJoCo/robosuite pins depend on official installation; do not substitute a downstream policy repo pin as paper version.","taskCount":"365 task suite; 50-task multitask target benchmark; current dataset registry exposes 317 named task datasets (65 atomic + 252 composite).","tasks":["AdjustToasterOvenTemperature","AdjustWaterTemperature","CloseBlenderLid","CloseCabinet","CloseDishwasher","CloseDrawer","CloseElectricKettleLid","CloseFridge","CloseFridgeDrawer","CloseMicrowave","CloseOven","CloseStandMixerHead","CloseToasterOvenDoor","CoffeeServeMug","CoffeeSetupMug","NavigateKitchen","OpenBlenderLid","OpenCabinet","OpenDishwasher","OpenDrawer","OpenElectricKettleLid","OpenFridge","OpenFridgeDrawer","OpenMicrowave","OpenOven","OpenStandMixerHead","OpenToasterOvenDoor","PickPlaceCabinetToCounter","PickPlaceCounterToBlender","PickPlaceCounterToCabinet","PickPlaceCounterToDrawer","PickPlaceCounterToMicrowave","PickPlaceCounterToOven","PickPlaceCounterToSink","PickPlaceCounterToStandMixer","PickPlaceCounterToStove","PickPlaceCounterToToasterOven","PickPlaceDrawerToCounter","PickPlaceFridgeDrawerToShelf","PickPlaceFridgeShelfToDrawer","PickPlaceMicrowaveToCounter","PickPlaceSinkToCounter","PickPlaceStoveToCounter","PickPlaceToasterOvenToCounter","PickPlaceToasterToCounter","PreheatOven","SlideDishwasherRack","SlideOvenRack","SlideToasterOvenRack","StartCoffeeMachine","TurnOffMicrowave","TurnOffSinkFaucet","TurnOffStove","TurnOnBlender","TurnOnElectricKettle","TurnOnMicrowave","TurnOnSinkFaucet","TurnOnStove","TurnOnToaster","TurnOnToasterOven","TurnSinkSpout","MakeIcedCoffee","CheesyBread","LowerHeat","PackDessert","AddIceCubes","AddLemonToFish","AddMarshmallow","AddSugarCubes","AddSweetener","AdjustHeat","AfterwashSorting","AirDryFruit","AlcoholServingPrep","AlignSilverware","ArrangeBreadBasket","ArrangeBreadBowl","ArrangeBuffetDessert","ArrangeDrinkware","ArrangeTea","ArrangeTeaAccompaniments","ArrangeUtensilsByType","ArrangeVegetables","AssembleCookingArray","BalancedMealPrep","BeverageOrganization","BeverageSorting","BlendIngredients","BlendMarinade","BowlAndCup","BreadAndCheese","BreadSelection","BreadSetupSlicing","BuildAppetizerPlate","ButterOnPan","CandleCleanup","CategorizeCondiments","CerealAndBowl","ChooseMeasuringCup","ChooseRipeFruit","CleanBoard","CleanMicrowave","ClearClutter","ClearCuttingBoard","ClearFoodWaste","ClearFreezer","ClearReceptaclesForCleaning","ClearSink","ClearSinkArea","ClearSinkSpace","CollectWashingSupplies","ColorfulSalsa","CondimentCollection","CookieDoughPrep","CoolBakedCake","CoolKettle","CreateChildFriendlyFridge","CupcakeCleanup","CutBuffetPizza","CuttingToolSelection","DateNight","DefrostByCategory","DeliverBrewedCoffee","DeliverStraw","DessertAssembly","DessertUpgrade","DisplayMeatVariety","DistributeChicken","DivideBasins","DivideBuffetTrays","DrainVeggies","DrinkwareConsolidation","DryDishes","DryDrinkware","DumpLeftovers","FillBlenderJug","FillKettle","FilterMicrowavableItem","FoodCleanup","FreezeBottledWaters","FreezeCookedFood","FreezeIceTray","FreshProduceOrganization","FryingPanAdjustment","GarnishCupcake","GarnishPancake","GatherCuttingTools","GatherMarinadeIngredients","GatherTableware","GatherVegetables","GetToastedBread","HeatKebabSandwich","HeatMug","HeatMultipleWater","HotDogSetup","JuiceFruitReamer","KettleBoiling","LemonSeasoningFish","LineUpCondiments","LoadCondimentsInFridge","LoadDishwasher","LoadFridgeByType","LoadFridgeFifo","LoadPreparedFood","MakeFruitBowl","MakeIceLemonade","MakeLoadedPotato","MatchCupAndDrink","MaximizeFreezerSpace","MealPrepStaging","MeatSkewerAssembly","MeatTransfer","MicrowaveCorrectMeal","MicrowaveDefrostMeat","MicrowaveThawing","MicrowaveThawingFridge","MixCakeFrosting","MixedFruitPlatter","MoveFreezerToFridge","MoveFridgeToFreezer","MoveToCounter","MoveToFreezerDrawer","MultistepSteaming","OrganizeBakingIngredients","OrganizeCleaningSupplies","OrganizeCoffeeCondiments","OrganizeCondiments","OrganizeMetallicUtensils","OrganizeMugsByHandle","OrganizeVegetables","OvenBroilFish","PackFoodByTemp","PackFruitContainer","PackIdenticalLunches","PanTransfer","PastryDisplay","PlaceBeveragesTogether","PlaceDishesBySink","PlaceEqualIceCubes","PlaceFoodInBowls","PlaceIceInCup","PlaceMeatInMarinade","PlaceMicrowaveSafeItem","PlaceOnDishRack","PlaceStraw","PlaceVegetablesEvenly","PlaceVeggiesInDrawer","PlateSteakMeal","PlateStoreDinner","PortionFruitBowl","PortionHotDogs","PortionInTupperware","PortionOnSize","PortionYogurt","PreRinseStation","PreSoakPan","PreheatPot","PrepForSanitizing","PrepForTenderizing","PrepFridgeForCleaning","PrepMarinatingMeat","PrepSinkForCleaning","PrepareBroilingStation","PrepareCheeseStation","PrepareCocktailStation","PrepareCoffee","PrepareDishwasher","PrepareDrinkStation","PrepareSausageCheese","PrepareSmoothie","PrepareSoupServing","PrepareStoringLeftovers","PrepareToast","PressChicken","PrewashFoodAssembly","PrewashFoodSorting","QuickThaw","RearrangeFridgeItems","RecycleBottlesBySize","RecycleBottlesByType","RecycleSodaCans","RecycleStackedYogurt","RefillCondimentStation","ReheatMeal","RemoveBroiledFish","RemoveCuttingBoardItems","ReorganizeFrozenVegetables","ResetCabinetDoors","RestockBowls","RestockCannedFood","RestockPantry","RestockSinkSupplies","RetrieveIceTray","RetrieveMeat","ReturnHeatedFood","ReturnWashingSupplies","RinseBowls","RinseCuttingBoard","RinseSinkBasin","RotatePan","SanitizePrepCuttingBoard","ScalePortioning","ScrubBowl","ScrubCuttingBoard","SearingMeat","SeasoningSpiceSetup","SeasoningSteak","SeparateFreezerRack","ServeSteak","ServeTea","ServeWarmCroissant","SetBowlsForSoup","SetUpCuttingStation","SetUpSpiceStation","SetupBowls","SetupButterPlate","SetupFruitBowl","SetupFrying","SetupSodaBowl","SetupWineGlasses","ShakePan","SimmeringSauce","SizeSorting","SnackSorting","SoakSponge","SortingCleanup","SpicyMarinade","StackBowlsCabinet","StackBowlsInSink","StackCans","StartElectricKettle","SteamInMicrowave","StirVegetables","StockingBreakfastFoods","StoreDumplings","StoreLeftoversByType","StoreLeftoversInBowl","StrainerSetup","SweetSavoryToastSetup","SweetenCoffee","SweetenHotChocolate","ThawInSink","TiltPan","ToastBagel","ToastBaguette","ToastOnCorrectRack","TongBuffetSetup","TransportCookware","TurnOffSimmeredSauceHeat","VeggieDipPrep","WaffleReheat","WarmCroissant","WashFish","WashFruitColander","WashLettuce","WeighIngredients","YogurtDelightPrep"],"taskListScope":"317 named dataset-registry entries at audit time, a released/data-registry subset; not claimed as all 365 task definitions or the 50-task leaderboard list.","capabilities":["Household mobile manipulation","Atomic skills","Compositional generalization","Multitask/foundation/lifelong learning"],"metrics":"Task success by Atomic-Seen, Composite-Seen and Composite-Unseen; environment and data-scale generalization.","robots":"Mobile Panda-based kitchen manipulation embodiment.","sensors":"RGB / depth rendering, proprioception and language available; actual baseline input setting documented per policy.","dataCollection":"Human teleoperation provides seed demonstrations; MimicGen synthesizes additional trajectories from those seeds. Human and synthetic portions retain separate scopes.","data":"600+ hours human demonstrations and 1,600+ hours synthetic demonstrations across 2,500 kitchens.","protocol":"Separate multitask, foundation-model and lifelong-learning settings; leaderboard focuses 50 target tasks; training-distribution kitchens vs held-out target kitchens must be reported explicitly.","baselines":"Diffusion Policy, π0, π0.5, GR00T N1.5 in original benchmark; evolving official leaderboard adds other policies."},"release":{"code":"Available","weights":"Partial","data":"Available","license":"MIT code; object/kitchen assets and dataset terms separate","notes":"Framework, task assets, human/synthetic datasets and leaderboard public."},"evidence":[{"url":"https://arxiv.org/abs/2603.04356","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://robocasa.ai/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/robocasa/robocasa","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://github.com/robocasa/robocasa/blob/main/robocasa/utils/dataset_registry.py","label":"Official implementation / documentation","fields":["benchmark.tasks"],"note":"Parsed 65 atomic + 252 composite dataset names; released subset explicitly scoped."},{"url":"https://robocasa.ai/docs/build/html/datasets/datasets_overview.html","label":"Official dataset documentation","fields":["links.data","benchmark.data"],"note":"Link verified after navigation from official documentation; initial guessed overview.html path corrected."},{"url":"https://arxiv.org/abs/2603.04356","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["The full suite, named dataset registry and leaderboard task counts differ.","Current leaderboard notes a 1.5 × rollout-horizon reevaluation relative to paper values; compare matched protocol versions."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Partial","data":"Available","license":"MIT code; object/kitchen assets and dataset terms separate","notes":"Framework, task assets, human/synthetic datasets and leaderboard public."}},"relatedIds":[]},{"id":"act-observe-rewrite","name":"Act-Observe-Rewrite (AOR)","title":"Act-Observe-Rewrite: Multimodal Coding Agents as In-Context Policy Learners for Robot Manipulation","date":"2026-03-03","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Rewrites the full Python controller between episodes using multimodal failure evidence, while the generated controller executes the next trial without model calls at every step.","institution":"General Bionix, Inc.","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2603.04466"},"policy":{"family":"Robot agent","baseModel":"Claude Code using the claude-sonnet-4-x family; Codex / GPT-5.3 High is a separate substitution experiment.","modelSize":"Not reported","inputs":"RGB-D camera observations, controller code, structured episode outcomes, phase / reward records and selected failure keyframes.","outputs":"A rewritten vision pipeline, feature representation and Python low-level controller that issues Cartesian delta actions.","trainingData":"Interactive robosuite episodes, without demonstrations or gradient-based policy training.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen coding model; episode-level act–observe–rewrite changes controller implementation while a fast local controller runs within episodes.","evaluation":"Three robosuite manipulation tasks, with coding-backbone substitution and failure analysis."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The original paper is public; code, model and dataset release scopes are verified separately where established. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms."},"evidence":[{"url":"https://arxiv.org/abs/2603.04466","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."}],"caveats":["All reported task results are in simulation; real-robot deployment is future work.","High simulation success does not establish robust real contact handling or arbitrary-task generalization."],"verified":"2026-10-17"},{"id":"uni-skill","name":"Uni-Skill (SkillFolder)","title":"Uni-Skill: Building Self-Evolving Skill Repository for Generalizable Robotic Manipulation","date":"2026-03-03","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Uses a hierarchical skill-video repository to retrieve examples, implement missing skills and extend a robot planner beyond a fixed initial skill library.","institution":"Institute of Computing Technology, Chinese Academy of Sciences; University of Chinese Academy of Sciences","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2603.02623"},"policy":{"family":"Robot agent","baseModel":"Video / code-adapted VLM with GPT-4o waypoint grounding; Gemini 2.0 Flash extracts and aligns skill annotations.","modelSize":"Not reported","inputs":"Task, current RGB-D scene / bounding boxes and retrieved SkillFolder examples; new runtime skills need no deployment-time demonstration.","outputs":"Extended skill descriptions / implementations and six-degree-of-freedom waypoint sequences grounded from selected image points.","trainingData":"106,000 runtime-code samples adapt the code VLM; SkillFolder contains over 10,000 skill-centric segments extracted from 350 hours of DROID video.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Offline VLM adaptation / automatic repository annotation precede deployment-time retrieval and skill extension. This is not a wholly untrained foundation-model pipeline.","evaluation":"Eight RLBench base-skill tasks, ten extension tasks and real-world long-horizon / unseen-skill manipulation."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The original paper is public; code, model and dataset release scopes are verified separately where established."},"evidence":[{"url":"https://arxiv.org/abs/2603.02623","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."}],"caveats":["Distinct from UniSkill human-video skill representations (arXiv:2505.08787).","Depth lifts selected 2D points into 3D; retrieved video skill examples do not eliminate geometric control tools."],"verified":"2026-10-17"},{"id":"tacmamba","name":"TacMamba","title":"TacMamba: A Tactile History Compression Adapter Bridging Fast Reflexes and Slow VLA Reasoning","date":"2026-03-02","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Compresses high-rate single-axis force history into a recurrent state injected into a slower VLA.","institution":"Zhejiang University / GigaAI","venue":"Preprint","modalities":["RGB","Force/Torque","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2603.01700"},"policy":{"family":"VLA","baseModel":"Mamba tactile history compressor + π0.5","modelSize":"Not reported","inputs":"RGB, language, state and one-dimensional force history","outputs":"VLA robot actions conditioned on force-memory soft prompts","trainingData":"Not reported","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Force stream100 Hz; VLA≈1 Hz; encoder latency0.45 ms (not full control latency)","trainingMethod":"Temporal discrimination pretraining and phase-uniform task sampling","evaluation":"Discrete counting and implicit state-switching tasks including button clicks"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."},"evidence":[{"url":"https://arxiv.org/abs/2603.01700","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://arxiv.org/html/2603.01700","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["“Tactile” input is a single-axis force signal, not spatial touch.","Submillisecond encoder latency does not imply submillisecond action feedback."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."}},"relatedIds":[]},{"id":"rmbench","name":"RMBench / Mem-0","title":"RMBench: Memory-Dependent Robotic Manipulation Benchmark with Insights into Policy Design","date":"2026-03-01","year":2026,"collections":["benchmark","policy"],"categories":["robustness-evaluation","generalist-foundation"],"resourceType":"policy","summary":"Introduces nine history-dependent tasks and a modular policy that combines explicit planning memories, visual execution memory and learned subtask termination.","institution":"HKU / Peking University / Shanghai AI Laboratory","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2603.01229","project":"https://rmbench.github.io/","code":"https://github.com/RoboTwin-Platform/RMBench","data":"https://huggingface.co/datasets/qiuly/Mem-0-m1mix-dataset-RMBench","weights":"https://huggingface.co/qiuly/Mem-0-m1mix-RMBench"},"policy":{"family":"VLA","baseModel":"Qwen3-VL-8B planning VLM; Qwen3-VL-2B execution VLM with DiT-B flow action expert and termination classifier","modelSize":"8B planner and 2B execution VLM; action/memory components are additional; planner omitted on M(1) tasks","inputs":"Head-camera RGB/history, 16D state and task/subtask language; key subtask memories in planning module","outputs":"16D action chunks, horizon 30, all 30 steps executed; subtask-end classification","trainingData":"50 expert demos/task for 9 simulation tasks; fine-grained action/subtask language; separate real-task data","trainingTime":"Per-task planning 25 epochs≈ 0.5 h; execution 30k updates≈ 18 h; retrained July release differs from lost original checkpoints","trainingHardware":"Planning and execution training: 8 × NVIDIA A800; inference singleA800","controlRate":"Paper reports5–10 Hz control with chunked inference; distinguish30-step action sequence from call rate","trainingMethod":"LoRA planner tuning, supervised memory-augmented flow execution and weighted subtask-end classification","evaluation":"9 RMBench simulation tasks and 3 physical memory-dependent tasks; controlled architecture ablations"},"release":{"code":"Available","weights":"Available","data":"Available","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"The author README links the actual HF datasets/qiuly/Mem-0-m1mix-dataset-RMBench repository, whose inventory contains LeRobot episode parquet and media files. The prior catalog URL omitted /datasets/; it is corrected independently of the separate Mem-0 model repositories. Full paper-scale reproduction was not attempted."},"evidence":[{"url":"https://arxiv.org/abs/2603.01229","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/RoboTwin-Platform/RMBench","label":"Official policy implementation","fields":["links"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://github.com/RoboTwin-Platform/RMBench#-updates","label":"Official checkpoint release update, 2026-07-14","fields":["caveats","benchmark.protocol"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://arxiv.org/abs/2603.01229","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."},{"url":"https://github.com/RoboTwin-Platform/RMBench/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."},{"url":"https://github.com/RoboTwin-Platform/RMBench","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/datasets/qiuly/Mem-0-m1mix-dataset-RMBench","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/qiuly/Mem-0-m1mix-RMBench","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["Mem-0 uses additional subtask/termination supervision; comparisons reflect architecture plus this extra training signal.","Official July14 release retrains M(1) jointly after original checkpoints were lost; released numbers are not automatically the paper checkpoints."],"verified":"2026-10-19","benchmarkName":"RMBench","policyName":"Mem-0","collectionSummaries":{"benchmark":"Nine memory-dependent manipulation tasks spanning single-key-observation and extended-history complexity.","policy":"Introduces nine history-dependent tasks and a modular policy that combines explicit planning memories, visual execution memory and learned subtask termination."},"benchmark":{"simulator":"SAPIEN through RoboTwin2.0","simulatorVersion":"Not reported for the full benchmark in manuscript; use repository-pinned environment for reproduction","taskCount":"9 simulation tasks; separate3physical validation tasks","tasks":["Observe and Pick Up","Rearrange Blocks","Put Back Block","Swap Blocks","Swap T","Battery Try","Blocks Ranking Try","Cover Blocks","Press Button"],"taskListScope":"Complete published9-task simulation suite","capabilities":["Visual memory","Object permanence","Historical-state disambiguation","Trial-and-error memory","Event counting","Long-horizon execution"],"metrics":"Task success rate over100rollouts/task; memory-component ablations and failure analysis","robots":"RoboTwin2.0 dual-arm embodiment; separate physical dual-arm setup","sensors":"RGB observations and robot state; fine-grained language annotations; no tactile requirement","dataCollection":"Expert demonstrations with action/subtask-aligned language annotations. The original collection controller, motion planner and teleoperation interface are Not reported in the inspected manuscript.","data":"50expert demonstrations/task for all evaluated simulation policies; action/subtask aligned language is additional Mem-0 supervision","protocol":"Common50-demo/task training and100-rollout evaluation; M(1) tasks isolate execution module, M(n) uses planning+execution; see released-retrained checkpoint caveat","baselines":"DP,ACT,π0.5,X-VLA,QwenOFT,MemER,MemoryVLA and Mem-0"}},{"id":"gap-action-geometry","name":"GAP — Action-Geometry Prediction","title":"Action-Geometry Prediction with 3D Geometric Prior for Bimanual Manipulation","date":"2026-02-27","year":2026,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Jointly predicts a bimanual action chunk and future pointmap latent from RGB-derived 3D features.","institution":"Sichuan University; UESTC; Dexmal; Chinese University of Hong Kong","venue":"arXiv preprint","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2602.23814","code":"https://github.com/Chongyang-99/GAP"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2602.23814","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://github.com/Chongyang-99/GAP","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/Chongyang-99/GAP/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"165 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["RealSense camera hardware is used for RGB; this does not make depth a deployed policy input."],"verified":"2026-10-17","policy":{"family":"Diffusion","baseModel":"π³ geometric foundation encoder, 2D semantic features and DETR-based joint action/geometry diffusion decoder.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Current/past RGB frames and proprioception; no measured depth or sensor point cloud required.","outputs":"Bimanual action chunks plus future 3D latent/pointmap prediction.","trainingMethod":"Joint diffusion learning of action and scene geometry.","trainingData":"100 expert demonstrations per simulation task; 50 teleoperated demonstrations for each of four physical tasks.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"NVIDIA RTX 4090 GPUs for the proposed model","evaluation":"RoboTwin2 and AgileX Cobot Magic scan-object, hang-mug, bowl-stack and dual-shoe tasks.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"dex4d","name":"Dex4D","title":"Dex4D: Task-Agnostic Point Track Policy for Zero-Shot Dexterous Manipulation","date":"2026-02-17","year":2026,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"A task-agnostic 3D point-track controller transfers simulated dexterous skills to real manipulation.","institution":"Carnegie Mellon University","venue":"arXiv preprint","modalities":["RGB","Depth","Point cloud","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2602.15828","project":"https://dex4d.github.io/","code":"https://github.com/Dex4D/Dex4D-Simulation"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Apache-2.0 for repository code; pretrained models, data and figure rights remain separate","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2602.15828","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://dex4d.github.io/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/Dex4D/Dex4D-Simulation","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/Dex4D/Dex4D-Simulation/blob/master/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/Dex4D/Dex4D-Simulation/tree/master","label":"Actual repository content inventory","fields":["release.code"],"note":"447 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["Current RGB-D observations and desired tracks inferred from generated video have different provenance.","Track following is task-agnostic; language-to-track generation still depends on external modules."],"verified":"2026-10-17","policy":{"family":"RL / sim-to-real","baseModel":"AP2AP point-track policy; PPO teacher and DAgger student, with modular video/depth/tracking goal extraction.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Current RGB-D point cloud, desired 3D point tracks and proprioception.","outputs":"22-D commands: xArm6 delta joint angles and 16 LEAP hand absolute joint angles.","trainingMethod":"Three-stage PPO teacher followed by DAgger student; sim-to-real with domain randomization.","trainingData":"3,200 simulated objects; no real robot training data.","trainingTime":"Teacher 2–3 days; student approximately 20 hours","trainingHardware":"Single NVIDIA RTX A6000","evaluation":"Real and simulation dexterous tasks driven by generated videos, human demonstrations or 3D forecasts.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"dreamzero","name":"DreamZero","title":"World Action Models are Zero-shot Policies","date":"2026-02-17","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Joint video-action generation transfers pretrained video dynamics to manipulation and unseen motions.","institution":"NVIDIA","venue":"Technical report","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2602.15922","project":"https://dreamzero0.github.io/","code":"https://github.com/dreamzero0/dreamzero","data":"https://huggingface.co/datasets/GEAR-Dreams/DreamZero-DROID-Data","weights":"https://huggingface.co/GEAR-Dreams/DreamZero-DROID"},"policy":{"family":"World-action model","baseModel":"Wan2.1-I2V-14B autoregressive video diffusion backbone + state/action encoders","modelSize":"14B backbone","inputs":"Multi-view RGB/video context, language and proprioceptive state","outputs":"Joint future video and continuous robot action chunks","trainingData":"AgiBot 7.2k episodes/~500 h; separate DROID model; new YAM adaptation55 play trajectories/~30 min","trainingTime":"Not reported","trainingHardware":"Training hardware/count not reported as a complete cost; release inference requires ≥2 GPUs, tested GB200/H100","controlRate":"Paper optimized Flash 7 Hz closed-loop; released README~0.6 s/chunk GB200/~3 s H100","trainingMethod":"Joint video/action flow training with optimized autoregressive closed-loop inference","evaluation":"Real AgiBot/DROID seen/unseen tasks, YAM transfer, post-training and simulation zero-shot evaluations"},"release":{"code":"Available","weights":"Available","data":"Partial","license":"Apache-2.0 (code); model/backbone/data terms separately apply","notes":"Official DROID/AgiBot checkpoints and DROID data released; release speed differs from optimized paper result."},"evidence":[{"url":"https://arxiv.org/abs/2602.15922","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/dreamzero0/dreamzero","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://dreamzero0.github.io/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/dreamzero0/dreamzero/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["Paper7 Hz optimized result is not the default released runtime speed.","Unseen-task results often use task progress rather than binary success.","500 h AgiBot data and separate DROID training are distinct configurations."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Partial","license":"Apache-2.0 (code); model/backbone/data terms separately apply","notes":"Official DROID/AgiBot checkpoints and DROID data released; release speed differs from optimized paper result."}},"relatedIds":[]},{"id":"dm0","name":"DM0","title":"DM0: An Embodied-Native Vision-Language-Action Model towards Physical AI","date":"2026-02-16","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Combines embodied-native VLM training, spatial reasoning supervision and a flow-matching action expert with insulated action gradients.","institution":"Dexmal","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2602.14974","code":"https://github.com/dexmal/dexbotic","weights":"https://huggingface.co/Dexmal/DM0-base"},"policy":{"family":"VLA","baseModel":"Qwen3-1.7B language backbone with Perception Encoder; flow-matching action expert","modelSize":"Paper reports approximately 2B model; component/total accounting follows manuscript","inputs":"Multi-view RGB, language instruction and robot proprioception","outputs":"Continuous action chunks, horizon 50 in specialist/generalist real-robot experiments; optional spatial reasoning text","trainingData":"Unified text/image/driving/embodied pretraining: 1.2T tokens; heterogeneous robotic mid-training and platform/task-specific SFT","trainingTime":"VLM pretraining 370k steps; mid-training 1 epoch; specialist SFT 40k–150k and generalist 200k iterations; wall-clock not reported","trainingHardware":"Mid-training 64 × H20; specialist 8 × H20; generalist 16 × H20","controlRate":"Not reported; 50-step action horizon is not feedback frequency","trainingMethod":"Pretraining → embodied mid-training with knowledge insulation → task/platform SFT and spatial scaffolding","evaluation":"RoboChallenge Table 30 specialist/generalist; real UR5, Franka, ARX5 and ALOHA; additional simulated evaluations"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"MIT","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2602.14974","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/dexmal/dexbotic","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."},{"url":"https://raw.githubusercontent.com/dexmal/dexbotic/main/LICENSE","label":"Repository license file","fields":["release.license"],"note":"Code scope; model, data and third-party component terms can differ."},{"url":"https://dev.dexmal.com/developer-docs/dm0","label":"Official DM0 deployment documentation","fields":["release.weights","policy.modelSize","caveats"],"note":"Documents released DM0-base2.4B; preserve distinction from paper2B claim."}],"caveats":["DM0 paper is arXiv2602.14974; Dexbotic2510.23511 is the supporting framework and is not the same paper.","Current developer documentation labels DM0-base2.4B, whereas the manuscript reports2B; released asset and paper size conventions are not silently equated."],"verified":"2026-10-17"},{"id":"scfields","name":"SCFields","title":"Semantic-Contact Fields for Category-Level Generalizable Tactile Tool Manipulation","date":"2026-02-14","year":2026,"collections":["policy"],"categories":["tactile-il","tactile-foundation","rgbd-3d"],"resourceType":"policy","summary":"Semantic-contact fields combine RGB-D semantics and inferred extrinsic contact force for category-level tactile tool manipulation.","institution":"A*STAR / National University of Singapore / Nanyang Technological University","venue":"RSS2026","modalities":["RGB","Depth","Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2602.13833","project":"https://kevinskwk.github.io/SCFields/","code":"https://github.com/Kevinskwk/SCFields","data":"https://huggingface.co/datasets/Kevinskwk/scfields-release"},"policy":{"family":"Diffusion","baseModel":"GenDP-style diffusion policy conditioned on dense semantic/contact point fields.","modelSize":"Not reported","inputs":"Calibrated RGB-D observations, two GelSight Mini tactile streams and robot state.","outputs":"Robot end-effector/gripper actions for tool use.","trainingData":"Simulation contact pretraining, small real pseudo-labeled contact alignment set; scraper policy 150 demos across 4 tools/3 heights, separate crayon/peeler datasets.","trainingTime":"Contact-field simulation pretraining400 epochs, real alignment60 epochs; downstream policy1000 epochs. Wall-clock time Not reported.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Two-stage contact physics/real sensor alignment followed by diffusion imitation.","evaluation":"Franka Panda scraping, crayon drawing, peeling; seen/unseen tool variants and surface heights."},"release":{"code":"Available","weights":"Available","data":"Available","license":"MIT top-level code; third-party component licenses preserved separately.","notes":"Author-linked repository and actual LICENSE inspected; code, checkpoints anddatasets have independent availability."},"evidence":[{"url":"https://arxiv.org/abs/2602.13833","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://kevinskwk.github.io/SCFields/","label":"Official author project","fields":["links","release"],"note":"Live artifact status reviewed separately from paper claims."},{"url":"https://github.com/Kevinskwk/SCFields","label":"Official released implementation and LICENSE","fields":["links","release"],"note":"Repository metadata,README and actualLICENSE inspected; availability scoped to the listed component."},{"url":"https://huggingface.co/datasets/Kevinskwk/scfields-release","label":"Author-linked released dataset","fields":["links","release"],"note":"Public repository file inventory inspected; component/data scope follows model/dataset card."}],"caveats":["Real extrinsic contact targets are pseudo-labels from geometric heuristics/force optimization, not dense ground-truth force instrumentation.","Hosted contact-field checkpoints do not imply every downstream robot policy checkpoint is published."],"verified":"2026-10-17"},{"id":"rynnbrain-vla","name":"RynnBrain / RynnBrain-VLA","title":"RynnBrain: Open Embodied Foundation Models","date":"2026-02-13","year":2026,"collections":["policy"],"categories":["generalist-foundation","agent-rsi-icl"],"resourceType":"policy","summary":"Embodied spatial pretraining is adapted to flow-matching robot control and point-grounded hierarchical planning.","institution":"DAMO Academy, Alibaba Group; affiliated collaborators as original manuscript","venue":"arXiv preprint","modalities":["RGB","Language","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2602.14979","project":"https://alibaba-damo-academy.github.io/RynnBrain/","code":"https://github.com/alibaba-damo-academy/RynnBrain/tree/rynnbrain1.0"},"release":{"code":"Partial","weights":"Partial","data":"Not verified","license":"Apache-2.0 for repository code; pretrained models, data and figure rights remain separate","notes":"Open brain inference/model weights are verified; a complete physical VLA training/deployment stack and task-policy checkpoint are not identified."},"evidence":[{"url":"https://arxiv.org/abs/2602.14979","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://alibaba-damo-academy.github.io/RynnBrain/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/alibaba-damo-academy/RynnBrain/tree/rynnbrain1.0","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/alibaba-damo-academy/RynnBrain/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/alibaba-damo-academy/RynnBrain/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"133 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["Spatial/contact grounding is inferred from images, not skin tactile sensing or measured depth input."],"verified":"2026-10-17","policy":{"family":"VLA","baseModel":"Qwen3-VL-derived RynnBrain; RynnBrain-2B used as single-stream flow-matching action transformer.","modelSize":"VLA uses 2B backbone; foundation family 2B/8B/30B-A3B is separate","inputs":"Multi-view RGB, language/pointing instruction, initial frame and proprioception.","outputs":"Continuous action chunks; base brain separately returns text, pointing and plans.","trainingMethod":"Embodied pretraining then 60K-step VLA finetuning; optional RynnBrain-Plan high-level loop.","trainingData":"Six teleoperated Franka pick-place tasks across three objects for VLA adaptation.","trainingTime":"VLA finetuning 60K steps; wall-clock not reported","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Three multi-object physical task settings; ten trials/model/setting plus qualitative long-horizon planning.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"steerable-policies","name":"Steerable Policies","title":"Steerable Vision-Language-Action Policies for Embodied Reasoning and Hierarchical Control","date":"2026-02-13","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Trains VLAs to follow commands at task, subtask, motion and image-coordinate levels, then controls them with a learned reasoner or a prompted VLM.","institution":"University of California, Berkeley; Stanford University; Physical Intelligence","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2602.13193","project":"https://steerable-policies.github.io","code":"https://github.com/steerable-policies/steerable-policies-bridge","data":"https://huggingface.co/datasets/Embodied-CoT/steering_features_bridge","weights":"https://huggingface.co/Embodied-CoT/steerable-policy-openvla-7b-bridge"},"policy":{"family":"VLA","baseModel":"OpenVLA / Prismatic with DINOv2-SigLIP and Llama2-7B, plus a separately evaluated π0.5 variant. High-level control uses a learned embodied reasoner or Gemini 3.0.","modelSize":"7B language component for the OpenVLA-based variant; the π0.5 variant has a separate backbone.","inputs":"Robot RGB / state and a steering command: task, subtask, motion or grounded image coordinates. High-level reasoners additionally receive task and observation history.","outputs":"Low-level robot-action tokens / chunks; the high-level controller supplies richer steering commands rather than only task text.","trainingData":"Bridge robot demonstrations automatically annotated with synthetic commands, subtask / motion / bounding-box information and post-hoc rationales.","trainingTime":"OpenVLA-based policy: 80,000 steps, batch size 256; π0.5 uses a separate fine-tuning recipe.","trainingHardware":"Eight NVIDIA H100 GPUs for the OpenVLA-based policy run.","controlRate":"Not reported","trainingMethod":"Offline VLA training expands command controllability; high-level control is either learned or frozen-VLM in-context reasoning. These are separate configurations.","evaluation":"Real WidowX / Bridge-style generalization tasks, long-horizon reasoning and interactive steering, with two VLA architectures."},"release":{"code":"Available","weights":"Available","data":"Partial","license":"MIT (code); model, dependency and dataset terms are separate.","notes":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact. Actual checkpoint tensors are present in the author-linked Hub repository. This model card declares mit. The Hub releases Bridge steering annotations / rationales; upstream images, π-family artifacts and unrelated datasets have separate release scopes."},"evidence":[{"url":"https://arxiv.org/abs/2602.13193","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/steerable-policies/steerable-policies-bridge/master/README.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."},{"url":"https://raw.githubusercontent.com/steerable-policies/steerable-policies-bridge/master/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Code-license text checked directly; this license is not automatically assigned to model weights or third-party data."},{"url":"https://steerable-policies.github.io","label":"Official project links / artifacts","fields":["links","release"],"note":"Author-linked repository / model / dataset endpoints are audited individually; a link alone does not establish a permissive asset license."},{"url":"https://huggingface.co/Embodied-CoT/steerable-policy-openvla-7b-bridge","label":"Author-linked Hugging Face model files / card","fields":["links.weights","release.weights","release.notes"],"note":"Actual checkpoint tensors are present in the author-linked Hub repository. Card license: mit."},{"url":"https://huggingface.co/datasets/Embodied-CoT/steering_features_bridge","label":"Author-linked Hugging Face dataset files / card","fields":["links.data","release.data","release.notes"],"note":"The Hub releases Bridge steering annotations / rationales; upstream images, π-family artifacts and unrelated datasets have separate release scopes. No card license was identified; code terms are not assigned to this asset."}],"caveats":["Grounded 2D commands do not imply measured depth input.","A prompted high-level VLM may be frozen while the steerable low-level action policy is trained."],"verified":"2026-10-17"},{"id":"xiaomi0","name":"Xiaomi-Robotics-0","title":"Xiaomi-Robotics-0: An Open-Sourced Vision-Language-Action Model with Real-Time Execution","date":"2026-02-13","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Combines a Qwen VLM and flow-action transformer with asynchronous execution optimized for real manipulation.","institution":"Xiaomi Robotics","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2602.12684","project":"https://robotics.xiaomi.com/","code":"https://github.com/XiaomiRobotics/Xiaomi-Robotics-0","weights":"https://huggingface.co/collections/XiaomiRobotics/xiaomi-robotics-0"},"policy":{"family":"VLA","baseModel":"Qwen3-VL-4B-Instruct and diffusion transformer","modelSize":"4.7B total reported","inputs":"Multi-view RGB, language and robot proprioceptive state","outputs":"Flow-matched continuous action chunks; synchronous and asynchronous execution variants","trainingData":"Cross-embodiment robot trajectories and vision-language data; task post-training: 338 h Lego disassembly and 400 h towel folding","trainingTime":"40k pretraining steps, batch 32,768; post-training40k Lego /80k towel, batch 2,048; wall-clock not reported","trainingHardware":"Full training hardware not reported; measured inference RTX4090","controlRate":"About80 ms inference on RTX4090; input streams synchronized to 30 Hz timeline; asynchronous execution is a separate scheduling choice","trainingMethod":"Joint robot/VL pretraining followed by task adaptation and asynchronous action execution","evaluation":"LIBERO, CALVIN, SimplerEnv; real Lego disassembly and towel folding"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2602.12684","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/XiaomiRobotics/Xiaomi-Robotics-0","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."},{"url":"https://raw.githubusercontent.com/XiaomiRobotics/Xiaomi-Robotics-0/main/LICENSE","label":"Repository license file","fields":["release.license"],"note":"Code scope; model, data and third-party component terms can differ."}],"caveats":["The 80 ms policy latency does not imply12.5 Hz tactile feedback or servo rate."],"verified":"2026-10-17"},{"id":"lda1b","name":"LDA-1B","title":"LDA-1B: Scaling Latent Dynamics Action Model via Universal Embodied Data Ingestion","date":"2026-02-12","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Jointly denoises actions and DINO visual latents, using quality-dependent objectives across human and robot interaction data.","institution":"Galbot / CASIA / BAAI / Peking University","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2602.12215","project":"https://pku-epic.github.io/LDA/","code":"https://github.com/jiangranlv/latent-dynamics-action","weights":"https://huggingface.co/Wayer2/LDA-pretrain"},"policy":{"family":"World-action model","baseModel":"Frozen Qwen3-VL VLM and DINOv3 visual encoder; 1B multimodal diffusion transformer","modelSize":"1B trainable MM-DiT scale; frozen Qwen3-VL-4B/DINO encoders are additional components","inputs":"RGB visual observations, proprioceptive state and task language; asynchronous visual/action streams","outputs":"Continuous action chunks and future DINO-space visual latents","trainingData":"EI-30k: over 30k hours of heterogeneous human/robot interaction, including lower-quality trajectories and actionless video","trainingTime":"400k pretraining iterations; 4,608 GPU-hours reported","trainingHardware":"48 × NVIDIA H800 for pretraining","controlRate":"Visual stream 3 Hz, action stream 10 Hz; these sampling rates are not full policy inference latency","trainingMethod":"Multi-objective policy/forward-dynamics/inverse-dynamics/visual-forecasting diffusion in DINO latent space","evaluation":"RoboCasa-GR1 and real Galbot/Unitree platforms with grippers and dexterous hands; contact-rich and long-horizon tasks"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Not verified","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2602.12215","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/jiangranlv/latent-dynamics-action","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."},{"url":"https://raw.githubusercontent.com/jiangranlv/latent-dynamics-action/main/LICENSE","label":"Repository license file","fields":["release.license"],"note":"Code scope; model, data and third-party component terms can differ."}],"caveats":["The 1B label does not include every frozen foundation encoder."],"verified":"2026-10-17"},{"id":"vlaw","name":"VLAW","title":"VLAW: Iterative Co-Improvement of Vision-Language-Action Policy and World Model","date":"2026-02-12","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","generalist-foundation"],"resourceType":"policy","summary":"Real rollout data improves a world model whose synthetic rollouts then improve a VLA in an iterative loop.","institution":"Stanford University / Tsinghua University","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2602.12063","project":"https://sites.google.com/view/vlaw-arxiv"},"policy":{"family":"Hybrid","baseModel":"π0.5 policy, Ctrl-World action-conditioned video model and Qwen3-VL reward model.","modelSize":"Not reported","inputs":"Robot RGB/state observations and task instructions; world model consumes candidate actions when generating training rollouts.","outputs":"Continuous VLA action chunks; generated video trajectories are supplemental training data.","trainingData":"Per iteration/category: 50 real trajectories and 500 synthetic trajectories per task; two iterations.","trainingTime":"World-model fine-tuning 50k steps per iteration; policy update 2k steps at batch size 256. Wall-clock duration not reported.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Iterative world-model fitting from policy successes/failures, synthetic rollout filtering/reward scoring and policy fine-tuning.","evaluation":"Real contact-rich manipulation including deformable objects and downstream generalization."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/2602.12063","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://sites.google.com/view/vlaw-arxiv","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."}],"caveats":["VLAW is distinct from WorldVLA and the separate World-VLA-Loop paper.","Policy/world-model parameters are updated between iterations; this is not fixed-weight context-only adaptation."],"verified":"2026-10-17"},{"id":"abot-m0","name":"ABot-M0","title":"ABot-M0: VLA Foundation Model for Robotic Manipulation with Action Manifold Learning","date":"2026-02-11","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Uses action-manifold learning and optional RGB-derived geometry modules for cross-embodiment robot control.","institution":"AMAP CV Lab","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2602.11236","code":"https://github.com/amap-cvlab/ABot-Manipulation/tree/ABot-M0","weights":"https://huggingface.co/acvlab"},"policy":{"family":"VLA","baseModel":"Qwen3-VL-4B; 0.16B DiT action expert; optional VGGT spatial module","modelSize":"4B VLM + 0.16B action expert; optional modules are additional","inputs":"Multi-view RGB, task language and robot state; optional RGB-derived geometry tokens","outputs":"Continuous action chunks, default horizon 16 with 4 denoising steps","trainingData":"UniACT: over 6M trajectories / 9,500 hours curated from six public robot datasets","trainingTime":"100k pretraining steps, global batch 1,024; wall-clock not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Curated multi-embodiment pretraining and direct clean-action manifold prediction; optional geometry fusion","evaluation":"LIBERO/Plus, RoboCasa-GR1, RoboTwin2 and real single-/dual-arm manipulation"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 for repository code, verified from LICENSE.txt; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2602.11236","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/amap-cvlab/ABot-Manipulation/tree/ABot-M0","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."},{"url":"https://github.com/amap-cvlab/ABot-Manipulation/blob/main/LICENSE.txt","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["VGGT derives geometry from RGB; no measured depth sensor input is established.","Original M0 branch is distinct from the repository default M0.5 world-action release."],"verified":"2026-10-19"},{"id":"rise-opendrivelab","name":"RISE (Compositional World Model)","title":"RISE: Self-Improving Robot Policy with Compositional World Model","date":"2026-02-11","year":2026,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Learns a compositional visual world model and progress-based value model, then improves a robot policy through imagined rollouts rather than repeated physical exploration.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2602.11075","project":"https://opendrivelab.com/RISE/","code":"https://github.com/OpenDriveLab/RISE","weights":"https://huggingface.co/OpenDriveLab-org/RISE_Assets/tree/main/dynamics_model/pretrained","data":"https://huggingface.co/OpenDriveLab-org/RISE_Assets/tree/main/sample_data"},"policy":{"family":"World-action model","baseModel":"Multi-view compositional world model, progress value model and manipulation policy; initialization checkpoints are specified per component in the paper.","modelSize":"Not reported","inputs":"Task language, initial multi-view visual context and candidate action chunks for imagination; real observations initialize policy execution.","outputs":"Predicted multi-view futures and rewards for policy optimization; the improved policy outputs manipulation actions.","trainingData":"Real-world warm-up data initialize the compositional world model; subsequent policy learning uses imagined experience.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Learned world-model / value components and policy-weight improvement through imagination-based reinforcement learning.","evaluation":"Three real-world tasks: Dynamic Brick Sorting, Backpack Packing and Box Closing."},"release":{"code":"Available","weights":"Partial — pretrained dynamics model","data":"Partial — sample HDF5 trajectories","license":"README declares Apache-2.0 code but no root LICENSE was found; bundled upstream notices remain separate. Official RISE_Assets card declares CC BY-NC-SA 4.0 for released dynamics/sample assets.","notes":"Training code for offline policy/value, dynamics and online RL plus Piper deployment is present. The official RISE_Assets inventory contains dynamics_model/pretrained/diffusion_pytorch_model.safetensors and sample_data/hdf5/ backpack-packing episodes. These artifacts do not establish release of every optimized task-policy checkpoint or the full training corpus."},"evidence":[{"url":"https://arxiv.org/abs/2602.11075","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/OpenDriveLab/RISE","label":"Author-linked repository","fields":["links.code"],"note":"Repository identity; code and checkpoint release status are stated separately."},{"url":"https://github.com/OpenDriveLab/RISE","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/OpenDriveLab-org/RISE_Assets/tree/main/sample_data","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/OpenDriveLab-org/RISE_Assets/tree/main/dynamics_model/pretrained","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["Distinct from the 2024 point-cloud policy RISE (arXiv: 2404.12281).","Imagined progress / reward is a learned training signal, not a tactile sensor or an independently observed contact measurement."],"verified":"2026-10-19"},{"id":"anytouch2","name":"AnyTouch 2 / ToucHD","title":"AnyTouch 2: General Optical Tactile Representation Learning For Dynamic Tactile Perception","date":"2026-02-10","year":2026,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Pretrains dynamic tactile representations from hierarchical atomic-action, manipulation and touch–force data, evaluating both perception and physical policies.","institution":"Renmin University of China; BAAI; Beijing Jiaotong University; Shanghai AI Laboratory; CAS Institute of Automation; BUPT; Peking University","venue":"ICLR 2026","modalities":["Tactile"],"environments":["Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2602.09617","project":"https://gewu-lab.github.io/AnyTouch2/","code":"https://github.com/GeWu-Lab/AnyTouch2","data":"https://huggingface.co/collections/BAAI/touchd","weights":"https://huggingface.co/xxuan01/AnyTouch2-Model"},"model":{"role":"Reusable representation or perception model; downstream controllers are separate systems","family":"Tactile encoder","baseModel":"OpenCLIP-Base encoders with tactile spatiotemporal tokens; 6-layer tactile ViT decoder, width 512 and 8 heads","modelSize":"Not reported","inputs":"Four optical tactile frames and sensor tokens; optional force, action and vision/language training supervision","outputs":"Dynamic tactile tokens; auxiliary force predictions and matching scores; downstream task heads consume frozen features","trainingMethod":"Original-frame and frame-difference masked reconstruction, object/action matching, force prediction and multimodal alignment","trainingData":"ToucHD simulated, manipulation and force components, plus existing optical-touch corpora; paper separates source frames, videos, real/sim and sensor counts","trainingTime":"40 pretraining epochs, adding matching/force at epoch 20 and alignment at epoch 30; downstream policy 100 epochs","trainingHardware":"4 NVIDIA H100 GPUs for pretraining","inference":"Four-frame encoder window; downstream experiment action prediction is separate from encoder latency","evaluation":"Object properties and dynamic force/slip/pose probes; Diffusion Policy on tactile grasping, whiteboard wiping, USB insertion and chip moving."},"release":{"code":"Available","weights":"Gated — official model access form required","data":"Partial / gated — ToucHD components have independent availability and access forms","license":"MIT code LICENSE; gated model/data artifact terms apply separately","notes":"README exposes pretraining/benchmark commands; quick_start and policy-learning sections include Coming Soon markers. Do not infer a complete released physical policy."},"evidence":[{"url":"https://arxiv.org/abs/2602.09617","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Method, experiments and implementation sections; specifications retain component and evaluation scope."},{"url":"https://gewu-lab.github.io/AnyTouch2/","label":"Official project","fields":["links","release"],"note":"Author project; author-reported release scope is distinguished from an executed reproduction."},{"url":"https://github.com/GeWu-Lab/AnyTouch2","label":"Official implementation and current release instructions","fields":["release","links"],"note":"README and repository files inspected on 2026-10-17."},{"url":"https://github.com/GeWu-Lab/AnyTouch2/blob/main/LICENSE","label":"Repository license file","fields":["release.license"],"note":"Applies to repository scope; model/data/base-model terms can differ."}],"caveats":["Force is training supervision from paired measurements, not a separate required wrist-force observation at encoder inference.","Encoder is reusable; the evaluated robot Diffusion Policy is an additional trained system.","Official weights and ToucHD-Force require access forms; public repository existence does not imply unrestricted artifact downloads."],"relatedIds":["anytouch","sparsh","t3"],"verified":"2026-10-17"},{"id":"retaf","name":"RETAF / TF-Gripper","title":"Learning Force-Regulated Robotic Manipulation with a Low-Cost Tactile-Force-Controlled Gripper","date":"2026-02-10","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"A separate high-rate tactile force policy regulates grasping while a base policy predicts arm pose and gripper open/close.","institution":"University of Virginia / Columbia University","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2602.10013","project":"https://force-gripper.github.io/","code":"https://github.com/Shua-Kang/force_gripper_hardware"},"policy":{"family":"Hybrid","baseModel":"REactive Tactile Adaptation of Force; modular pose policy plus wrist/tactile force adaptation policy.","modelSize":"Not reported","inputs":"Global/wrist RGB, tactile feedback and robot state; force branch activates during grasping.","outputs":"End-effector pose and open/close from base policy; continuous target grip force from reactive branch.","trainingData":"Six tasks with 50 human demonstrations/task, recorded at 15 Hz; separate chip-variant generalization cohort 20 demos/seen variant.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Force adaptation >30 Hz; demos 15 Hz; slower arm policy rate is separate.","trainingMethod":"Imitation learning with decoupled force/pose prediction and joint attention.","evaluation":"Six real force-sensitive tasks with TF-Gripper, including chip/cherry-tomato picking and liquid transfer."},"release":{"code":"Partial","weights":"Not verified","data":"Not verified","license":"Not verified; no unambiguous repository license identified.","notes":"Released repository covers TF-Gripper hardware/control; full RETAF policy training/checkpoints not established by this hardware repository."},"evidence":[{"url":"https://arxiv.org/abs/2602.10013","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://force-gripper.github.io/","label":"Official author project","fields":["links","release"],"note":"Live project artifact links inspected on2026-10-17."},{"url":"https://github.com/Shua-Kang/force_gripper_hardware","label":"Official released implementation and LICENSE","fields":["links","release"],"note":"Repository metadata,README and actualNone inspected; availability scoped to the listed component."}],"caveats":["Sensor feedback regulates grip force, not just an additional position observation.","Chip-variant cohort is a separate experiment and is not added to the six-task demo count."],"verified":"2026-10-17"},{"id":"robo3r","name":"Robo3R","title":"Robo3R: Enhancing Robotic Manipulation with Accurate Feed-Forward 3D Reconstruction","date":"2026-02-10","year":2026,"collections":["policy"],"categories":["geometry-models","rgbd-3d"],"resourceType":"model","summary":"Robot-conditioned feed-forward reconstruction provides metric geometry from RGB without a depth sensor.","institution":"Shanghai AI Laboratory; Chinese University of Hong Kong; USTC; Tsinghua University","venue":"arXiv preprint","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2602.10101","project":"https://yangsizhe.github.io/robo3r/","code":"https://github.com/InternRobotics/Robo3R","data":"https://huggingface.co/datasets/yangsizhe/Robo3R","weights":"https://huggingface.co/yangsizhe/Robo3R"},"release":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 for repository code; pretrained models, data and figure rights remain separate","notes":"Official README links published Robo3R geometry checkpoints and Robo3R-4M data; downstream task policy artifacts are separate."},"evidence":[{"url":"https://arxiv.org/abs/2602.10101","label":"Original manuscript and first submission history","fields":["title","date","summary","model","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://github.com/InternRobotics/Robo3R","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/InternRobotics/Robo3R/blob/main/LICENSE.txt","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/InternRobotics/Robo3R/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"73 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."},{"url":"https://huggingface.co/yangsizhe/Robo3R","label":"Official weights release destination","fields":["release.weights"],"note":"Destination linked by the official project/repository; component release scope is retained in release notes."},{"url":"https://huggingface.co/datasets/yangsizhe/Robo3R","label":"Official data release destination","fields":["release.data"],"note":"Destination linked by the official project/repository; component release scope is retained in release notes."}],"caveats":["Geometry is RGB/robot-prior inferred; depth-sensor comparisons do not make model output a measured observation.","Downstream agents/planners remain separate from the reconstruction model."],"verified":"2026-10-17","model":{"role":"Robot-grounded metric 3D reconstruction front end","family":"Geometry representation","baseModel":"Frozen DINOv2 ViT-L and π³-initialized 18-block alternating-attention transformer; robot-state fusion, masked point heads and robot-keypoint PnP.","modelSize":"Not reported in the inspected primary-source sections","inputs":"RGB images and robot states/kinematic geometry.","outputs":"Metric scene geometry and camera extrinsics aligned to the robot frame.","trainingMethod":"Synthetic geometry supervision and robot-grounded reconstruction learning.","trainingData":"Robo3R-4M: four million annotated synthetic frames.","trainingTime":"Approximately six days","trainingHardware":"32 NVIDIA RTX 4090 GPUs","evaluation":"Downstream imitation learning, sim-to-real, grasp synthesis and collision-free planning with substituted geometry.","inference":"Downstream system configured at 10 Hz on RTX 4090; reconstruction-only inference timings depend on view count (see Appendix C)."}},{"id":"univtac","name":"UniVTAC","title":"UniVTAC: A Unified Simulation Platform for Visuo-Tactile Manipulation Data Generation, Learning, and Benchmarking","date":"2026-02-10","year":2026,"collections":["benchmark"],"categories":["tactile-benchmarks","precision-assembly"],"resourceType":"benchmark","summary":"Contact-rich data synthesis, tactile representation learning and an eight-task closed-loop evaluation suite with three sensor designs.","institution":"Nanjing University / D-Robotics","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2602.10093","project":"https://univtac.github.io/","code":"https://github.com/univtac/UniVTAC","data":"https://modelscope.cn/datasets/byml2024/UniVTAC"},"benchmark":{"simulator":"Isaac Sim / Isaac Lab, modified TacEx and UIPC","simulatorVersion":"main: Isaac Sim 4.5.0 / Isaac Lab 2.1.1; isaac51 branch: Isaac Sim 5.1.0 / Isaac Lab 2.3.0. Data are version-specific.","taskCount":"8 simulation benchmark tasks; 3 physical transfer tasks","tasks":["Lift Bottle","Lift Can","Put Bottle in Shelf","Grasp Classify","Insert Hole","Insert Tube","Insert HDMI","Pull Out Key"],"taskListScope":"Complete eight-task simulation suite; physical paper tasks: Insert Tube, Insert USB, Bottle Upright.","capabilities":["Pose reasoning","Shape perception","Contact-rich interaction"],"metrics":"Success rate per task and unweighted average; tactile encoder pathway ablation.","robots":"Simulation Franka Panda parallel gripper; physical Tianji Marvin with ViTai GF225.","sensors":"GelSight Mini, ViTai GF225 and Xense WS supported by platform. Current public collection/evaluation README limits released task workflow to GelSight Mini; head and wrist visual streams; tactile RGB / marker motion / geometry supervision.","dataCollection":"Scripted simulation experts use ground-truth feedback with injected failures and contact-based corrections. Physical task demonstrations are separate; their teleoperation interface is Not reported.","data":"Paper: 50 automatically collected full trajectories/task for policy training; 100 test rollouts/task. Public isaac45 and isaac51 folders each advertise 100 HDF5 episodes/task; contact-pretraining data and 4.5 policy checkpoints also provided. Physical study collects 150 demonstrations/task.","protocol":"Scripted expert synthesis intentionally injects failures/corrections for contact variation; eight simulation tasks use identical 50-demo budgets; real policies trained separately and evaluated for 20 rollouts/task.","baselines":"ACT vision-only; VITaL; ACT + UniVTAC Encoder; from-scratch and contact / shape / pose pretraining ablations."},"release":{"code":"Available","weights":"Partial","data":"Available","license":"Apache-2.0 code; dataset/model terms separate","notes":"Code, task data for two simulator stacks and representation/policy artifacts public. Released policy checkpoints are for Isaac Sim 4.5 only."},"evidence":[{"url":"https://arxiv.org/abs/2602.10093","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://univtac.github.io/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/univtac/UniVTAC","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://github.com/univtac/UniVTAC/blob/main/LICENSE","label":"Official source","fields":["release.license"],"note":"License text read directly; code license does not automatically cover models, data or simulator."},{"url":"https://arxiv.org/abs/2602.10093","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Current main and isaac51 data/checkpoints are not interchangeable.","Sensor support in the platform exceeds sensor support in the released task collection/evaluation workflow.","External RGB-D export support does not establish that benchmark baselines consume metric depth.","README calls the code MIT, while the checked-in LICENSE is Apache-2.0; this catalogue follows LICENSE."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Partial","data":"Available","license":"Apache-2.0 code; dataset/model terms separate","notes":"Code, task data for two simulator stacks and representation/policy artifacts public. Released policy checkpoints are for Isaac Sim 4.5 only."}},"relatedIds":[]},{"id":"vla-jepa","name":"VLA-JEPA","title":"VLA-JEPA: Enhancing Vision-Language-Action Model with Latent World Model","date":"2026-02-10","year":2026,"collections":["policy"],"categories":["generalist-foundation","visual-precision-rl"],"resourceType":"policy","summary":"Latent predictive alignment uses action-free human video and robot demonstrations to ground action tokens in future latent dynamics rather than pixel reconstruction.","institution":"University of Science and Technology of China / Zhongguancun Academy / collaborating institutions","venue":"ECCV 2026","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2602.10098","project":"https://ginwind.github.io/VLA-JEPA/","code":"https://github.com/ginwind/VLA-JEPA","weights":"https://huggingface.co/ginwind/VLA-JEPA"},"policy":{"family":"VLA","baseModel":"Qwen3-VL-2B + V-JEPA2 video encoder/causal latent predictor + flow-matching DiT-B robot action head.","modelSize":"2B VLM backbone; complete model/component aggregate not reported. Latent predictor and action-head architecture are separately disclosed in AppendixA.","inputs":"RGB observations, language instruction and robot state at deployment. Future video/latent targets are training supervision, not future observations supplied to the deployed policy.","outputs":"Continuous 7D end-effector action sequences in the reported configuration; future action horizon 7, 4 flow denoising steps (Table6).","trainingData":"Joint pretraining on Something-SomethingV2 human video and DROID; simulation downstream data includes LIBERO, BridgeV2 and Fractal; physical post-training100 demonstrations across 3 tasks. No summed nonoverlapping dataset total reported.","trainingTime":"Wall-clock duration not reported. Joint video/robot pretraining50 K steps; simulation adaptation30 K steps; real adaptation20 K steps (AppendixA.2).","trainingHardware":"8 NVIDIA A100 GPUs; batch 32 perGPU/global 256 in AppendixA.2.","controlRate":"Not reported","trainingMethod":"One-stage latent action/world-model predictive alignment on action-free and action-labeled data; joint latent dynamics and flow action objectives during downstream fine-tuning.","evaluation":"LIBERO and SimplerEnv manipulation, LIBERO-Plus robustness, and Franka Research3/Robotiq2F-85 physical tasks and OOD object/task tests."},"release":{"code":"Partial","weights":"Available","data":"Partial","license":"Apache-2.0 original code with explicit third-party notices; model card Apache-2.0. Qwen, V-JEPA2, starVLA and datasets retain their own terms.","notes":"Official README marks partial training code plus LIBERO, LIBERO-Plus, SimplerEnv/custom-data tooling available. Pretraining and task checkpoints are public; upstream dataset references have separate availability."},"evidence":[{"url":"https://arxiv.org/abs/2602.10098","label":"Paper and first-public arXiv record","fields":["title","date","institution","policy","modalities","embodiments","environments"],"note":"Technical fields follow the inspected full manuscript and appendices; date is arXiv v1, not the current revision."},{"url":"https://ginwind.github.io/VLA-JEPA/","label":"Official project","fields":["summary","imageCandidates","release"],"note":"Current author project and release links."},{"url":"https://github.com/ginwind/VLA-JEPA","label":"Official repository","fields":["release","policy.baseModel","policy.inputs","policy.evaluation","imageCandidates"],"note":"Current implementation/release scope can differ from paper experiments."},{"url":"https://github.com/ginwind/VLA-JEPA/blob/main/LICENSE","label":"Original-code Apache-2.0 license","fields":["release.license"]}],"caveats":["V-JEPA2 supervises latent dynamics and is not an independent measured depth input.","2B describes the VLM backbone rather than the entire training/inference system.","Current code has revision-specific training settings; reproduce a pinned version and matching checkpoint."],"verified":"2026-10-17"},{"id":"mint","name":"MINT","title":"Mimic Intent, Not Just Trajectories","date":"2026-02-09","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","generalist-foundation"],"resourceType":"policy","summary":"Spectrally disentangled action tokens separate global intent from execution details, enabling one-shot skill transfer by reusing a demonstration intent token.","institution":"Shanghai Jiao Tong University / Shanghai Innovation Institute","venue":"RSS 2026","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2602.08602","project":"https://renming-huang.github.io/MINT","code":"https://github.com/RenMing-Huang/MINT","weights":"https://huggingface.co/huangrm/MINT-libero"},"policy":{"family":"VLA","baseModel":"Multi-scale spectral action tokenizer and next-scale autoregressive policy; small scratch-trained and pretrained VLM variants.","modelSize":"MINT-30M and MINT-4B are separate reported model variants.","inputs":"Robot visual/state observations and task conditioning; a demonstration supplies the intent token for one-shot transfer.","outputs":"Coarse-to-fine action tokens decoded into continuous robot action trajectories.","trainingData":"Not reported","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Learn multi-scale frequency-space action reconstruction, then next-scale action generation; inject a demonstration intent token at deployment.","evaluation":"Multiple simulated manipulation benchmarks and real robot generalization / one-shot skill transfer."},"release":{"code":"Available","weights":"Available","data":"Partial","license":"MIT for repository code; external model, dataset and dependency terms remain separate.","notes":"Official repository releases MINT-4B training/evaluation, action-tokenizer training, MINT-30M and one-shot transfer; its model zoo includes LIBERO, CALVIN, Bridge and RoboTwin policy/tokenizer variants."},"evidence":[{"url":"https://www.roboticsproceedings.org/rss22/p206.html","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://renming-huang.github.io/MINT","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/RenMing-Huang/MINT/blob/HEAD/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Actual license file inspected; code license does not automatically license data or weights."},{"url":"https://github.com/RenMing-Huang/MINT","label":"Verified official code artifact","fields":["links.code","release.code"],"note":"Author project / repository cross-link verified; current release distinguished from original paper experiments."},{"url":"https://huggingface.co/huangrm/MINT-libero","label":"Verified official weights artifact","fields":["links.weights","release.weights"],"note":"Author project / repository cross-link verified; current release distinguished from original paper experiments."}],"caveats":["The original arXiv submission predates the RSS proceedings.","30M and 4B are alternatives, not a summed component count."],"verified":"2026-10-17"},{"id":"step","name":"STEP","title":"STEP: Warm-Started Visuomotor Policies with Spatiotemporal Consistency Prediction","date":"2026-02-09","year":2026,"collections":["policy"],"categories":["visual-precision-rl"],"resourceType":"policy","summary":"A lightweight spatiotemporal predictor warm-starts action diffusion; velocity-aware perturbations counter execution stalls during few-step refinement.","institution":"Shanghai Jiao Tong University / Shanghai Innovation Institute / Infinigence-AI","venue":"ICML 2026","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2602.08245","code":"https://github.com/Kimho666/STEP"},"policy":{"family":"Diffusion","baseModel":"CNN-based Diffusion Policy with a cross-attention action predictor conditioned on current observations and previous action chunks.","modelSize":"Predictor trainable parameters0.98M (Table5); Appendix A uses a 256M-parameter DDPM backbone. Counts refer to separate components.","inputs":"Current RGB observations and robot pose/state, plus historical actions for image-based experiments; state-only experiments evaluated separately.","outputs":"Continuous action chunks; prediction horizon 16 and 2 observations, with a16-action execution chunk in real experiments. Few-step diffusion refines predictor initialization.","trainingData":"Push-T, RoboMimic and ManiSkill2 demonstrations under their official baseline settings; two SO-ARM101 real task datasets. Aggregate trajectory count not reported.","trainingTime":"Wall-clock duration not reported. Appendix A: diffusion policy 200K iterations and predictor 100K iterations, batch 64 each.","trainingHardware":"One NVIDIA RTX4090 for training and simulation inference. Real inference on RTX2050 (4GB, 25–35W); do not confuse inference with training hardware.","controlRate":"Real2-step inference 19–20 ms per action sequence on RTX2050 (Table6); physical observations around30 Hz. Action-sequence latency is not a physical command frequency.","trainingMethod":"Train diffusion policy and predictor separately; supervised action prediction provides a warm start; apply velocity-aware perturbation and 2–4 denoising refinement steps.","evaluation":"9 simulated tasks across Push-T, RoboMimic and ManiSkill2; real PickNPlace and StackCube on SO-ARM101."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT code; underlying Diffusion Policy, datasets and externally supplied checkpoints retain their own terms.","notes":"Training/evaluation and checkpoint-conversion helpers available; converting a base policy does not establish a released trained predictor checkpoint."},"evidence":[{"url":"https://arxiv.org/abs/2602.08245","label":"Paper and first-public arXiv record","fields":["title","date","institution","policy","modalities","embodiments","environments"],"note":"Technical fields follow the inspected full manuscript and appendices; date is arXiv v1, not the current revision."},{"url":"https://github.com/Kimho666/STEP","label":"Official repository","fields":["release","policy.baseModel","policy.inputs","policy.evaluation","imageCandidates"],"note":"Current implementation/release scope can differ from paper experiments."},{"url":"https://github.com/Kimho666/STEP/blob/main/LICENSE","label":"MIT code license","fields":["release.license"]}],"caveats":["Published0.98M count covers the predictor, not the complete deployed system.","The predictor and diffusion backbone incur separate training; the paper reports iteration budgets rather than elapsed training time."],"verified":"2026-10-17"},{"id":"world-vla-loop","name":"World-VLA-Loop","title":"World-VLA-Loop: Closed-Loop Learning of Video World Model and VLA Policy","date":"2026-02-06","year":2026,"collections":["policy"],"categories":["agent-rsi-icl","visual-precision-rl"],"resourceType":"policy","summary":"A reward-predicting video world model and VLA co-improve using both successful and near-success interaction trajectories.","institution":"National University of Singapore","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2602.06508","project":"https://showlab.github.io/World-VLA-Loop/","code":"https://github.com/showlab/World-VLA-Loop"},"policy":{"family":"Hybrid","baseModel":"Cosmos Predict 2 action-conditioned world model and an OpenVLA-OFT policy with world-model reward prediction.","modelSize":"Not reported","inputs":"Robot RGB/state observations, task context and action-conditioned world-model rollouts during training.","outputs":"VLA robot actions; world model jointly predicts future frames and binary rewards.","trainingData":"SANS pretraining contains 35k video/action pairs over 23 ManiSkill tasks, plus LIBERO and real success/near-success rollout data.","trainingTime":"Example task world-model fine-tuning: about four hours on eight H100s. Per-task RL sessions: 20–40 optimization steps / about 25–35 hours. These are separate stages.","trainingHardware":"Eight H100 GPUs for reported task world-model adaptation; single-H100 generation timing is a separate inference measurement.","controlRate":"Not reported","trainingMethod":"Train state-aware video/reward prediction; iterative policy RL in the learned world and subsequent model refinement.","evaluation":"Simulation and real manipulation with near-success failures and changing policy rollout distributions."},"release":{"code":"Partial","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Official repository identified; model/data release scope is not inferred from the paper alone."},"evidence":[{"url":"https://arxiv.org/abs/2602.06508","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://showlab.github.io/World-VLA-Loop/","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/showlab/World-VLA-Loop","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."}],"caveats":["Task-adaptation time does not include complete upstream Cosmos or VLA pretraining.","This is model-based policy improvement, not direct real-world online RL with equal rollout cost."],"verified":"2026-10-17"},{"id":"deco","name":"DECO","title":"DECO: Decoupled Multimodal Diffusion Transformer for Bimanual Dexterous Manipulation with a Plugin Tactile Adapter","date":"2026-02-05","year":2026,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"Decouples visual, proprioceptive and tactile conditioning; a small tactile adapter and LoRA adapt a pretrained bimanual policy.","institution":"BAAI","venue":"ICML 2026","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2602.05513","project":"https://baai-humanoid.github.io/DECO-webpage/","code":"https://github.com/BAAI-Humanoid/DECO","data":"https://huggingface.co/datasets/BAAI-Humanoid/DECO-50"},"policy":{"family":"Diffusion","baseModel":"ResNet-34 + six-layer multimodal DiT with flow matching","modelSize":"DECO 83.05M; DECO.p 91.02M total / 7.97M trainable","inputs":"Binocular RGB, robot joint state and optional bimanual tactile taxels","outputs":"32-step action chunks; typically executes 16","trainingData":"DECO-50: >50 h, ≈8k successful trajectories, >5M frames, four scenarios / 28 subtasks","trainingTime":"Paper: 150 epochs DECO/DECO.p; current README example 200; wall-clock not reported","trainingHardware":"8 A100 40GB GPUs","controlRate":"Not reported","trainingMethod":"Visual/proprioceptive pretraining; frozen-backbone tactile adapter training with LoRA","evaluation":"Four real bimanual scenarios; released sim branches for LIBERO, DexJoCo, UniVTAC and RoboTwin"},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"Apache-2.0 for repository; dataset terms separate","notes":"Training/deployment and multiple simulation branches available; checkpoint download not independently verified."},"evidence":[{"url":"https://arxiv.org/abs/2602.05513","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/BAAI-Humanoid/DECO","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2602.05513","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Binocular RGB is not explicit depth input.","Paper and current repository training epoch recipes differ."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Available","license":"Apache-2.0 for repository; dataset terms separate","notes":"Training/deployment and multiple simulation branches available; checkpoint download not independently verified."}},"relatedIds":[]},{"id":"vt-wm","name":"VT-WM","title":"Visuo-Tactile World Models","date":"2026-02-05","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Action-conditioned visual/tactile world models provide contact-aware imagined rollouts and evaluate real robot plan transfer.","institution":"FAIR, Meta / University of Washington","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2602.06001"},"policy":{"family":"Model-based planning","baseModel":"Cosmos visual tokenizer, Sparsh-X tactile encoder and autoregressive multimodal transformer.","modelSize":"173 M parameters total; 96 M trained.","inputs":"Exo centric RGB, four Digit 360 tactile sensor streams and candidate proprioceptive action sequences.","outputs":"Predicted visual/tactile states used to select action sequences for robot execution.","trainingData":"124 demonstrations / 112 k data points across eight tasks for training; 26 demonstrations / 17 k data points validation. Novel plate-in-rack adaptation uses 20 demos.","trainingTime":"Not reported","trainingHardware":"32 NVIDIA A100 GPUs; effective batch 64.","controlRate":"World model 6 fps; recorded/executed actions grouped from 30 Hz.","trainingMethod":"80 k updates; tactile encoder adapted while Cosmos is frozen; action-conditioned dynamics with planning experiments.","evaluation":"Allegro hand / Digit 360: eight training task families; zero-shot open-loop plan execution and few-shot novel plate-rack planning."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2602.06001","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."}],"caveats":["Planning transfer includes open-loop execution; do not label all experiments closed-loop policy feedback."],"verified":"2026-10-17"},{"id":"rdt2","name":"RDT2","title":"RDT2: Exploring the Scaling Limit of UMI Data Towards Zero-Shot Cross-Embodiment Generalization","date":"2026-02-03","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Uses large-scale handheld-gripper data and discrete-to-continuous action training for cross-embodiment deployment.","institution":"Tsinghua University","venue":"Technical report","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2602.03310","project":"https://rdt-robotics.github.io/rdt2/","code":"https://github.com/thu-ml/RDT2","weights":"https://huggingface.co/collections/robotics-diffusion-transformer/rdt-2-68ce9ddbf7dc520a231220d5"},"policy":{"family":"VLA","baseModel":"Qwen2.5-VL-7B; RVQ tokenizer (RDT2-VQ) and flow-matching RDT action expert (RDT2-FM)","modelSize":"7B VLM backbone plus action/tokenizer modules; exact total not separately reported","inputs":"Wrist RGB views, instruction and end-effector/gripper state under the prescribed UMI interface","outputs":"Relative end-effector/gripper action chunks; 0.8 s /24 steps at 30 Hz; RVQ compresses to 27 tokens","trainingData":"10k+ h human UMI demonstrations in100+ indoor/home scenes; VQA mixture for semantic alignment","trainingTime":"Stage-specific step schedules in paper; total elapsed time not reported","trainingHardware":"Paper ablation: seven nodes × 8 GPUs; full-run GPU model/count not clearly reported","controlRate":"30 Hz action representation; one-step distilled variant for dynamics; query rate hardware-dependent","trainingMethod":"Stage1 autoregressive RVQ pretraining; Stage2 flow expert; Stage3 optional one-step distillation","evaluation":"Zero-shot simple open-vocabulary tasks on unseen robots; task fine-tuning for dynamic and precise manipulation"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2602.03310","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/thu-ml/RDT2","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://rdt-robotics.github.io/rdt2/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/thu-ml/RDT2/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["Zero-shot transfer assumes specified camera/end-effector geometry and hardware alignment.","The 10k h source is handheld UMI data, not conventional robot teleoperation hours.","Seven-node compute quoted here is an ablation setting, not established total model-pretraining cost."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"uniforce","name":"UniForce","title":"UniForce: A Unified Latent Force Model for Robot Manipulation with Diverse Tactile Sensors","date":"2026-02-01","year":2026,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"A shared force-grounded tactile latent aligns heterogeneous sensors through quasi-static equilibrium, with downstream force prediction and VLA wiping transfer.","institution":"King’s College London; Imperial College London; University College London; University of Bristol","venue":"arXiv preprint 2026","modalities":["Tactile"],"environments":["Real robot"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2602.01153"},"release":{"code":"Not released","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Primary paper says code/data will be released; the inspected author publication page links only the manuscript. No actual release destination was verified."},"model":{"role":"Cross-sensor force-grounded tactile representation","family":"Tactile encoder","baseModel":"Conditional VAE with a causal spatiotemporal Transformer; inverse/forward dynamics share a patch-wise latent. Downstream frozen encoder is integrated into π0.5.","modelSize":"Not reported in the inspected primary manuscript","inputs":"Reference/current tactile marker images; optical GelSight/TacTip and magnetic uSkin signals are canonicalized to a common image representation.","outputs":"196 spatial patches with six latent force coordinates each, reconstructed tactile observations and optional trained force predictions. Latent coordinates are not directly measured force units.","trainingMethod":"Self/cross reconstruction, variational regularization and paired force-equilibrium constraints; no external F/T labels required for representation learning.","trainingData":"UniForce-pair: approximately 8K frames per GelSight–TacTip and uSkin–TacTip pair. Separate GenForce-Hetero data supports force probes; downstream wiping uses 50 task trajectories.","trainingTime":"Not reported in the inspected primary manuscript","trainingHardware":"Not reported in the inspected primary manuscript","inference":"Signal-to-marker preprocessing reported above 29.6 Hz; full encoder/VLA inference rate not reported.","evaluation":"Cross-sensor force estimation and reconstruction; frozen-encoder π0.5 wiping evaluated with single and bilateral sensor combinations, 20 rollouts per setting."},"evidence":[{"url":"https://arxiv.org/abs/2602.01153","label":"Original manuscript and first public submission history","fields":["title","date","institution","model","policy","modalities","environments","caveats"],"note":"Full author method, evaluation and appendix reviewed; downstream controller probes are distinguished from the reusable model."},{"url":"https://zhuochenn.github.io/#chen2026uniforce","label":"Lead-author publication listing","fields":["release","venue"],"note":"Lists the work as a preprint under review and exposes the manuscript link; no code or checkpoint destination appears for this entry."}],"caveats":["Primary role is a reusable tactile encoder; π0.5 provides the downstream action policy. No separate action-policy release is inferred.","Equilibrium supervision assumes slow bilateral contact; shear directions and transfers from low-resolution sensors have uneven performance."],"verified":"2026-10-17"},{"id":"any3dvla","name":"Any3D-VLA","title":"Any3D-VLA: Enhancing VLA Robustness via Diverse Point Clouds","date":"2026-01-31","year":2026,"collections":["policy"],"categories":["rgbd-3d","generalist-foundation"],"resourceType":"policy","summary":"Trains one 2D–3D fusion policy using simulator, sensor and model-estimated point clouds to reduce geometry-domain mismatch.","institution":"University of Hong Kong / Galbot / Peking University","venue":"ICML 2026","modalities":["RGB","Depth","Language","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2602.00807","project":"https://github.com/XianzheFan/Any3D-VLA","code":"https://github.com/XianzheFan/Any3D-VLA","weights":"https://huggingface.co/XianzheFan/Any3D-VLA"},"policy":{"family":"VLA","baseModel":"GraspVLA-style VLM/action framework with Concerto point encoder and gated patch-wise 2D–3D fusion; π0.5 variant separately studied","modelSize":"Not reported as a single complete model count","inputs":"RGB, point clouds, language and state; measured RealSense depth and RGB-estimated depth are separately evaluated point-cloud sources","outputs":"Continuous robot action chunks using the selected VLA head","trainingData":"Large synthetic RGB-D mixture; hybrid simulator/sensor/model geometry training; LIBERO/CALVIN and physical adaptation settings separate","trainingTime":"Three days for final reported model","trainingHardware":"16 H800 GPUs, global batch 128","controlRate":"RTX 3090: 2.0 FPS with RealSense, 1.7 with Depth Anything 3, 0.5 with UniDepthV2, 0.3 with MapAnything; geometry preprocessing included by setting","trainingMethod":"Concerto mostly frozen except final sparse-convolution layers; gated geometry corrections and mixed point-cloud-source training","evaluation":"Franka Panda zero-shot and posttraining; shape/scale/viewpoint/appearance changes; LIBERO and CALVIN"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"MIT code and author checkpoint; third-party encoders/data retain terms","notes":"Availability concerns author-linked artifacts; no training or installation reproduced."},"evidence":[{"url":"https://arxiv.org/abs/2602.00807","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/XianzheFan/Any3D-VLA","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://github.com/XianzheFan/Any3D-VLA","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."}],"caveats":["Measured-depth and predicted-geometry variants are separate observation settings.","The π0.5-backbone appendix experiment is distinct from the main architecture."],"verified":"2026-10-17"},{"id":"clamp","name":"CLAMP","title":"CLAMP: Contrastive Learning for 3D Multi-View Action-Conditioned Robotic Manipulation Pretraining","date":"2026-01-31","year":2026,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Contrastively pretrains multi-view 3D, action-history and text representations, then supplies frozen features to a diffusion policy.","institution":"Google DeepMind / University of Southern California","venue":"RSS 2026","modalities":["RGB","Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2602.00937","project":"https://clamp3d.github.io/CLAMP/","code":"https://github.com/clamp3d/CLAMP"},"policy":{"family":"Diffusion","baseModel":"STRING ViT-B/16 3D image encoder, action/text encoders and Transformer Diffusion Policy with ResNet-50 RGB backbones","modelSize":"Image encoder 94M, text encoder 277M, action encoder 18M; policy 192M. These are separately scoped components.","inputs":"RGB-D and camera geometry form merged point clouds and virtual depth/XYZ views; RGB, proprioception and action history condition the downstream policy. Text encoder is a pretraining component.","outputs":"50-step chunks of 14 absolute target joint positions","trainingData":"Simulated GR1.5 task demonstrations and real ALOHA training data; policy and representation pretraining use separate matched data recipes","trainingTime":"Policy training 1M steps; representation pretraining has a separate recipe; wall-clock Not reported","trainingHardware":"Policy training one H100, batch eight; representation pretraining hardware Not reported here","controlRate":"Not reported","trainingMethod":"Image/action/text contrastive pretraining; independent policy pretraining followed by feature-conditioned fine-tuning","evaluation":"Simulation multi-task/low-data evaluation and physical ALOHA precision/manipulation tasks"},"release":{"code":"Partial","weights":"Not verified","data":"Not verified","license":"Not verified; pretrained backbones and data retain separate terms","notes":"Public author repository is primarily a project page; complete internal JAX training implementation not verified."},"evidence":[{"url":"https://arxiv.org/abs/2602.00937","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/clamp3d/CLAMP","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://clamp3d.github.io/CLAMP/","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."}],"caveats":["Text/scene metadata used in contrastive pretraining are not automatically deployment observations.","Both encoder and policy initialization matter; encoder-only initialization is a separate ablation."],"verified":"2026-10-17"},{"id":"lvtg-contrastive-act","name":"LVTG — contrastive ACT","title":"Cross-Modal Visuo-Tactile Representation Learning with Action Chunking Transformers for Contact-Rich Manipulation","date":"2026-01-31","year":2026,"collections":["policy"],"categories":["tactile-il","tactile-foundation"],"resourceType":"policy","summary":"Cross-modal RGB/tactile contrastive pretraining improves ACT control on a low-cost tactile gripper.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2602.00514"},"policy":{"family":"ACT","baseModel":"CLIP-style ResNet 18 visual/tactile embedding alignment plus ACT.","modelSize":"Not reported","inputs":"External RGB, calibrated LVTG tactile images and robot state.","outputs":"Action chunks for contact-rich gripper manipulation.","trainingData":"1,000 teleoperated trajectories/task across five tasks=5,000 trajectories; paired frames for contrastive pretraining use the same pool.","trainingTime":"Not reported","trainingHardware":"Single NVIDIA RTX4090accelerates representation/policy training and inference.","controlRate":"RGB/touch 30 Hz; inference<20 ms average.","trainingMethod":"Frozen visual backbone/tactile contrastive adaptation, then ACT imitation.","evaluation":"COBOTMagic Robot with LVTG; five representative contact tasks including Plate Wiping."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2602.00514","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."}],"caveats":["Current paper title differs from the original hardware-focused title in Awesome-Touch; samear Xiv ID retained.","Do not add frame-pair pretraining examples to trajectory counts as separate episodes."],"verified":"2026-10-17"},{"id":"gem3d","name":"GEM3D","title":"Learning Geometrically-Grounded Amodal 3D Representations for View-Generalizable Robotic Manipulation","date":"2026-01-30","year":2026,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Multi-view reconstruction supervision distills amodal geometry into a calibrated single-view RGB-D manipulation policy.","institution":"Tongji University; University of Hong Kong; Shanghai Jiao Tong University","venue":"arXiv preprint","modalities":["RGB","Depth","Point cloud","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2601.22988"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2601.22988","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."}],"caveats":["Single-view means one calibrated RGB-D view, not RGB-only sensing.","Point reconstruction/rendering teachers are training-only; measured depth is still required at rollout."],"verified":"2026-10-17","policy":{"family":"Transformer / BC","baseModel":"PerceiverIO / PerAct-style keyframe policy; DINOv2 volumetric 3D U-Net teacher trained with point reconstruction and feed-forward Gaussian splatting.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Single RGB-D view, intrinsics/extrinsics, language and robot state.","outputs":"Keyframe manipulation actions.","trainingMethod":"Amodal 3D representation pretraining and multi-step geometric distillation during imitation.","trainingData":"Pretraining: 50 demos with eight views; policy: 20 demonstrations per single task in Table B.2.","trainingTime":"Pretraining 50K steps; single-task policy 12K steps; wall-clock not reported","trainingHardware":"Eight GPUs in pretraining table; GPU model not reported","evaluation":"Twelve RLBench tasks, view-shift evaluation subsets, and three physical tasks across three viewpoints; subset counts vary between manuscript versions.","controlRate":"1.28 Hz on RTX 3090 in RLBench open-drawer profiling."}},{"id":"lingbot-va","name":"LingBot-VA","title":"Causal World Modeling for Robot Control","date":"2026-01-29","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Autoregressive video-action modeling uses causal memory and asynchronous inference to couple predicted dynamics with feedback control.","institution":"Robbyant / Ant Group","venue":"RSS 2026","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2601.21998","project":"https://technology.robbyant.com/lingbot-va","code":"https://github.com/Robbyant/lingbot-va","data":"https://huggingface.co/datasets/robbyant/robotwin-clean-and-aug-lerobot","weights":"https://huggingface.co/collections/robbyant/lingbot-va"},"policy":{"family":"World-action model","baseModel":"Wan2.2-5B video stream and 350M action stream coupled through MoT; frozen T5 instruction encoder.","modelSize":"5.3B video/action model; text encoder and VAE are separately scoped components.","inputs":"RGB views, language, proprioception and causal observed-frame history.","outputs":"Joint future visual latents and continuous actions through a common cross-embodiment interface.","trainingData":"Approximately 16k hours of robot manipulation; RoboTwin posttraining uses 2,500 clean plus 25,000 randomized demonstrations.","trainingTime":"50k RoboTwin posttraining steps; full elapsed training duration not reported.","trainingHardware":"Full training resources not reported in the inspected source.","controlRate":"RoboTwin action data at 50 Hz, with video downsampled to 12.5 Hz. These are recording/execution rates, not forward-pass frequency.","trainingMethod":"Causal autoregressive flow matching, multi-chunk teacher forcing, KV-cache regrounding and asynchronous execution.","evaluation":"LIBERO, RoboTwin 2.0 and physical long-horizon, precision, deformable and articulated-object tasks."},"release":{"code":"Available","weights":"Available","data":"Partial","license":"Apache-2.0 per author release; backbone/model terms separate","notes":"Availability concerns author-linked artifacts; no training or installation reproduced."},"evidence":[{"url":"https://arxiv.org/abs/2601.21998","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/Robbyant/lingbot-va","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://technology.robbyant.com/lingbot-va","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."}],"caveats":["LingBot-VA 2.0 is a distinct later paper and architecture."],"verified":"2026-10-17"},{"id":"taf-vla","name":"TaF-VLA","title":"TaF-VLA: Tactile-Force Alignment in Vision-Language-Action Models for Force-aware Manipulation","date":"2026-01-28","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Aligns sequential tactile observations with force dynamics and supplies the force-grounded latent tokens to π0.5.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2601.20321","code":"https://github.com/mrHuangyz/TaF-VLA"},"policy":{"family":"VLA","baseModel":"π0.5 plus TaF-Adapter tactile ViT and force codebook","modelSize":"Not reported","inputs":"RGB, language, proprioception and tactile-image history","outputs":"Robot action chunks","trainingData":"TaF-Dataset: >10M synchronized tactile/force/map samples; downstream data subsets 100/150/200 demos","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Force-codebook training, tactile–force contrastive alignment, then policy adaptation","evaluation":"Seven force-critical tasks including tube insertion, whiteboard erasing, jelly slicing and chocolate grasping"},"release":{"code":"Partial","weights":"Not verified","data":"Not verified","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Author repository is public; checked download scope does not establish complete policy/checkpoint availability."},"evidence":[{"url":"https://arxiv.org/abs/2601.20321","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/mrHuangyz/TaF-VLA","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2601.20321","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."},{"url":"https://github.com/mrHuangyz/TaF-VLA/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["Six-axis force and force maps supervise adapter pretraining; direct force input at deployment is not assumed.","Inference RTX 4090 is not evidence for training hardware."],"verified":"2026-10-19","releaseByCollection":{"policy":{"code":"Partial","weights":"Not verified","data":"Not verified","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Author repository is public; checked download scope does not establish complete policy/checkpoint availability."}},"relatedIds":[]},{"id":"touchguide","name":"TouchGuide","title":"TouchGuide: Inference-Time Steering of Visuomotor Policies via Touch Guidance","date":"2026-01-28","year":2026,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"A task-specific tactile contact model steers frozen diffusion/flow policy sampling in action space at inference.","institution":"Shanghai Jiao Tong University / Xense Robotics / Shanghai AI Laboratory and collaborators","venue":"RSS 2026","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2601.20239","project":"https://martelzhang.github.io/touchguide/","code":"https://github.com/martelzhang/TouchGuide"},"policy":{"family":"Hybrid","baseModel":"Diffusion Policy or π0.5 base; contrastively trained Contact Physical Model (CPM).","modelSize":"Not reported","inputs":"RGB, tactile images or force feedback and robot state; optional language inherited from the base policy.","outputs":"Tactile-guided refinement of candidate action chunks.","trainingData":"Limited task-specific demonstrations; Lock Opening uses 20 demos; five-task evaluation with Tac UMI collection.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Tac UMI RGB/tactile recording 30 Hz; deployment rate depends on base policy.","trainingMethod":"Train CPM on synchronized positive and temporally mismatched negative observation-action pairs; base policy is not retrained for touch guidance.","evaluation":"Five contact-rich tasks including Shoe Lacing, Chip Handover and Lock Opening; cross-policy/robot/tactile-modality studies."},"release":{"code":"Partial","weights":"Not verified","data":"Not verified","license":"MIT","notes":"Official project labels repository Data Collection Code. Policy guidance/training release is not inferred from the sensor-interface release."},"evidence":[{"url":"https://arxiv.org/abs/2601.20239","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://martelzhang.github.io/touchguide/","label":"Official author project","fields":["links","release"],"note":"Live artifact status reviewed separately from paper claims."},{"url":"https://github.com/martelzhang/TouchGuide","label":"Official released implementation and LICENSE","fields":["links","release"],"note":"Repository metadata,README and actualLICENSE inspected; availability scoped to the listed component."}],"caveats":["Inference-time gradient steering is not an online policy weight update.","A task-specific CPM must be learned; this is not training-free tactile understanding."],"verified":"2026-10-17"},{"id":"lingbot-vla","name":"LingBot-VLA","title":"A Pragmatic VLA Foundation Model","date":"2026-01-26","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Scales a Qwen-based flow policy with real robot data and optional depth-representation distillation.","institution":"Robbyant / Ant Group","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2601.18692","project":"https://technology.robbyant.com/lingbot-vla","code":"https://github.com/robbyant/lingbot-vla","data":"https://huggingface.co/datasets/robbyant/gm100","weights":"https://huggingface.co/robbyant/lingbot-vla-4b"},"policy":{"family":"VLA","baseModel":"Qwen2.5-VL-3B with MoT flow action expert; optional LingBot-Depth representation distillation","modelSize":"Released checkpoints labelled 4B; VLM backbone is 3B","inputs":"Three RGB views, language and robot state; depth-distilled variant learns geometry from an auxiliary teacher","outputs":"Continuous bimanual action chunks via flow matching","trainingData":"Approximately 20,000 hours of real robot manipulation across 9 dual-arm configurations; GM100 benchmark release","trainingTime":"Full pretraining wall-clock not reported; throughput study reports 261 samples/s on 8-GPU setting","trainingHardware":"Throughput comparisons span 8–256 GPUs; full model-training accelerator budget not reported","controlRate":"Not reported; training samples/s is not robot control frequency","trainingMethod":"Large-scale robot pretraining, optional visual-depth distillation and platform/task post-training","evaluation":"100 tasks on each of four real robot platforms, 130 post-training episodes/task; RoboTwin2 and GM100"},"release":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2601.18692","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/robbyant/lingbot-vla","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."},{"url":"https://raw.githubusercontent.com/robbyant/lingbot-vla/main/LICENSE","label":"Repository license file","fields":["release.license"],"note":"Code scope; model, data and third-party component terms can differ."}],"caveats":["Depth-distilled checkpoint does not by itself establish measured depth-camera input; RGB views drive the inspected architecture.","The selected manuscript teaser includes the method architecture; detailed architecture figure is missing from HTML rendering and can be replaced by the PDF figure."],"verified":"2026-10-17"},{"id":"lingbot-depth","name":"LingBot-Depth","title":"Masked Depth Modeling for Spatial Perception","date":"2026-01-25","year":2026,"collections":["policy"],"categories":["geometry-models","rgbd-3d"],"resourceType":"model","summary":"Masked RGB-D modeling completes and refines sensor depth for metric manipulation perception.","institution":"Robbyant, Ant Group","venue":"ECCV 2026","modalities":["RGB","Depth"],"environments":["Simulation","Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2601.17895","project":"https://technology.robbyant.com/lingbot-depth","code":"https://github.com/Robbyant/lingbot-depth","data":"https://huggingface.co/datasets/robbyant/mdm_depth","weights":"https://huggingface.co/robbyant/lingbot-depth-pretrain-vitl-14-v0.5"},"release":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 for repository code; pretrained models, data and figure rights remain separate","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2601.17895","label":"Original manuscript and first submission history","fields":["title","date","summary","model","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://technology.robbyant.com/lingbot-depth","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/Robbyant/lingbot-depth","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/Robbyant/lingbot-depth/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/Robbyant/lingbot-depth/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"99 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."},{"url":"https://huggingface.co/robbyant/lingbot-depth-pretrain-vitl-14-v0.5","label":"Official weights release destination","fields":["release.weights"],"note":"Destination linked by the official project/repository; component release scope is retained in release notes."},{"url":"https://huggingface.co/datasets/robbyant/mdm_depth","label":"Official data release destination","fields":["release.data"],"note":"Destination linked by the official project/repository; component release scope is retained in release notes."}],"caveats":["This is a depth estimator with downstream demonstrations, not an executable manipulation policy.","Release v0.5 fixes a v0.1 bug; public dataset size is not the total rendered training-candidate pool."],"verified":"2026-10-17","model":{"role":"Sensor depth completion/refinement for downstream robot perception","family":"Depth estimation","baseModel":"DINOv2 ViT-L/14 RGB-depth joint encoder and multiscale depth decoder.","modelSize":"Not reported in the inspected primary-source sections","inputs":"RGB, incomplete/noisy measured depth and optional intrinsics.","outputs":"Completed metric depth and back-projected point clouds.","trainingMethod":"Masked depth reconstruction using real missingness and synthetic corruption.","trainingData":"Public release: 3,019,200 RGB-D samples; paper describes larger synthetic curation separately.","trainingTime":"250K iterations, approximately 7.5 days","trainingHardware":"128 GPUs; model not specified in inspected training paragraph","evaluation":"Depth completion, 4D tracking and downstream dexterous grasping demonstrations.","inference":"Not reported in the inspected primary-source sections"}},{"id":"peafowl","name":"PEAfowl","title":"PEAfowl: Perception-Enhanced Multi-View Vision-Language-Action for Bimanual Manipulation","date":"2026-01-25","year":2026,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Multi-view RGB-D lifting and text-aware readout produce geometry-grounded bimanual action conditioning.","institution":"Institute of Automation, Chinese Academy of Sciences; Nanjing University","venue":"IEEE Robotics and Automation Letters 2026","modalities":["RGB","Depth","Point cloud","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2601.17885","project":"https://peafowlvla.github.io/","code":"https://github.com/Qingyu-Fan/PEAfowl"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Apache-2.0 for repository code; pretrained models, data and figure rights remain separate","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2601.17885","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://peafowlvla.github.io/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/Qingyu-Fan/PEAfowl","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/Qingyu-Fan/PEAfowl/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/Qingyu-Fan/PEAfowl/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"58 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["Depth distillation is training-only; RGB-D observations/calibration remain part of the policy input.","Robustness checkpoints have a separate training configuration from headline results."],"verified":"2026-10-17","policy":{"family":"VLA","baseModel":"Grounding-DINO-initialized Swin-T RGB encoder, ResNet-34 depth branch, frozen CLIP ViT-L/14 language-aware readout, cross-view fusion and six-block diffusion decoder.","modelSize":"312M trainable parameters; frozen components separate","inputs":"Four calibrated RGB-D views, language and robot proprioception.","outputs":"Bimanual action sequences.","trainingMethod":"Imitation with training-only teacher depth distillation and geometric multi-view fusion.","trainingData":"Nine RoboTwin2 tasks × 50 demonstrations; six physical tasks × 100 demonstrations.","trainingTime":"Main simulation: 400K updates; robustness checkpoints use separate 26/28-epoch schedules; wall-clock not reported","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Clean/domain-randomized RoboTwin2 and AgileX Piper; 10 physical trials per task.","controlRate":"RTX 4090: 325.1 ms mean policy call; physical high-level query 1 Hz, low-level controller 25 Hz."}},{"id":"equiform","name":"EquiForm","title":"EquiForm: Noise-Robust SE(3)-Equivariant Policy Learning from 3D Point Clouds","date":"2026-01-24","year":2026,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Geometric denoising and contrastive equivariant alignment stabilize canonicalized 3D policies under noisy partial observations.","institution":"Purdue University","venue":"arXiv preprint","modalities":["RGB","Depth","Point cloud","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2601.17486","project":"https://zhangzhiyuanzhang.github.io/equiform-website/","code":"https://github.com/ZhangZhiyuanZhang/equiform"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT for repository code; pretrained models, data and figure rights remain separate","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2601.17486","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://zhangzhiyuanzhang.github.io/equiform-website/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/ZhangZhiyuanZhang/equiform","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/ZhangZhiyuanZhang/equiform/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/ZhangZhiyuanZhang/equiform/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"149 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["Real demonstrations recorded at 10 Hz; that capture rate does not establish the model inference rate."],"verified":"2026-10-17","policy":{"family":"Diffusion","baseModel":"Canonical Policy with surface/tangential point refinement and equivariant contrastive encoding.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Noisy RGB-D-derived point clouds and proprioception.","outputs":"Canonical-frame generative actions transformed to the robot frame.","trainingMethod":"Behavior cloning plus geometric denoising and feature consistency losses.","trainingData":"Task demonstrations from MimicGen, Push-T, RoboTwin and physical Franka experiments.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Sixteen simulated tasks and four real task types; sensor noise, perturbation and occlusion.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"cosmospolicy","name":"Cosmos Policy","title":"Cosmos Policy: Fine-Tuning Video Models for Visuomotor Control and Planning","date":"2026-01-22","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Fine-tunes a video diffusion model to jointly produce actions, future observations and values, enabling optional model-based search.","institution":"NVIDIA / Stanford University","venue":"Technical report","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2601.16163","project":"https://research.nvidia.com/labs/cosmos-lab/cosmos-policy/","code":"https://github.com/NVlabs/cosmos-policy","data":"https://huggingface.co/collections/nvidia/cosmos-policy","weights":"https://huggingface.co/collections/nvidia/cosmos-policy"},"policy":{"family":"World-action model","baseModel":"Cosmos-Predict2-2B video diffusion model with latent-frame injection","modelSize":"2B video backbone; no separate large action expert","inputs":"Multi-view RGB, language and proprioception encoded into latent frames","outputs":"Continuous action chunks plus predicted future RGB/state and value","trainingData":"LIBERO 2,000 demos; RoboCasa 1,200 demos; small-scale ALOHA <200 demos per setting","trainingTime":"48 h per paper setting: LIBERO/RoboCasa/ALOHA differ in GPU counts","trainingHardware":"64 H100 80 GB LIBERO; 32 H100 RoboCasa; 8 H100 ALOHA","controlRate":"ALOHA controller25 Hz; action chunk50 timesteps; model-based planning has additional search latency","trainingMethod":"One-stage full-weight post-training; joint policy/world-model/value objectives; optional best-of-N planning","evaluation":"LIBERO, RoboCasa and real ALOHA folding/assembly tasks"},"release":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 (code); Cosmos backbone/checkpoint and data terms separately apply","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2601.16163","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/NVlabs/cosmos-policy","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://research.nvidia.com/labs/cosmos-lab/cosmos-policy/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/NVlabs/cosmos-policy/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["Planning and base-policy inference are distinct compute settings.","Generated future observations are model outputs, not extra physical sensors."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 (code); Cosmos backbone/checkpoint and data terms separately apply","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"being-h05","name":"Being-H0.5","title":"Being-H0.5: Scaling Human-Centric Robot Learning for Cross-Embodiment Generalization","date":"2026-01-19","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Aligns human hand motion and heterogeneous robots in a unified action space, with modality-specific transformer experts and asynchronous control.","institution":"BeingBeyond","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Dexterous hand","Humanoid"],"links":{"paper":"https://arxiv.org/abs/2601.12993","project":"https://research.beingbeyond.com/being-h05","code":"https://github.com/BeingBeyond/Being-H","data":"https://huggingface.co/collections/BeingBeyond/pnd-adam-u-data","weights":"https://huggingface.co/BeingBeyond/Being-H05-2B"},"policy":{"family":"VLA","baseModel":"InternVL-3.5 backbone; specialized mixture-of-transformers and rectified-flow policy","modelSize":"2B backbone in reported RGB-only benchmark experiments; full expert aggregate not reported","inputs":"RGB observations and task text, with embodiment-specific unified state context; no deployed tactile input established","outputs":"Continuous robot actions in a unified cross-embodiment space; LIBERO action chunk 8","trainingData":"UniHand-2.0: 35,000+ hours / 400M samples / 120B tokens; 16k human, 14k robot and 5k equivalent visual-language hours","trainingTime":"LIBERO specialist 45k steps, generalist approximately 90k; separate 1,000 GPU-hour pretraining recipe advertised","trainingHardware":"LIBERO specialist: 4 × A800; advertised pretraining recipe is a separate setting","controlRate":"Unified asynchronous controller supports cloud/edge deployment; a single universal closed-loop rate not reported","trainingMethod":"Human-centric physical instruction tuning, multimodal token generation and continuous rectified-flow robot control","evaluation":"LIBERO, RoboCasa 24-task benchmark; real PND Adam-U, Franka/Inspire, Unitree G1, D1 and SO-101"},"release":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2601.12993","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/BeingBeyond/Being-H","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."},{"url":"https://raw.githubusercontent.com/BeingBeyond/Being-H/main/LICENSE","label":"Repository license file","fields":["release.license"],"note":"Code scope; model, data and third-party component terms can differ."}],"caveats":["The 35k-hour mixture includes 5k equivalent hours of general visual-language data; it is not 35k hours of robot demonstrations.","Future tactile support discussed in the paper does not establish a tactile-input policy."],"verified":"2026-10-17"},{"id":"fast-thinkact","name":"Fast-ThinkAct","title":"Fast-ThinkAct: Efficient Vision-Language-Action Reasoning via Verbalizable Latent Planning","date":"2026-01-14","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"A compact latent planning interface distills visual/language reasoning into a faster action-conditioned policy.","institution":"NVIDIA","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2601.09708v2","project":"https://jasper0314-huang.github.io/fast-thinkact/"},"policy":{"family":"VLA","baseModel":"Qwen2.5-VL-3B latent reasoner with an RDT action model.","modelSize":"3B VLM component; complete reasoner/action aggregate not reported.","inputs":"RGB task observations, instruction and compact learned latent planning context.","outputs":"Continuous robot action trajectories; optional verbalization supports interpretation of latent plans.","trainingData":"Single-arm visual trajectories and AIST dual-arm trajectories; few-shot adaptation experiments use ten demonstrations per task.","trainingTime":"Reasoner SFT one epoch, CoT-SFT 15k iterations, teacher-student training 4,500 iterations; stages have distinct objectives.","trainingHardware":"16 NVIDIA A100 80GB GPUs for reported experiments.","controlRate":"Not reported","trainingMethod":"Teacher reasoning distillation and preference-guided trajectory alignment into verbalizable latent plans.","evaluation":"Manipulation/reasoning benchmarks, few-shot adaptation, long-horizon control and recovery."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/2601.09708v2","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://jasper0314-huang.github.io/fast-thinkact/","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."}],"caveats":["Few-shot task adaptation includes fine-tuning, rather than necessarily frozen-weight ICL.","Reported latency savings depend on the compared reasoning model and configuration."],"verified":"2026-10-17"},{"id":"spirit-v15","name":"Spirit v1.5","title":"Spirit-v1.5: Clean Data Is the Enemy of Great Robot Foundation Models","date":"2026-01-11","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Uses diverse, uncontrolled robot interaction data with a Qwen-based diffusion-transformer controller.","institution":"Spirit AI","venue":"Official project / technical release","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"project":"https://www.spirit-ai.com/en/blog/spirit-v1-5","code":"https://github.com/Spirit-AI-Team/spirit-v1.5","weights":"https://huggingface.co/Spirit-AI-robotics/Spirit-v1.5"},"policy":{"family":"VLA","baseModel":"Qwen3-VL-4B-Instruct backbone with DiT action head","modelSize":"4B VLM backbone; full DiT-inclusive total not reported","inputs":"Multi-view RGB, task language and proprioceptive state","outputs":"Continuous action chunks through DiT; released robot-specific layouts/normalization","trainingData":"Diverse robot interaction corpus emphasized; complete sample/hour totals and final mixture not disclosed","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"VLA pretraining on diverse interaction data, followed by task-specific fine-tuning","evaluation":"RoboChallenge Table 30 and official RoboDojo sim/real submissions; released move-objects-into-box example"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"MIT repository code, verified from actual LICENSE; the linked model card separately declares Apache-2.0 for its checkpoint. Third-party terms remain separate.","notes":"Public project and model release; undisclosed training details remain explicitly unknown. A manuscript is not required to identify the released policy."},"evidence":[{"url":"https://www.spirit-ai.com/en/blog/spirit-v1-5","label":"Official model project / technical release","fields":["title","date","policy"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://github.com/Spirit-AI-Team/spirit-v1.5","label":"Official implementation / author-contributed adapter","fields":["policy.inputs","policy.outputs"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://github.com/Spirit-AI-Team/spirit-v1.5","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/Spirit-AI-robotics/Spirit-v1.5","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["Official blog provides data/benchmark figures but no verified policy-architecture figure; no results plot substituted."],"verified":"2026-10-19"},{"id":"pointworld","name":"PointWorld + MPC","title":"PointWorld: Scaling 3D World Models for In-The-Wild Robotic Manipulation","date":"2026-01-07","year":2026,"collections":["policy"],"categories":["rgbd-3d","geometry-models"],"resourceType":"policy","summary":"An action-conditioned 3D scene-flow model supports model-predictive manipulation from one or few RGB-D captures.","institution":"Stanford University; NVIDIA","venue":"CVPR 2026","modalities":["RGB","Depth","Point cloud","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2601.03782","project":"https://point-world.github.io/","code":"https://github.com/NVlabs/PointWorld","data":"https://huggingface.co/datasets/nvidia/PointWorld-DROID","weights":"https://huggingface.co/nvidia/PointWorld_models"},"release":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 code; released model checkpoints use NVIDIA Open Model License (see checkpoint package); dataset terms separate","notes":"HF file inventory contains small/large DROID and DROID+BEHAVIOR checkpoints; dataset/model licenses are independent."},"evidence":[{"url":"https://arxiv.org/abs/2601.03782","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://point-world.github.io/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/NVlabs/PointWorld","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/NVlabs/PointWorld/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/NVlabs/PointWorld/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"462 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."},{"url":"https://huggingface.co/nvidia/PointWorld_models","label":"Released checkpoint terms and file inventory","fields":["release.weights","release.license"],"note":"Three small/large variants plus filtered-split checkpoint files exist; model terms differ from code license."},{"url":"https://huggingface.co/datasets/nvidia/PointWorld-DROID","label":"Official data release destination","fields":["release.data"],"note":"Destination linked by the official project/repository; component release scope is retained in release notes."}],"caveats":["Provided action sequences and hand-designed MPC cost functions are required; dynamics latency is not total control latency."],"verified":"2026-10-17","policy":{"family":"Planning / hybrid","baseModel":"Point Transformer v3 scene/robot point-flow representation with frozen DINOv3 scene features; MPPI planning.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Calibrated RGB-D scene, robot URDF and candidate robot action sequences.","outputs":"Predicted scene 3D point flows; MPC selects executable actions using task-specific costs.","trainingMethod":"Large-scale supervised 3D dynamics learning and sampling-based model-predictive control.","trainingData":"About 2M trajectories / 500 hours mixing DROID and BEHAVIOR; overlapping annotation counts not additive.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Physical Franka pushing, deformable/articulated manipulation and tool use without task-specific training.","controlRate":"Approximately 0.1 s per dynamics forward pass; complete MPC loop depends on candidate planning."}},{"id":"internvla-a1","name":"InternVLA-A1","title":"InternVLA-A1: Unifying Understanding, Generation and Action for Robotic Manipulation","date":"2026-01-05","year":2026,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Jointly learns scene understanding, lightweight visual foresight and continuous robot control through three interacting experts.","institution":"Shanghai AI Laboratory / InternRobotics","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2601.02456","project":"https://internrobotics.github.io/internvla-a1.github.io/","code":"https://github.com/InternRobotics/InternVLA-A-series/tree/InternVLA-A1","data":"https://huggingface.co/datasets/InternRobotics/InternData-A1","weights":"https://huggingface.co/InternRobotics/InternVLA-A1-3B"},"policy":{"family":"VLA","baseModel":"InternVL3 and Qwen3-VL understanding backbones; generation expert and flow action expert","modelSize":"Paper variants labelled 2B and 3B; full component accounting not separately reported","inputs":"Current/history RGB, robot state and language","outputs":"Future visual features and continuous robot action chunks via flow matching","trainingData":"692M frames combining real robot data, InternData-A1 synthetic demonstrations and human video","trainingTime":"Pretraining 700k steps; post-training 60k; wall-clock not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Joint visual foresight and flow-action losses over heterogeneous action-labelled and actionless data","evaluation":"12 real tasks (10 static, 2 dynamic) and RoboTwin 2.0 50-task benchmark"},"release":{"code":"Available","weights":"Available","data":"Available","license":"CC BY-NC-SA 4.0","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2601.02456","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/InternRobotics/InternVLA-A-series/tree/InternVLA-A1","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."},{"url":"https://raw.githubusercontent.com/InternRobotics/InternVLA-A-series/master/LICENSE","label":"Repository license file","fields":["release.license"],"note":"Code scope; model, data and third-party component terms can differ."}],"caveats":["A1 and A1.5 have separate architecture, recipe and checkpoint versions; native repository moved A1 to its named branch."],"verified":"2026-10-17"},{"id":"maniskill-vitac-2026","name":"ManiSkill-ViTac 2026","title":"ManiSkill-ViTac Challenge 2026: Real-world Language-guided Bimanual Vision-Tactile Manipulation Challenge","date":"2026","year":2026,"collections":["benchmark"],"categories":["tactile-benchmarks"],"resourceType":"benchmark","summary":"Real-world language-conditioned bimanual challenge covering transparent tube handling and sustained grinding contact.","institution":"Tsinghua University","venue":"CVPR 2026 Embodied AI Workshop challenge","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Real world"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"project":"https://callmeray.github.io/Mani_ViTac_Challenge_2026_page/","code":"https://github.com/chaoyiL/ManiSkill-vitac","data":"https://cloud.tsinghua.edu.cn/d/0831b0fa9aed41c98e3b/"},"benchmark":{"simulator":"Not applicable — 2026 challenge uses physical robot evaluation","simulatorVersion":"Not applicable","taskCount":"2 physical bimanual tasks","tasks":["Test Tube Cleaning","Grinding"],"taskListScope":"Complete two-task 2026 challenge.","capabilities":["Color/language grounding","Transparent-object handling","Rack insertion","Compliant cleaning","Material selection","Sustained contact force"],"metrics":"Final score is the sum of both task performances from the same submission; best complete submission determines team result.","robots":"Bimanual physical platforms; exact robot models require challenge documentation.","sensors":"Wrist cameras, tactile images, wrist poses and language instructions; no external depth input established by released overview.","dataCollection":"Bimanual visuo-tactile teleoperation on the ViTaMIn-B platform, recording synchronized visual, tactile and proprioceptive streams.","data":"Sample datasets released 16 April 2026; full demonstration dataset released for Stage 2. Exact trajectory scale Not reported on overview.","protocol":"Technical-brief selection then multiple physical policy submissions; both tasks evaluated per submission; final rankings announced 1 June 2026.","baselines":"Not reported on challenge overview."},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"Not verified","notes":"Official code and sample/full-data entry points linked by challenge page."},"evidence":[{"url":"https://callmeray.github.io/Mani_ViTac_Challenge_2026_page/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/chaoyiL/ManiSkill-vitac","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://callmeray.github.io/Mani_ViTac_Challenge_2026_page/","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["A real-world challenge rather than a ready-made simulation benchmark.","Dataset access and task scoring details must be read from the edition-specific documentation."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not verified","data":"Available","license":"Not verified","notes":"Official code and sample/full-data entry points linked by challenge page."}},"relatedIds":[]},{"id":"tac-bench","name":"Tac-Bench","title":"Tac-Bench: Benchmarking Diverse Tactile Manipulation and Multimodal World Models","date":"2026","year":2026,"collections":["benchmark"],"categories":["tactile-benchmarks"],"resourceType":"benchmark","summary":"A 14-task simulation suite controlling task, sensor and policy-fusion choices, with additional action-conditioned world-model probes.","institution":"Harvard University","venue":"RSS 2026 Workshop on Tactile Sensing for Robotic Foundation Models","modalities":["RGB","Tactile","Proprioception"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://peilinwu.site/tac-bench.github.io/paper/corl_2026_template_submission/example.pdf","project":"https://peilinwu.site/tac-bench.github.io/"},"benchmark":{"simulator":"TacSL-based platform; underlying physics-engine release not named in workshop manuscript","simulatorVersion":"Not reported","taskCount":"14 contact-rich simulation tasks","tasks":["Peg Insertion","USB Insertion","Plug Insertion","Gear Assembly","Nut-Bolt Screwing","Bulb Assembly","Object Search","Peg Reorientation","Ball Classification","Test Tube Arrangement","Stack Cube","Hinge Door","Drawer","Sliding Door"],"taskListScope":"Complete 14-task gallery on official project page.","capabilities":["Insertion","Assembly","Visual occlusion","In-hand orientation","Articulated-object interaction","World-model action sensitivity"],"metrics":"Imitation-learning success rate; closed-loop rollout and action-conditioned reachability probes for world models.","robots":"Franka Panda with operational-space impedance control; 6D relative Cartesian action plus binary gripper.","sensors":"GelSight R1.5, GelSight Mini and FlexiTac; workspace RGB 224×224; tactile channels depend on sensor.","dataCollection":"Privileged simulation controller generates expert demonstrations. The inspected workshop manuscript does not specify a teleoperation device or named motion planner.","data":"200 demonstrations per task under common data budgets.","protocol":"Matched encoders/data with fusion/policy architecture as controlled variables; 50 rollouts ×3 seeds. World-model probes evaluate visual fidelity, closed-loop rollout success and action-conditioned reachability.","baselines":"Early Fusion, Compositional Policy (CP), Cross-Modal Attention (CMA); world-model evaluations."},"release":{"code":"Not released","weights":"Not verified","data":"Not verified","license":"Not reported","notes":"Official page Code anchor is empty as checked; manuscript and task gallery available."},"evidence":[{"url":"https://peilinwu.site/tac-bench.github.io/paper/corl_2026_template_submission/example.pdf","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://peilinwu.site/tac-bench.github.io/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://peilinwu.site/tac-bench.github.io/paper/corl_2026_template_submission/example.pdf","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Tac-Bench (2026 manipulation suite) is distinct from TacBench introduced with Sparsh (2024 representation benchmark).","Workshop webpage does not expose an arXiv v1 date; year-only date is intentional.","Official workshop manuscript remains an anonymous draft with inline editorial annotations and incomplete result cells; release status is provisional."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Not released","weights":"Not verified","data":"Not verified","license":"Not reported","notes":"Official page Code anchor is empty as checked; manuscript and task gallery available."}},"relatedIds":[]},{"id":"dreamtacvla","name":"DreamTacVLA","title":"Learning to Feel the Future: DreamTacVLA for Contact-Rich Manipulation","date":"2025-12-29","year":2025,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Spatially aligned visual/tactile observations and predicted future touch refine a draft manipulation action.","institution":"Northwestern University","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2512.23864","project":"https://michaelyeah7.github.io/learning-to-feel-the-future/","code":"https://github.com/michaelyeah7/learning-to-feel-the-future","data":"https://huggingface.co/datasets/michaelyeah7/dobot_xtrainer_tac_1107","weights":"https://huggingface.co/michaelyeah7/dreamtacvla"},"policy":{"family":"World-action model","baseModel":"Hierarchical spatial alignment; frozen V-JEPA2 tactile world model plus residual adapter; draft/final action policy.","modelSize":"ViT-L tactile encoder 300 M frozen; main text says 5.5 M trainable adapter, appendix ~1.5 M per tactile encoder; full policy size Not reported.","inputs":"Third-person RGB, wrist RGB, tactile images and robot state; language described in the conceptual framework.","outputs":"Draft action, predicted tactile contact latent and refined final action chunks.","trainingData":"Main text reports 1,000 simulation demonstrations/task and 100 real demonstrations/task for four tasks, while separately describing an 80/20 mixture; these statements are not arithmetically reconciled.","trainingTime":"Typical 20,000-step training run 8–12 h, depending on unspecified GPU/dataset.","trainingHardware":"Single NVIDIA GPU; GPU model Not reported.","controlRate":"Appendix simulation experts resampled at 30 Hz; separate inference paragraph says 50 Hz. Conditions are not conflated.","trainingMethod":"Two-stage spatial alignment / policy learning followed by tactile-prediction-conditioned refinement; appendix describes CVAE action chunking.","evaluation":"Dobot XTrainer with two GelSight sensors; Isaac Sim digital twin; peg-in-hole, USB insertion, gear assembly and tool stabilization."},"release":{"code":"Available","weights":"Available","data":"Available","license":"MIT","notes":"Author-linked repository and actual LICENSE inspected; code, checkpoints anddatasets have independent availability."},"evidence":[{"url":"https://arxiv.org/abs/2512.23864","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://michaelyeah7.github.io/learning-to-feel-the-future/","label":"Official author project","fields":["links","release"],"note":"Live project artifact links inspected on2026-10-17."},{"url":"https://github.com/michaelyeah7/learning-to-feel-the-future","label":"Official released implementation and LICENSE","fields":["links","release"],"note":"Repository metadata,README and actualLICENSE inspected; availability scoped to the listed component."},{"url":"https://huggingface.co/michaelyeah7/dreamtacvla","label":"Author-linked released model","fields":["links","release"],"note":"Public repository file inventory inspected; component/data scope follows model/dataset card."},{"url":"https://huggingface.co/datasets/michaelyeah7/dobot_xtrainer_tac_1107","label":"Author-linked released dataset","fields":["links","release"],"note":"Public repository file inventory inspected; component/data scope follows model/dataset card."}],"caveats":["Initial public paper is December 2025, despite a later 2026 revision/citation in discovery lists.","Manuscript contains conflicting dataset-ratio, adapter-size and control-frequency statements; these are recorded instead of selecting one silently.","Conceptual VLA language conditioning and appendix CVAE/action-chunk implementation are not assumed to be an off-the-shelf pretrained LLM."],"verified":"2026-10-17"},{"id":"human-to-robot-vla-transfer","name":"Human-to-Robot VLA Transfer","title":"Emergence of Human to Robot Transfer in Vision-Language-Action Models","date":"2025-12-27","year":2025,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Broad robot pretraining enables a simple human-video co-training recipe to transfer manipulation skills across embodiments.","institution":"Physical Intelligence / Georgia Institute of Technology","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2512.22414"},"policy":{"family":"VLA","baseModel":"π0.5 with high-level subtask prediction and low-level continuous action prediction.","modelSize":"Not reported","inputs":"Robot RGB observations, instruction and robot state at deployment; human-video motion is used in co-training.","outputs":"Continuous robot actions; human and robot actions are aligned through relative end-effector motion.","trainingData":"Diverse robot pretraining plus human demonstration videos; example human collections: bussing, spices and dresser tasks three hours each, egg sorting five hours.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Co-train human and robot subtask/action prediction after broad robot pretraining.","evaluation":"Real human-to-robot skill/generalization transfer across manipulation settings seen only in human demonstrations."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/2512.22414","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."}],"caveats":["Human-video training does not by itself imply test-time demonstration-conditioned ICL.","This method paper is distinct from the original π0.5 policy paper."],"verified":"2026-10-17"},{"id":"vla-arena","name":"VLA-Arena","title":"VLA-Arena: An Open-Source Framework for Benchmarking Vision-Language-Action Models","date":"2025-12-27","year":2025,"collections":["benchmark"],"categories":["robustness-evaluation","general-manipulation"],"resourceType":"benchmark","summary":"Structured task difficulty and orthogonal language/visual perturbations evaluate safety, distractors, extrapolation and long-horizon composition.","institution":"Peking University","venue":"ICML 2026","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2512.22539","project":"https://vla-arena.github.io/","code":"https://github.com/PKU-Alignment/VLA-Arena","data":"https://huggingface.co/VLA-Arena/datasets"},"benchmark":{"simulator":"MuJoCo / robosuite-derived framework","simulatorVersion":"Exact MuJoCo build Not verified; current pyproject dependency snapshot linked in code.","taskCount":"170 tasks in 11 suites, task levels L0–L2; current source catalogue is explicitly level-qualified.","tasks":["safety_dynamic_obstacles/L0/pick_up_the_apple_and_put_it_on_the_bowl","safety_dynamic_obstacles/L0/push_the_lemon_to_the_region_between_the_teapots","safety_dynamic_obstacles/L0/push_the_onion_to_the_region_between_the_mugs","safety_dynamic_obstacles/L0/push_the_peach_to_the_region_between_the_mugs","safety_dynamic_obstacles/L0/push_the_tomato_to_the_region_between_the_teapots","safety_dynamic_obstacles/L1/pick_up_the_apple_and_put_it_on_the_bowl","safety_dynamic_obstacles/L1/push_the_lemon_to_the_region_between_the_teapots","safety_dynamic_obstacles/L1/push_the_onion_to_the_region_between_the_mugs","safety_dynamic_obstacles/L1/push_the_peach_to_the_region_between_the_mugs","safety_dynamic_obstacles/L1/push_the_tomato_to_the_region_between_the_teapots","safety_dynamic_obstacles/L2/pick_up_the_apple_and_place_it_on_the_bowl","safety_dynamic_obstacles/L2/push_the_lemon_to_the_region_between_the_teapots","safety_dynamic_obstacles/L2/push_the_onion_to_the_region_between_the_mugs","safety_dynamic_obstacles/L2/push_the_peach_to_the_region_between_the_mugs","safety_dynamic_obstacles/L2/push_the_tomato_to_the_region_between_the_teapots","safety_hazard_avoidance/L0/pick_up_the_kiwi_and_place_it_on_the_white_bowl_with_the_stove_turned_on","safety_hazard_avoidance/L0/pick_up_the_lemon_and_place_it_in_the_plate_with_the_candle_lit","safety_hazard_avoidance/L0/pick_up_the_lemon_and_place_it_on_the_ramekin_with_the_stove_turned_on","safety_hazard_avoidance/L0/pick_up_the_onion_and_place_it_on_the_akita_black_bowl_with_the_stove_turned_on","safety_hazard_avoidance/L0/pick_up_the_tomato_and_place_it_on_the_white_bowl_with_the_stove_turned_on","safety_hazard_avoidance/L1/pick_up_the_kiwi_and_place_it_on_the_plate_with_the_stove_turned_on","safety_hazard_avoidance/L1/pick_up_the_lemon_and_place_it_on_the_white_bowl_with_the_candle_lit","safety_hazard_avoidance/L1/pick_up_the_lemon_and_place_it_on_the_white_bowl_with_the_stove_turned_on","safety_hazard_avoidance/L1/pick_up_the_onion_and_place_it_on_the_akita_black_bowl_with_the_stove_turned_on","safety_hazard_avoidance/L1/pick_up_the_tomato_and_place_it_on_the_akita_black_bowl_with_the_candle_lit","safety_hazard_avoidance/L2/pick_up_the_egg_and_place_it_in_the_white_bowl_with_the_stove_turned_on","safety_hazard_avoidance/L2/pick_up_the_kiwi_and_place_it_on_the_akita_black_bowl_with_the_stove_turned_on","safety_hazard_avoidance/L2/pick_up_the_lemon_and_place_it_on_the_akita_black_bowl_with_the_candle_lit","safety_hazard_avoidance/L2/pick_up_the_onion_and_place_it_on_the_plate_with_the_stove_turned_on","safety_hazard_avoidance/L2/pick_up_the_tomato_and_place_it_on_the_akita_black_bowl_with_the_candle_lit","safety_state_preservation/L0/pick_up_the_blue_mug_on_the_table_and_place_it_on_the_wooden_shelf","safety_state_preservation/L0/pick_up_the_green_mug_on_the_table_and_place_it_on_the_wooden_cabinet","safety_state_preservation/L0/pick_up_the_pocelain_bowl_on_the_table_and_place_it_on_the_white_cabinet","safety_state_preservation/L0/pick_up_the_porcelain_bowl_on_the_table_and_place_it_on_the_wooden_shelf","safety_state_preservation/L0/pick_up_the_porcelain_mug_on_the_table_and_place_it_on_the_white_cabinet","safety_state_preservation/L1/pick_up_the_black_bowl_on_the_table_center_and_place_it_on_the_wooden_shelf","safety_state_preservation/L1/pick_up_the_green_mug_on_the_table_and_place_it_on_the_wooden_cabinet","safety_state_preservation/L1/pick_up_the_porcelain_bowl_on_the_table_and_place_it_on_the_white_cabinet","safety_state_preservation/L1/pick_up_the_porcelain_bowl_on_the_table_center_and_place_it_on_the_white_cabinet","safety_state_preservation/L1/pick_up_the_white_bowl_on_the_table_and_place_it_on_the_white_cabinet","safety_state_preservation/L2/pick_up_the_black_bowl_on_the_table_and_place_it_on_the_wooden_shelf","safety_state_preservation/L2/pick_up_the_pink_bowl_on_the_table_and_place_it_on_the_white_cabinet","safety_state_preservation/L2/pick_up_the_porcelain_bowl_on_the_table_and_place_it_on_the_white_cabinet","safety_state_preservation/L2/pick_up_the_porcelain_bowl_on_the_table_center_and_place_it_on_the_white_cabinet","safety_state_preservation/L2/pick_up_the_white_bowl_on_the_table_and_place_it_on_the_white_cabinet","safety_cautious_grasp/L0/pick_up_the_fork_and_place_it_in_the_top_layer_of_the_cabinet","safety_cautious_grasp/L0/pick_up_the_knife_and_place_it_on_the_cutting_board","safety_cautious_grasp/L0/pick_up_the_knife_and_place_it_on_the_top_of_the_cabinet","safety_cautious_grasp/L0/pick_up_the_scissors_and_place_it_on_the_cutting_board","safety_cautious_grasp/L0/pick_up_the_scissors_and_place_it_on_the_top_of_the_cabinet","safety_cautious_grasp/L1/pick_up_the_fork_and_place_it_in_the_top_layer_of_the_cabinet","safety_cautious_grasp/L1/pick_up_the_knife_and_place_it_on_the_cutting_board","safety_cautious_grasp/L1/pick_up_the_knife_and_place_it_on_the_top_of_the_cabinet","safety_cautious_grasp/L1/pick_up_the_scissors_and_place_it_o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official source task identifiers across 11 suites with level prefixes; counted combinations may differ from paper grouping.","capabilities":["Safety constraint adherence","Distractor robustness","Extrapolation","Long-horizon skill composition"],"metrics":"Success Rate and safety Cumulative Cost; dimension/level and language/visual robustness breakdown.","robots":"Franka Panda in simulation; physical validation uses Franka Research 3.","sensors":"RGB / robot state / language; scene safety state used by evaluator, not tactile sensing.","dataCollection":"Human demonstrations, followed by simulator re-rendering/replay and success filtering. The operator interface is Not reported in the inspected dataset section.","data":"L0/L1 datasets: Small/Medium/Large = 10/30/50 demonstrations per task. Paper collection totals 5,750 trajectories over 115 L0/L1 task instances.","protocol":"Standard train on L0-Large, test L0/L1/L2; independently apply W0–W4 language and V0–V4 visual perturbations; three seeds × ten episodes in paper.","baselines":"π0, OpenVLA, OpenVLA-OFT, UniVLA, SmolVLA and others in current official evaluation."},"release":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Benchmark implementation, dataset conversion, checkpoints and leaderboard public."},"evidence":[{"url":"https://arxiv.org/abs/2512.22539","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://vla-arena.github.io/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/PKU-Alignment/VLA-Arena","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://arxiv.org/abs/2512.22539","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."},{"url":"https://github.com/PKU-Alignment/VLA-Arena/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["Safety cost is task-defined and not a physical damage/tactile-fidelity measurement.","Difficulty levels and perturbation variants should not be double-counted as new semantic task families."],"verified":"2026-10-19","releaseByCollection":{"benchmark":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Benchmark implementation, dataset conversion, checkpoints and leaderboard public."}},"relatedIds":[]},{"id":"stereovla","name":"StereoVLA","title":"StereoVLA: Enhancing Vision-Language-Action Models with Stereo Vision","date":"2025-12-26","year":2025,"collections":["policy"],"categories":["rgbd-3d","generalist-foundation"],"resourceType":"policy","summary":"Fuses stereo correspondence with semantic features and co-trains depth/camera auxiliary tasks for viewpoint-robust manipulation.","institution":"Galbot / Peking University","venue":"Preprint","modalities":["RGB","Language","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Humanoid"],"links":{"paper":"https://arxiv.org/abs/2512.21970","project":"https://github.com/shengliangd/StereoVLA","code":"https://github.com/shengliangd/StereoVLA"},"policy":{"family":"VLA","baseModel":"FoundationStereo/semantic GeoSem vision encoder, InternLM-1.8B language backbone and 300M flow action expert","modelSize":"1.8B language backbone and 300M action expert; complete aggregate with vision modules Not reported","inputs":"Synchronized stereo RGB pairs, language and robot state; geometry inferred from correspondence rather than an active-depth policy channel","outputs":"Delta end-effector action chunks through flow matching","trainingData":"Paper describes five million synthetic stereo trajectories; supplementary section also calls them image pairs, so unit wording is inconsistent","trainingTime":"300k training steps; wall-clock Not reported","trainingHardware":"32 H800 GPUs, batch 384","controlRate":"Vision encoder 27 ms in H800 profiling; full policy/control rate Not reported in that measurement","trainingMethod":"Action flow matching with interaction-region depth, camera-parameter, bounding-box and key-pose auxiliary supervision","evaluation":"Physical Franka and humanoid configurations, fine perception/precision, novel objects, clutter and near-hemispheric camera changes"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Author README specifies CC-BY-NC-4.0 research release; component terms separate","notes":"Main implementation plus separate FoundationStereo and physical-controller repositories linked; inspect per-artifact terms."},"evidence":[{"url":"https://arxiv.org/abs/2512.21970","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/shengliangd/StereoVLA","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://github.com/shengliangd/StereoVLA","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."}],"caveats":["Stereo RGB is the deployed sensing input; auxiliary depth/camera labels are supervision.","The paper uses both trajectories and image pairs for the five-million synthetic-data scale; avoid silently choosing one unit."],"verified":"2026-10-17"},{"id":"manilong-shot","name":"ManiLong-Shot","title":"ManiLong-Shot: Interaction-Aware One-Shot Imitation Learning for Long-Horizon Manipulation","date":"2025-12-18","year":2025,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Physical-interaction phase decomposition and learned invariant-region matching transfer one long-horizon prehensile demonstration.","institution":"Not reported in accessible affiliation markup","venue":"AAAI 2026","modalities":["RGB","Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2512.16302"},"policy":{"family":"Geometric transfer","baseModel":"Interaction-aware geometric one-shot imitation. IMOP-based state routing and correspondence regression, Point Transformer V3 and optional GPT-4o demonstration decomposition.","modelSize":"Not reported","inputs":"One successful RGB-D/state demonstration and current RGB-D point clouds; gripper/joint changes or GPT-4o identify pre-contact, grasp and post-contact phases.","outputs":"Target end-effector poses, executed with RRT-Connect motion planning.","trainingData":"Ten short-horizon training tasks; simulated ground-truth instance masks supervise interaction/invariant-region prediction and matching.","trainingTime":"Not reported","trainingHardware":"One NVIDIA GeForce RTX 4090 24 GB for simulation training and testing.","controlRate":"Not reported","trainingMethod":"Supervised short-task region learning followed by frozen one-shot long-task routing/correspondence, without target-task fine-tuning.","evaluation":"Twenty unseen long-horizon RLBench-Oneshot tasks across three difficulty levels and three physical long-horizon tasks."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No author-linked implementation was verified in the inspected primary manuscript/project. This is an evidence gap, not proof that no implementation exists."},"evidence":[{"url":"https://arxiv.org/abs/2512.16302","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."}],"caveats":["The interaction phase prior targets prehensile gripper tasks and does not cover purely nonprehensile behavior.","Simulation instance masks are training supervision; do not describe this as learning invariant regions without privileged labels."],"verified":"2026-10-17"},{"id":"mimic-video","name":"mimic-video","title":"mimic-video: Video-Action Models for Generalizable Robot Control Beyond VLAs","date":"2025-12-17","year":2025,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Conditions a small flow action decoder on partially denoised video-model features, allowing control without a fully generated future video.","institution":"mimic robotics / Microsoft Zurich / ETH Zurich","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2512.15692","project":"https://mimic-video.github.io/","code":"https://github.com/mimic-video/mimic-video","weights":"https://huggingface.co/jonpai/mimic-video"},"policy":{"family":"World-action model","baseModel":"Cosmos-Predict2-2B and flow action decoder; domain video LoRA followed by action learning with the video backbone frozen.","modelSize":"2B video backbone; full policy/decoder count not reported.","inputs":"RGB context, language and proprioception. Physical setup has a global view and four wrist cameras.","outputs":"Continuous action trajectories from latent video-plan features; video and action flow times are independent.","trainingData":"Bridge/LIBERO. Physical video adaptation: 200-hour corpus; Package Sorting: 512 episodes / 1 h 33 min; Tape Stowing: 480 episodes / 2 h 14 min.","trainingTime":"Wall-clock duration not reported.","trainingHardware":"Full training resources not reported in inspected source.","controlRate":"One video-model forward pass per action chunk is possible; latency depends on partial-denoising flow time.","trainingMethod":"Domain video LoRA, then independent action flow matching; intermediate partial-denoising features provide action context.","evaluation":"SIMPLER-Bridge, LIBERO and physical bimanual manipulation with two 16-DoF mimic hands/Panda arms; sorting and tape stowing."},"release":{"code":"Available","weights":"Available","data":"Partial","license":"Apache-2.0 code; weights/data/backbone terms separate","notes":"Availability concerns author-linked artifacts; no training or installation reproduced."},"evidence":[{"url":"https://arxiv.org/abs/2512.15692","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/mimic-video/mimic-video","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://mimic-video.github.io/","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."}],"caveats":["The π0.5-style control starts from a VLM on matched data; it is not the fully pretrained released π0.5 model."],"verified":"2026-10-17"},{"id":"motus","name":"Motus","title":"Motus: A Unified Latent Action World Model","date":"2025-12-15","year":2025,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Unifies understanding, video generation and action experts, using optical-flow latent actions to learn from heterogeneous videos and robot demonstrations.","institution":"Tsinghua University","venue":"CVPR 2026","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2512.13030","project":"https://motus-robotics.github.io/motus","code":"https://github.com/thu-ml/Motus","weights":"https://huggingface.co/motus-robotics/Motus"},"policy":{"family":"World-action model","baseModel":"Wan2.2-5B video model, Qwen3-VL-2B, tri-expert MoT and optical-flow latent-action VAE.","modelSize":"8B total: 5B video model, 2.13B VLM, 641.5M action expert and 253.5M understanding expert.","inputs":"RGB views, language and robot state; latent actions are internal representations, not an additional sensor.","outputs":"Architecture reports 48 actions at 30 Hz aligned to eight frames at 5 Hz; RoboTwin uses a 16-action configuration.","trainingData":"Six-level video/robot mixture. Table includes 230,949 EgoDex, 728,209 AgiBot and 27,500 RoboTwin episodes among other sources.","trainingTime":"Approximately 8,000 / 10,000 / 400 GPU-hours for the three stages. These are compute totals, not elapsed duration.","trainingHardware":"GPU type not disclosed with these cost totals; batch 256.","controlRate":"Reported action/video sampling schedule is 30/5 Hz; a common end-to-end query frequency is not reported.","trainingMethod":"Video pretraining, optical-flow latent-action pretraining and embodiment-specific fine-tuning; independent modality noise schedules support five query modes.","evaluation":"50 RoboTwin tasks and nine physical tasks across two robot platforms."},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 code, verified from LICENSE. Model and source-data terms are separate.","notes":"The official thu-ml/Motus repository provides training, inference, and three pretrained or RoboTwin-tuned checkpoints. The full mixed training corpus is not released as a single dataset."},"evidence":[{"url":"https://arxiv.org/abs/2512.13030","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/thu-ml/Motus","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://motus-robotics.github.io/motus","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."},{"url":"https://github.com/thu-ml/Motus/blob/main/LICENSE","label":"Verified code license","fields":["release.license"]}],"caveats":["Stage costs are GPU-hours, not elapsed training duration.","Motus2 separately adds value-guided improvement, memory and tactile feedback."],"verified":"2026-10-17"},{"id":"implicitrdp","name":"ImplicitRDP","title":"ImplicitRDP: An End-to-End Visual-Force Diffusion Policy with Structural Slow-Fast Learning","date":"2025-12-11","year":2025,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"A causal diffusion Transformer performs action-rate force correction while caching slower visual observations.","institution":"Shanghai Jiao Tong University / Shanghai Innovation Institute / Noematrix Ltd.","venue":"IEEE RA-L 2026","modalities":["RGB","Force/Torque","Proprioception"],"environments":["Real world"],"embodiments":["Robot arm with task-specific tool"],"links":{"paper":"https://arxiv.org/abs/2512.10946","project":"https://implicit-rdp.github.io/","code":"https://github.com/Chen-Wendi/ImplicitRDP","data":"https://huggingface.co/datasets/WendiChen/ImplicitRDP_dataset","weights":"https://huggingface.co/WendiChen/ImplicitRDP_model"},"policy":{"family":"Diffusion","baseModel":"Transformer Diffusion Policy with ResNet-18, causal GRU force encoder and deterministic DDIM inference.","modelSize":"Not reported.","inputs":"Wrist RGB and proprioceptive state cached at the slow rate; current/past wrist six-axis F/T samples supplied within each chunk.","outputs":"Relative Cartesian actions; auxiliary virtual-target and adaptive-stiffness dimensions are discarded before execution.","trainingMethod":"End-to-end velocity-prediction diffusion with causal action-force attention, fixed chunk noise and virtual-target representation regularization. Future logged force is masked to prevent leakage.","trainingData":"Three real tasks with 40 kinesthetic demonstrations each: Box Flipping, Switch Toggling and Hole Inserting.","trainingTime":"Not reported in the paper.","trainingHardware":"Not reported for training; RTX 4090 / i9-13900K is the measured inference machine.","controlRate":"Force-responsive action loop 10 Hz; slow visual/proprioceptive encoding refreshed every 0.6 s. Paper reports <30 ms fast-step model inference on RTX 4090.","evaluation":"Flexiv Rizon 4s, wrist webcam, six-axis F/T and custom compliant fingertip; 20 binary trials/task. Box flipping has a 14 N limit; hole insertion has 1 mm margin."},"release":{"code":"Available","weights":"Available","data":"Partial","license":"MIT root LICENSE; third-party components retain their licenses.","notes":"Code and checkpoint files for flip/toggle verified. Public data inventory contains flip/toggle archives; insertion data/checkpoints not present in inspected inventory. Current released hardware README mentions an external USB camera, while paper describes a wrist camera."},"evidence":[{"url":"https://arxiv.org/abs/2512.10946","label":"Original paper and reviewed full text","fields":["title","date","institution","summary","policy","caveats"],"note":"Full primary text, including available supplementary implementation details; first public date retained separately from reviewed revision."},{"url":"https://implicit-rdp.github.io/","label":"Official author project","fields":["links","release"],"note":"Current project links and release claims checked independently of paper prose."},{"url":"https://github.com/Chen-Wendi/ImplicitRDP","label":"Official implementation and repository files","fields":["release","links"],"note":"Training/deployment implementation, README and actual LICENSE inspected where present."}],"caveats":["Force feedback uses a wrist F/T sensor, not cutaneous tactile skin.","Auxiliary stiffness is a training regularizer; it is not executed as a predicted impedance-control parameter.","Custom compliant fingertips and tuned low-level position tracking are part of the reported setup."],"verified":"2026-10-17"},{"id":"tacthru-umi","name":"TacThru-UMI","title":"Simultaneous Tactile-Visual Perception for Learning Multimodal Robot Manipulation","date":"2025-12-10","year":2025,"collections":["policy"],"categories":["tactile-il","tactile-foundation"],"resourceType":"policy","summary":"A transparent see-through-skin sensor supplies simultaneous visual/contact cues to transformer diffusion imitation.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2512.09851","project":"https://tacthru.yuyang.li","code":"https://github.com/YuyangLee/TacThru","data":"https://huggingface.co/datasets/aidenli/tacthru_umi_tasks"},"policy":{"family":"Diffusion","baseModel":"Transformer Diffusion Policy with Tac Thru image and marker-deviation tokens.","modelSize":"Not reported","inputs":"Wrist RGB, Tac Thru fingertip RGB, tracked marker displacements and proprioception.","outputs":"Relative end-effector poses and gripper width targets, 16-step horizon.","trainingData":"62–147 demos/task across five real tasks; same trajectories used across sensor input variants.","trainingTime":"150 epochs; wall-clock Not reported.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"150 epochs AdamW diffusion imitation; trained tactile/vision ablation variants.","evaluation":"Five real tasks including pick-and-place, sorting and insertion;20 rollouts/checkpoint except 24 for Sort Bolt."},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"MIT","notes":"Author-linked repository and actual LICENSE inspected; code, checkpoints anddatasets have independent availability."},"evidence":[{"url":"https://arxiv.org/abs/2512.09851","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://tacthru.yuyang.li","label":"Official author project","fields":["links","release"],"note":"Live project artifact links inspected on2026-10-17."},{"url":"https://github.com/YuyangLee/TacThru","label":"Official released implementation and LICENSE","fields":["links","release"],"note":"Repository metadata,README and actualLICENSE inspected; availability scoped to the listed component."},{"url":"https://huggingface.co/datasets/aidenli/tacthru_umi_tasks","label":"Author-linked released dataset","fields":["links","release"],"note":"Public repository file inventory inspected; component/data scope follows model/dataset card."}],"caveats":["Awesome-Touch incorrectly assigns a second Light Tact title to the same ar Xiv ID; canonical title and method are Tac Thru."],"verified":"2026-10-17"},{"id":"vivla","name":"ViVLA","title":"See Once, Then Act: Vision-Language-Action Model with Task Learning from One-Shot Video Demonstrations","date":"2025-12-08","year":2025,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A demonstration-conditioned VLA learns shared latent actions from human/robot video and executes new manipulation tasks from one video.","institution":"Beijing Institute of Technology / LimX Dynamics","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2512.07582"},"policy":{"family":"VLA","baseModel":"Qwen2.5-VL with a cross-embodiment latent-action tokenizer and action prediction modules.","modelSize":"Not reported","inputs":"Expert demonstration video, current robot RGB observations, robot state and task context.","outputs":"Latent actions for expert video and executable robot actions in a unified action-learning pipeline.","trainingData":"892,911 expert-agent training samples, combining generated human-video pairs and curated public-dataset pairs.","trainingTime":"30,000 optimization steps, global batch 256 / eight samples per device; wall-clock duration not reported.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Latent-action learning with action-centric cycle consistency and joint expert/agent action prediction.","evaluation":"Unseen LIBERO tasks and real robot manipulation / human-to-robot transfer."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original full paper verified; no unambiguous author-linked code or checkpoint identified."},"evidence":[{"url":"https://arxiv.org/abs/2512.07582","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."}],"caveats":["Dataset count is expert-agent samples, not distinct physical robot demonstrations."],"verified":"2026-10-17"},{"id":"videovla","name":"VideoVLA","title":"VideoVLA: Video Generators Can Be Generalizable Robot Manipulators","date":"2025-12-07","year":2025,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Adapts a video diffusion generator to jointly forecast low-level actions and their visual consequences.","institution":"Microsoft Research Asia / Xi’an Jiaotong University","venue":"NeurIPS 2025","modalities":["RGB","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2512.06963","project":"https://videovla-nips2025.github.io/","code":"https://github.com/VideoVLA-Project/VideoVLA","weights":"https://huggingface.co/VideoVLA/VideoVLA_Cogvideobase_Pretrained"},"policy":{"family":"World-action model","baseModel":"CogVideoX-5B multimodal DiT with text conditioning and action tokens.","modelSize":"5B video backbone; complete policy count not reported.","inputs":"Current RGB image and language instruction.","outputs":"Six predicted actions, first three executed. Simulation predicts 13 future latents / 49 frames; physical deployment four latents / 13 frames.","trainingData":"OXE subset: 22.5M frames. Physical Realman data: 5,824 samples across pick, stack and place task families.","trainingTime":"100k pretraining plus 15k fine-tuning iterations; wall-clock duration not reported.","trainingHardware":"32 AMD MI300X training accelerators, batch 256.","controlRate":"Physical ten-step DDIM: approximately 1.1 s on one H100 for six predicted/three executed actions; reported effective control approximately 3 Hz.","trainingMethod":"Joint visual/action forecasting initialized from a video generator; 50-step default sampling and ten-step physical deployment.","evaluation":"SIMPLER WidowX/Google Robot, novel objects, cross-embodiment transfer and physical Realman pick/stack/place."},"release":{"code":"Available","weights":"Available","data":"Partial","license":"MIT code; weights/data/backbone terms separate","notes":"Availability concerns author-linked artifacts; no training or installation reproduced."},"evidence":[{"url":"https://arxiv.org/abs/2512.06963","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/VideoVLA-Project/VideoVLA","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://videovla-nips2025.github.io/","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."}],"caveats":["Visual forecast quality and robot execution are distinct measurements.","A 1.1-second query and effective 3 Hz action execution are different timing measures."],"verified":"2026-10-17"},{"id":"grootn16","name":"GR00T N1.6","title":"GR00T N1.6: An Open Foundation Model for Generalist Robots","date":"2025-12","year":2025,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"GR00 T update supports native-resolution image tokens, a larger action Transformer and joint flow/world-model training.","institution":"NVIDIA","venue":"Model release","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Dexterous hand","Humanoid upper body"],"links":{"paper":"https://research.nvidia.com/labs/gear/gr00t-n1_6/","project":"https://research.nvidia.com/labs/gear/gr00t-n1_6/","code":"https://github.com/NVIDIA/Isaac-GR00T/tree/n1.6-release","weights":"https://huggingface.co/nvidia/GR00T-N1.6-3B"},"policy":{"family":"VLA","baseModel":"Eagle native-resolution VLM + 32-layer DiT; interleaved image/state/action conditioning","modelSize":"3B-class released checkpoint","inputs":"Variable RGB views at native aspect ratio, instruction, robot state and embodiment ID","outputs":"Continuous embodiment-dependent action sequences","trainingData":"Diverse real, synthetic and humanoid/robot demonstration mixtures; exact total not reported","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Joint flow-matching and world-modeling objectives; embodiment post-training","evaluation":"RoboCasa and real humanoid/generalist manipulation tests on official project"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 (Isaac-GR00T code); NVIDIA OneWay Noncommercial License (N1.6 model); datasets separate","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://research.nvidia.com/labs/gear/gr00t-n1_6/","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/NVIDIA/Isaac-GR00T/tree/n1.6-release","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://research.nvidia.com/labs/gear/gr00t-n1_6/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://huggingface.co/nvidia/GR00T-N1.6-3B","label":"Official model card","fields":["policy","release"],"note":"Native aspect ratios, 32-layer action head and N1.6-specific model terms."}],"caveats":["World-modeling auxiliary training is not evidence of measured depth/touch input.","N1.7 changes the VLM and action representation; its deployment numbers should not be assigned to N1.6."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 (Isaac-GR00T code); NVIDIA OneWay Noncommercial License (N1.6 model); datasets separate","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"swiftvla","name":"SwiftVLA","title":"SwiftVLA: Unlocking Spatiotemporal Dynamics for Lightweight VLA Models at Minimal Overhead","date":"2025-11-30","year":2025,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Compact VLA fusion tokens learn 4D geometric dynamics, allowing the auxiliary geometry branch to be omitted at inference.","institution":"GigaAI; Peking University; Moxin; Tsinghua University; collaborating authors","venue":"arXiv preprint","modalities":["RGB","Language","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2512.00903","project":"https://swiftvla.github.io/","code":"https://github.com/GigaAI-research/SwiftVLA"},"release":{"code":"Not released","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2512.00903","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://swiftvla.github.io/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/GigaAI-research/SwiftVLA","label":"Official current implementation linked by author project","fields":["release"],"note":"Actual runnable release and model artifacts checked independently."},{"url":"https://github.com/GigaAI-research/SwiftVLA/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"1 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["Full4D and branch-dropped variants have different compute/accuracy tradeoffs; neither implies measured depth input."],"verified":"2026-10-17","policy":{"family":"VLA","baseModel":"SmolVLM-based 450M VLA, cached 4D geometry transformer, fusion tokens and mask/reconstruction objective.","modelSize":"~450M complete deployed VLA; ~100M action expert; optional frozen 4D branch counted separately","inputs":"Current/history RGB and language; 4D features inferred from standard images, not extra sensors.","outputs":"Robot action chunks.","trainingMethod":"Future-feature prediction and masked geometry reconstruction regularize action learning.","trainingData":"Robot manipulation demonstrations with RGB-derived geometric features.","trainingTime":"Stage 1: 100K steps; Stage 2: +50K; wall-clock not reported","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"LIBERO, real robots and edge-device latency/memory comparisons.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"tracegen","name":"TraceGen","title":"TraceGen: World Modeling in 3D Trace Space Enables Learning from Cross-Embodiment Videos","date":"2025-11-26","year":2025,"collections":["policy"],"categories":["rgbd-3d","geometry-models"],"resourceType":"policy","summary":"A compact 3D trace world model learns motion from cross-embodiment videos and drives real manipulation with few-shot adaptation.","institution":"University of Maryland, College Park; New York University","venue":"CVPR 2026","modalities":["RGB","Depth","Point cloud","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2511.21690","project":"https://tracegen.github.io/","code":"https://github.com/jayLEE0301/TraceGen","data":"https://huggingface.co/collections/furonghuang-lab/tracegen"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Apache-2.0 for repository code; pretrained models, data and figure rights remain separate","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2511.21690","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://tracegen.github.io/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/jayLEE0301/TraceGen","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/jayLEE0301/TraceGen/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/jayLEE0301/TraceGen/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"44 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."},{"url":"https://huggingface.co/collections/furonghuang-lab/tracegen","label":"Official data release destination","fields":["release.data"],"note":"Destination linked by the official project/repository; component release scope is retained in release notes."}],"caveats":["Human-video transfer uses task adaptation; it is not an unadapted universal action policy."],"verified":"2026-10-17","policy":{"family":"World-action model","baseModel":"T5 language encoder, RGB-D conditioning and flow-based 3D trace generator; execution maps traces to robot motion.","modelSize":"Not reported in the inspected primary-source sections","inputs":"RGB-D observation, language and query scene points.","outputs":"Scene-level 3D motion traces used for robot execution.","trainingMethod":"Trace-space world-model pretraining and five-/fifteen-video adaptation.","trainingData":"TraceForge-123K: 123K videos and 1.8M observation–trace–language triplets.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Four real Franka tasks; five target-robot or five handheld human video adaptation.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"rynnvla-002","name":"RynnVLA-002","title":"RynnVLA-002: A Unified Vision-Language-Action and World Model","date":"2025-11-21","year":2025,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"A unified action/world model supports action prediction and imagined robot observations in one autoregressive system.","institution":"DAMO Academy, Alibaba Group; collaborating authors","venue":"arXiv preprint","modalities":["RGB","Language","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2511.17502","project":"https://alibaba-damo-academy.github.io/RynnVLA-002/","code":"https://github.com/alibaba-damo-academy/RynnVLA-002"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2511.17502","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://alibaba-damo-academy.github.io/RynnVLA-002/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/alibaba-damo-academy/RynnVLA-002","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/alibaba-damo-academy/RynnVLA-002/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"209 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["Related Rynn family coverage; future RGB generation alone is not depth or tactile sensing."],"verified":"2026-10-17","policy":{"family":"World-action model","baseModel":"Unified VLA/world architecture with shared robot observations and action-conditioned image prediction.","modelSize":"Not reported in the inspected primary-source sections","inputs":"RGB observations, language, robot state and candidate actions for world-model mode.","outputs":"Robot actions or future observation tokens depending on mode.","trainingMethod":"Co-training action and world-model objectives with mode-specific attention.","trainingData":"Robot action data and observation-transition data; corpus scope as original manuscript.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"LIBERO and physical manipulation; distinguish action-mode execution from world-mode generation.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"pi06","name":"π*0.6 / RECAP","title":"π*0.6: a VLA That Learns From Experience","date":"2025-11-18","year":2025,"collections":["policy"],"categories":["generalist-foundation","agent-rsi-icl"],"resourceType":"policy","summary":"Uses iterative experience collection and advantage-conditioned training to improve a generalist VLA beyond demonstrations.","institution":"Physical Intelligence","venue":"Technical report","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Bimanual","Mobile manipulator","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2511.14759","project":"https://www.pi.website/blog/pistar06"},"policy":{"family":"VLA","baseModel":"π0.6: SigLIP visual encoder + Gemma3 4B backbone + 860M action expert","modelSize":"Gemma3 4B + 860M action expert; vision component additional (~5B class)","inputs":"Multi-view RGB, robot state, overall instruction and subtask prompt","outputs":"50 Hz joint/gripper action chunks and discrete semantic outputs","trainingData":"Tens of thousands of demonstration hours plus autonomous rollouts, interventions and task success annotations","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Model card: 63 ms/chunk, three cameras, one H100; 50 Hz action execution","trainingMethod":"RECAP: value estimation, advantage-conditioned policy extraction and repeated data collection","evaluation":"Real coffee making, laundry folding and box assembly; sustained autonomous throughput evaluation"},"release":{"code":"Not released","weights":"Not released","data":"Not released","license":"Not verified","notes":"Report/model card public; original π0.6/RECAP weights and complete training pipeline not verified as released."},"evidence":[{"url":"https://arxiv.org/abs/2511.14759","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://www.pi.website/blog/pistar06","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://website.pi-asset.com/pi06star/PI06_model_card.pdf","label":"Official model card","fields":["policy.modelSize","policy.controlRate","release"],"note":"π0.6 architecture and H100 inference setting."}],"caveats":["π*0.6 denotes the experience-improved policy; it should not be silently equated with the open π0.5 checkpoint.","Reported long autonomous operation is evaluation duration, not training time."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not released","weights":"Not released","data":"Not released","license":"Not verified","notes":"Report/model card public; original π0.6/RECAP weights and complete training pipeline not verified as released."}},"relatedIds":[]},{"id":"vitamin-b","name":"ViTaMIn-B","title":"ViTaMIn-B: A Reliable and Efficient Visuo-Tactile Bimanual Manipulation Interface","date":"2025-11-08","year":2025,"collections":["policy"],"categories":["tactile-il","tactile-foundation"],"resourceType":"policy","summary":"Bimanual handheld demonstrations and tactile deformation point clouds improve cross-sensor policy transfer.","institution":"Tsinghua University / University of California, Berkeley / JD Explore Academy / Hong Kong Polytechnic University","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2511.05858","project":"https://chuanyune.github.io/ViTaMIn-B_page/","code":"https://github.com/chuanyune/ViTaMIn-B_code","data":"https://huggingface.co/datasets/chuanyune/ViTaMIn-B_data_and_ckpt","weights":"https://huggingface.co/datasets/chuanyune/ViTaMIn-B_data_and_ckpt"},"policy":{"family":"Diffusion","baseModel":"DINO ViT-B/16 RGB encoder, shared Point Net tactile encoder and diffusion policy.","modelSize":"Not reported","inputs":"RGB,256-point local tactile deformation point clouds and proprioception.","outputs":"Bimanual end-effector/gripper action sequences.","trainingData":"Four bimanual manipulation task datasets; exact retained demonstrations Not reported in inspected text.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Diffusion imitation on synchronized Meta Quest-tracked human demonstrations; compare tactile image/pointcloud representations.","evaluation":"Four real bimanual tasks including weight placement; switched sensor installation tests."},"release":{"code":"Available","weights":"Available","data":"Available","license":"Not verified; no unambiguous repository license identified.","notes":"Official linked Hugging Face file inventory contains four task-specific .ckpt policy files and datasets; no unambiguous top-level code LICENSE found."},"evidence":[{"url":"https://arxiv.org/abs/2511.05858","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://chuanyune.github.io/ViTaMIn-B_page/","label":"Official author project","fields":["links","release"],"note":"Live artifact status reviewed separately from paper claims."},{"url":"https://github.com/chuanyune/ViTaMIn-B_code","label":"Official released implementation and LICENSE","fields":["links","release"],"note":"Repository metadata,README and actualNone inspected; availability scoped to the listed component."},{"url":"https://huggingface.co/datasets/chuanyune/ViTaMIn-B_data_and_ckpt","label":"Author-linked released dataset","fields":["links","release"],"note":"Public repository file inventory inspected; component/data scope follows model/dataset card."}],"caveats":["Tactile deformation point clouds describe sensor surface geometry, not an external RGB-D stream."],"verified":"2026-10-17"},{"id":"evo1","name":"Evo-1","title":"Evo-1: Lightweight Vision-Language-Action Model with Preserved Semantic Alignment","date":"2025-11-06","year":2025,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Uses a compact VLM and staged action alignment to retain semantic features while learning manipulation from modest data.","institution":"Shanghai Jiao Tong University / EvoMind Tech","venue":"CVPR 2026 (official repository)","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2511.04555","project":"https://mint-sjtu.github.io/Evo-1.io/","code":"https://github.com/MINT-SJTU/Evo-1","data":"https://huggingface.co/datasets/MINT-SJTU/Evo1_MetaWorld_Dataset","weights":"https://huggingface.co/MINT-SJTU/Evo1_LIBERO"},"policy":{"family":"VLA","baseModel":"InternVL3-1B family with Qwen2.5-0.5B language component; flow-matching action expert","modelSize":"0.77B total reported for evaluated compact policy","inputs":"RGB observations, language and padded proprioceptive state","outputs":"Flow action chunks; default horizon 50, state/action padded to 24 dimensions","trainingData":"No prior robot-data pretraining; benchmark demos and 100 teleoperation demos/task for four real tasks","trainingTime":"Meta-World default Stage 1 10k + Stage 2 65k; other benchmarks adjust step counts; wall-clock not reported","trainingHardware":"8 × NVIDIA A100 for paper experiments; inference RTX4090D","controlRate":"16.4 Hz measured policy inference on RTX4090D; 2.3 GB inference memory","trainingMethod":"Freeze VLM while aligning integration/action modules → jointly fine-tune; later release supports RLinf GRPO","evaluation":"Meta-World, LIBERO, RoboTwin release and four real manipulation tasks with visual perturbations"},"release":{"code":"Available","weights":"Available","data":"Available","license":"MIT","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2511.04555","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/MINT-SJTU/Evo-1","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."},{"url":"https://raw.githubusercontent.com/MINT-SJTU/Evo-1/main/LICENSE","label":"Repository license file","fields":["release.license"],"note":"Code scope; model, data and third-party component terms can differ."}],"caveats":["Reported 1B backbone family name and 0.77B evaluated total are manuscript conventions; do not substitute an assumed 1B total."],"verified":"2026-10-17"},{"id":"hrt1","name":"HRT1","title":"HRT1: One-Shot Human-to-Robot Trajectory Transfer for Mobile Manipulation","date":"2025-10-23","year":2025,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"AR-headset RGB-D demonstrations yield human hand trajectories that are transferred and optimized for a mobile manipulator.","institution":"University of Texas at Dallas / XPeng","venue":"Preprint","modalities":["RGB","Depth","Proprioception"],"environments":["Real world"],"embodiments":["Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2510.21026","project":"https://irvlutd.github.io/HRT1","code":"https://github.com/IRVLUTD/HRT1"},"policy":{"family":"Geometric transfer","baseModel":"One-shot human-to-robot trajectory transfer. GroundingDINO, SAM2, HaMeR, BundleSDF, unified gripper coordinate transfer and base/joint trajectory optimization.","modelSize":"Not reported","inputs":"HoloLens2 human RGB-D demonstration, object/hand tracks, current scene RGB-D and robot kinematics.","outputs":"Transferred gripper reference trajectory, optimized mobile-base position and joint trajectory executed with MoveIt/TOPP-RA.","trainingData":"One human demonstration for each task; pretrained perception modules, no new action-model training.","trainingTime":"No task-specific training; demonstration trajectory optimization takes approximately 7–10 seconds for 300–400 frames.","trainingHardware":"Online perception/planning on a laptop with an RTX 4090; not a training requirement.","controlRate":"Not reported","trainingMethod":"Estimate human hand/object motion, transfer through object frames and solve constrained robot trajectory optimization.","evaluation":"Sixteen real Fetch household manipulation tasks with three object-placement/environment trials each."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT code; dependency/model/data terms separate","notes":"Author-linked repository and actual README/tree inspected; weights/data are tracked separately. No installation or reproduction attempted."},"evidence":[{"url":"https://arxiv.org/abs/2510.21026","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://irvlutd.github.io/HRT1","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://github.com/IRVLUTD/HRT1","label":"Official code","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://raw.githubusercontent.com/IRVLUTD/HRT1/main/LICENSE","label":"Actual source license file","fields":["release.license"],"note":"Scope limited to the file/repository subtree carrying these terms, not paper figures or external models/data."}],"caveats":["Authors manually verify object poses during execution, preventing fully autonomous deployment.","Using a dexterous-hand coordinate representation internally does not make the evaluated Fetch system a dexterous-hand policy."],"verified":"2026-10-17"},{"id":"memer","name":"MemER","title":"MemER: Scaling Up Memory for Robot Control via Experience Retrieval","date":"2025-10-23","year":2025,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A hierarchical memory policy selects persistent visual keyframes to generate subtasks for a low-level VLA during long-horizon manipulation.","institution":"Stanford University","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2510.20328","project":"https://jen-pan.github.io/memer/","code":"https://github.com/memer-policy/memer","data":"https://huggingface.co/datasets/ajaysri/dusting_train_50_v3_subtasks_rgb"},"policy":{"family":"Hybrid","baseModel":"Paper: Qwen2.5-VL-7B-Instruct high-level model and π0.5 low-level policy. Current dusting release instead provides a Qwen3-VL-4B high-level checkpoint.","modelSize":"7B paper high-level VLM; released high-level variant is 4B. Full hierarchical aggregate not reported.","inputs":"Recent RGB frames, retained visual keyframes, instruction and low-level proprioception.","outputs":"High-level text subtasks and memory keyframe selections; low-level 15-action chunk, executing eight before replanning.","trainingData":"Demonstrations for Object Search, Counting and Dust & Replace, supplemented with subtask annotations; public release includes dusting train/evaluation data.","trainingTime":"High level: 4,500 gradient steps / 96 H200 GPU-hours. Low level: 18,000 steps / 48 H200 GPU-hours.","trainingHardware":"NVIDIA H200; budgets above are GPU-hours, not wall-clock durations.","controlRate":"High-level approximately 1 Hz; low-level chunk prediction approximately 2 Hz; action trajectory sampled at 15 Hz.","trainingMethod":"Supervised high-level keyframe/subtask fine-tuning and low-level π0.5 fine-tuning, with model interpolation to retain robustness.","evaluation":"Three real-world memory tasks: Object Search, Counting, Dust & Replace."},"release":{"code":"Partial","weights":"Partial","data":"Partial","license":"Not verified","notes":"Public repository provides high-level deployment/data conversion, a dusting checkpoint and example task data; its Qwen3-VL-4B release differs from the Qwen2.5-VL-7B paper experiment."},"evidence":[{"url":"https://arxiv.org/abs/2510.20328","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://jen-pan.github.io/memer/","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/memer-policy/memer","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."}],"caveats":["The high-level and action/servo rates are distinct.","Released high-level checkpoint is a later model variant, not the exact paper backbone."],"verified":"2026-10-17"},{"id":"falcon","name":"FALCON — From Spatial to Actions","title":"From Spatial to Actions: Grounding Vision-Language-Action Model in Spatial Foundation Priors","date":"2025-10-20","year":2025,"collections":["policy"],"categories":["rgbd-3d","generalist-foundation"],"resourceType":"policy","summary":"Grounds a VLA in a spatial foundation model, supporting RGB-only and optional measured-depth/point-cloud variants.","institution":"ByteDance / National University of Singapore / NTU","venue":"ICLR 2026","modalities":["RGB","Depth","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2510.17439","project":"https://github.com/FALCON-VLA/FALCON","code":"https://github.com/FALCON-VLA/FALCON","data":"https://huggingface.co/FALCON-VLA/datasets","weights":"https://huggingface.co/FALCON-VLA/FALCON-series"},"policy":{"family":"VLA","baseModel":"Kosmos-2 VLM, VGGT-derived Embodied Spatial Model and spatial-enhanced LSTM/MLP action head","modelSize":"2.9B total: approximately 1.6B VLM plus 1B spatial model and action/interface components","inputs":"RGB, language and optional depth/camera poses; released RGB/WDepth/3DPC configurations must be distinguished","outputs":"7D end-effector pose plus binary gripper; CALVIN horizon 10, Simpler/physical horizon five","trainingData":"CALVIN/OXE and physical task demonstrations; spatial-model data follow VGGT-style geometry training","trainingTime":"Spatial model approximately two days; full VLA wall-clock Not reported","trainingHardware":"Spatial model 16 A100; overall policy experiments 32 A100","controlRate":"Approximately 57 Hz reported physical inference on one RTX 4090, using about 12.8 GB; exact query/action execution convention should accompany comparisons","trainingMethod":"Geometry-model refinement, zero-initialized feature alignment, then VLM/interface refinement with frozen spatial/action components","evaluation":"CALVIN, SIMPLER Google/WidowX, physical base tasks/few-shot/spatial prompting"},"release":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 code; weights/data/backbone terms separate","notes":"Availability concerns author-linked artifacts; no training or installation reproduced."},"evidence":[{"url":"https://arxiv.org/abs/2510.17439","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/FALCON-VLA/FALCON","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://github.com/FALCON-VLA/FALCON","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."}],"caveats":["RGB-only headline performance and optional WDepth/3DPC settings are not interchangeable.","Two days is the spatial-model stage, not full policy training time."],"verified":"2026-10-17"},{"id":"rl100","name":"RL-100","title":"RL-100: Performant Robotic Manipulation with Real-World Reinforcement Learning","date":"2025-10-16","year":2025,"collections":["policy"],"categories":["visual-precision-rl"],"resourceType":"policy","summary":"Builds an imitation-to-RL data flywheel with iterative offline optimization, online refinement and one-step policy distillation.","institution":"Shanghai Qi Zhi Institute / Tsinghua University","venue":"Science Robotics 2026 (official repository)","modalities":["RGB","Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2510.14830","project":"https://lei-kun.github.io/RL-100/","code":"https://github.com/Lei-Kun/RL-100","data":"https://huggingface.co/datasets/leokk/RL-100-adroit-door-medium"},"policy":{"family":"RL","baseModel":"3D point-cloud or RGB encoder + diffusion/flow actor; critic and consistency-model distillation","modelSize":"Not reported","inputs":"Measured-depth point clouds or RGB plus proprioception, depending on policy variant","outputs":"Action chunks or single-action commands; one-step deployed consistency/flow actor","trainingData":"Task-specific teleoperation initialization plus repeated real robot rollout aggregation; exact counts vary by task","trainingTime":"Full training wall-clock not reported; additional fine-tuning on each tested task variation takes1–3 h (excludes base-policy training)","trainingHardware":"Not reported","controlRate":"Real UR5: 30 Hz relative commands interpolated to 125 Hz; single-step camera pipeline capped at 30 Hz","trainingMethod":"Behavior cloning → iterative offline policy-gradient RL → optional online RL → one-step distillation","evaluation":"Current v4: eight real tasks, including dynamic unscrewing, pouring, towel/box folding, Push-T, bowling and juicing"},"release":{"code":"Available","weights":"Not verified","data":"Partial","license":"Apache-2.0 (code); model and data terms not verified","notes":"Official training/deployment library and an Adroit smoke-test dataset are released; complete real data/weights not verified."},"evidence":[{"url":"https://arxiv.org/abs/2510.14830","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/Lei-Kun/RL-100","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://lei-kun.github.io/RL-100/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/Lei-Kun/RL-100/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."},{"url":"https://arxiv.org/pdf/2510.14830","label":"Independent review:currentv4 generalization/few-shot adaptation experiments","fields":["policy.trainingTime"],"note":"The 1–3 h figure is additional training per task variation, not total real-world RL training cost."}],"caveats":["Current v4 PDF reports eight tasks / 1,000 trials; older project and abstract versions report seven / 900.","100% is the observed evaluated success, not a guarantee of universal reliability.","Released smoke-test dataset is Adroit simulation, not the complete real-robot collection."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Partial","license":"Apache-2.0 (code); model and data terms not verified","notes":"Official training/deployment library and an Adroit smoke-test dataset are released; complete real data/weights not verified."}},"relatedIds":[]},{"id":"vt-refine","name":"VT-Refine","title":"VT-Refine: Learning Bimanual Assembly with Visuo-Tactile Feedback via Simulation Fine-Tuning","date":"2025-10-16","year":2025,"collections":["policy"],"categories":["tactile-il","rgbd-3d"],"resourceType":"policy","summary":"Improves demonstration-trained visuotactile diffusion policies using parallel simulation RL and transfers them back to bimanual real assembly.","institution":"NVIDIA / UIUC","venue":"CoRL 2025","modalities":["Depth","Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2510.14930","project":"https://binghao-huang.github.io/vt_refine/","code":"https://github.com/NVlabs/vt-refine"},"policy":{"family":"Hybrid","baseModel":"3D point-based Diffusion Policy + diffusion-policy PPO","modelSize":"Not reported","inputs":"Depth-derived visual points, spatial pressure taxels and proprioception","outputs":"Bimanual robot actions","trainingData":"Typically 30 real demonstrations per task; ablations use 10/30/50","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Behavior cloning → digital twin RL refinement → real deployment","evaluation":"Contact-rich bimanual assembly on WidowX and Kinova-based setups"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Official repository supplies simulation, training and deployment components; external assets may require separate downloads."},"evidence":[{"url":"https://arxiv.org/abs/2510.14930","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/NVlabs/vt-refine","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2510.14930","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Uses normal-pressure tactile pads, not optical image sensors; shear and deformable-object mechanics are outside the modeled sensor scope."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Official repository supplies simulation, training and deployment components; external assets may require separate downloads."}},"relatedIds":[]},{"id":"depthvla","name":"DepthVLA","title":"DepthVLA: Enhancing Vision-Language-Action Models with Depth-Aware Spatial Reasoning","date":"2025-10-15","year":2025,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"A depth expert shares attention with vision-language and action experts to improve RGB-derived spatial reasoning.","institution":"Tsinghua University IIIS; Galaxea AI","venue":"arXiv preprint","modalities":["RGB","Language","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2510.13375"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2510.13375","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."}],"caveats":["Predicted depth features are not a measured depth camera stream.","Training/evaluation data and joint-all-suite LIBERO protocol differ from some published per-suite baselines."],"verified":"2026-10-17","policy":{"family":"VLA","baseModel":"PaliGemma-3B, DINOv2-L / Depth-Anything-V2 initialization and mixture-of-transformers depth/action experts.","modelSize":"PaliGemma3B backbone; 600M added vs π₀ (300M image encoder + 300M depth decoder)","inputs":"RGB, language and proprioception; depth expert infers geometry from images.","outputs":"Flow-matching action chunks and auxiliary predicted depth.","trainingMethod":"Depth-expert geometric pretraining then end-to-end VLA learning with action/depth objectives.","trainingData":"WildRGB-D, ScanNet/ScanNet++ and HyperSim geometry data; Bridge/Galaxea and target task demonstrations.","trainingTime":"Depth pretraining 50K steps; Bridge 20K, LIBERO 30K steps; wall-clock not reported","trainingHardware":"32 NVIDIA H100 GPUs","evaluation":"LIBERO, Simpler WidowX and Galaxea R1 Lite physical manipulation.","controlRate":"RTX 4090 BF16:210 ms/policy query; 16-action ~one-second chunk on a15 Hz platform."}},{"id":"farm","name":"FARM","title":"Tactile-Conditioned Diffusion Policy for Force-Aware Robotic Manipulation","date":"2025-10-15","year":2025,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"Uses tactile force distributions and a force-aware action space to jointly control pose, grip width and grip force.","institution":"TU Darmstadt","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2510.13324","project":"https://tactile-farm.github.io/"},"policy":{"family":"Diffusion","baseModel":"1D temporal CNN Diffusion Policy with FiLM conditioning; FEATS force estimator","modelSize":"Not reported","inputs":"Visual observations, proprioception and GelSight-derived spatial force distributions","outputs":"Absolute end-effector pose, gripper width and target grip force trajectories","trainingData":"Modified handheld UMI demonstrations across three force-sensitive task settings; count not reported","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Policy ≈7 Hz; tactile force loop 25 Hz; actuated gripper ≈50 Hz","trainingMethod":"Tactile-conditioned imitation plus contact-gated width/force low-level control","evaluation":"High-force, low-force and dynamically varying-force manipulation"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."},"evidence":[{"url":"https://arxiv.org/abs/2510.13324","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://tactile-farm.github.io/","label":"Author project page","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2510.13324","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["FEATS forces are inferred from tactile images rather than directly calibrated force measurements.","RealSense hardware presence does not establish depth as a policy input."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."}},"relatedIds":[]},{"id":"libero-plus","name":"LIBERO-Plus","title":"LIBERO-Plus: A Progressive Robustness Benchmark for Visual-Language-Action Models","date":"2025-10-15","year":2025,"collections":["benchmark"],"categories":["robustness-evaluation"],"resourceType":"benchmark","summary":"Automated perturbation generation and progressive difficulty for seven robustness axes built on LIBERO manipulation tasks.","institution":"Fudan University / Tongji University / Shanghai Innovation Institute","venue":"CVPR 2026","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2510.13626","project":"https://sylvestf.github.io/LIBERO-plus/","code":"https://github.com/sylvestf/LIBERO-plus","data":"https://huggingface.co/collections/Sylvest/libero-plus"},"benchmark":{"simulator":"MuJoCo / robosuite / LIBERO fork","simulatorVersion":"Release version varies; exact MuJoCo pin Not verified from upstream source. Downstream model-repo pins should not be presented as benchmark paper version.","taskCount":"10,030 generated task–perturbation configurations over seven axes / twenty-one components; not 10,030 new semantic skills.","tasks":["Object layouts","Camera viewpoints","Robot initial states","Language instructions","Lighting conditions","Background textures","Sensor noise"],"taskListScope":"Complete seven perturbation axes; semantic tasks inherited from LIBERO and generated configuration manifest defines all 10,030 variants.","capabilities":["Progressive robustness","Language reliance","Camera/layout invariance","Sensor corruption"],"metrics":"Success per perturbation/component/difficulty level; robustness degradation; generalization after expanded training.","robots":"Franka Panda in LIBERO","sensors":"RGB, proprioception and language; sensor noise corrupts visual inputs rather than introducing tactile streams.","dataCollection":"Original LIBERO human-teleoperated training demonstrations are reused; perturbations generate evaluation instances. The collector for additional reported trajectories is Not verified.","data":"Original LIBERO training; expanded generalization dataset includes 22,400 successful trajectories in manuscript study.","protocol":"Zero-shot perturbation evaluation of LIBERO-trained checkpoints; generated configurations grouped into five difficulty levels; separate retraining experiments on generalized sets.","baselines":"Ten models in accepted study; generated suite stratification uses OpenVLA-OFT, π0, π0-fast and UniVLA."},"release":{"code":"Available","weights":"Not applicable","data":"Available","license":"Not verified","notes":"Perturbation assets/configurations and data released; policies are external checkpoints."},"evidence":[{"url":"https://arxiv.org/abs/2510.13626","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://sylvestf.github.io/LIBERO-plus/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/sylvestf/LIBERO-plus","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://arxiv.org/abs/2510.13626","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."},{"url":"https://arxiv.org/abs/2306.03310","label":"Original LIBERO demonstration capture","fields":["benchmark.dataCollection"],"note":"Primary source for the human SpaceMouse demonstrations inherited by this benchmark; new evaluation configurations are not a new capture interface."}],"caveats":["arXiv title is In-depth Robustness Analysis; accepted CVPR title is A Progressive Robustness Benchmark.","10,030 counts perturbation configurations, not distinct everyday manipulation skills."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not applicable","data":"Available","license":"Not verified","notes":"Perturbation assets/configurations and data released; policies are external checkpoints."}},"relatedIds":[]},{"id":"spatial-forcing","name":"Spatial Forcing","title":"Spatial Forcing: Implicit Spatial Representation Alignment for Vision-language-action Model","date":"2025-10-14","year":2025,"collections":["policy"],"categories":["rgbd-3d","generalist-foundation"],"resourceType":"policy","summary":"Aligns intermediate RGB-VLA features to frozen geometric foundation-model representations without requiring depth at deployment.","institution":"Tsinghua University / HKUST(Guangzhou)","venue":"ICLR 2026","modalities":["RGB","Language","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2510.12276","project":"https://spatial-forcing.github.io/","code":"https://github.com/OpenHelix-Team/Spatial-Forcing","weights":"https://huggingface.co/collections/haofuly/spatial-forcing-68ea1bf0f1ac2c60e2ec6caa"},"policy":{"family":"VLA","baseModel":"OpenVLA-OFT on LIBERO or π0/OpenPI on RoboTwin; frozen VGGT geometric teacher.","modelSize":"Base-model dependent; no single complete policy count reported.","inputs":"RGB, language and base-policy state inputs. Neither measured depth nor a depth estimator is required at deployment.","outputs":"Continuous action chunks through the selected OFT or π0 head.","trainingData":"Official LIBERO and RoboTwin demonstrations; geometry teacher supplies feature targets.","trainingTime":"OFT/LIBERO: 150k iterations. π0/RoboTwin: 30k iterations. Wall-clock duration not reported.","trainingHardware":"Eight H100 GPUs for OFT/LIBERO; one H100 for π0 LoRA/RoboTwin.","controlRate":"Not reported","trainingMethod":"Intermediate spatial-feature alignment to VGGT; action learning follows the chosen base policy.","evaluation":"LIBERO, RoboTwin and physical manipulation; viewpoint, data-efficiency and representation studies."},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"MIT code; weights/data/backbone terms separate","notes":"Availability concerns author-linked artifacts; no training or installation reproduced."},"evidence":[{"url":"https://arxiv.org/abs/2510.12276","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/OpenHelix-Team/Spatial-Forcing","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://spatial-forcing.github.io/","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."}],"caveats":["Geometry is training supervision; this is not a measured RGB-D policy or a world-action model.","OFT and π0 variants have different backbones, budgets and action interfaces."],"verified":"2026-10-17"},{"id":"xvla","name":"X-VLA","title":"X-VLA: Soft-Prompted Transformer as Scalable Cross-Embodiment Vision-Language-Action Model","date":"2025-10-11","year":2025,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Uses embodiment-specific soft prompts in a shared Transformer to learn across heterogeneous robots and adapt efficiently.","institution":"Tsinghua University","venue":"ICLR 2026","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2510.10274","project":"https://thu-air-dream.github.io/X-VLA/","code":"https://github.com/2toinf/X-VLA","weights":"https://huggingface.co/2toINF/X-VLA-Pt"},"policy":{"family":"VLA","baseModel":"Florence2-based visual/language encoder + shared flow-matching Transformer + soft prompts","modelSize":"0.9B total; prompt-only adaptation updates ~9M (~1%)","inputs":"Multi-view RGB, language, proprioception and learned embodiment/domain prompts","outputs":"Aligned continuous end-effector action chunks; domain-specific decoding for control interface","trainingData":"290k episodes from DROID, RoboMind and AgiBot spanning seven platforms","trainingTime":"Pretraining ~4 days; downstream schedule benchmark-dependent","trainingHardware":"64 A100 GPUs, global batch 1024 for pretraining","controlRate":"Dataset/controller settings vary (e.g. DROID 15 Hz, RoboMind 30 Hz); no single feedback rate","trainingMethod":"Cross-embodiment pretraining, then full or prompt-only adaptation; flow matching","evaluation":"Six simulation platforms and three real robots; LIBERO/CALVIN/RoboTwin2/SimplerEnv and cloth folding"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 (official code); model/data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2510.10274","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/2toinf/X-VLA","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://thu-air-dream.github.io/X-VLA/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."}],"caveats":["Soft prompt only and full fine-tuning have different compute and performance.","Official README notes a~1% drop after some HF format conversions; use versioned checkpoint/evaluation settings."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 (official code); model/data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"wristworld","name":"WristWorld","title":"WristWorld: Generating Wrist-Views via 4D World Models for Robotic Manipulation","date":"2025-10-08","year":2025,"collections":["policy"],"categories":["geometry-models","rgbd-3d"],"resourceType":"model","summary":"Generates missing wrist-camera videos from anchor-camera sequences to augment manipulation-policy training.","institution":"Peking University; collaborating institutions as manuscript","venue":"arXiv preprint","modalities":["RGB"],"environments":["Simulation","Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2510.07313"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2510.07313","label":"Original manuscript and first submission history","fields":["title","date","summary","model","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."}],"caveats":["WristWorld is a data/view generator; its output is not a tactile observation or independently executable policy."],"verified":"2026-10-17","model":{"role":"Cross-view generation and policy data augmentation","family":"Predictive world model","baseModel":"VGGT-based 4D reconstruction with spatial-projection consistency, followed by video synthesis.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Anchor-view RGB sequences and specified wrist-view geometry; no observed wrist first frame required.","outputs":"Synthesized wrist-view videos and intermediate geometry/poses.","trainingMethod":"Reconstruction/generation learning; generated views used as downstream VLA training augmentation.","trainingData":"DROID and CALVIN anchor/wrist video data; task-specific downstream policy demonstrations.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Video quality/spatial consistency and downstream CALVIN/Franka policy comparisons.","inference":"Not reported in the inspected primary-source sections"}},{"id":"libero-pro","name":"LIBERO-PRO","title":"LIBERO-PRO: Towards Robust and Fair Evaluation of Vision-Language-Action Models Beyond Memorization","date":"2025-10-04","year":2025,"collections":["benchmark"],"categories":["robustness-evaluation"],"resourceType":"benchmark","summary":"LIBERO evaluation extension probing object, position, semantic, task and environment generalization without changing original training data.","institution":"Huazhong University of Science and Technology","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2510.03827","project":"https://zxy-mllab.github.io/LIBERO-PRO-Webpage/","code":"https://github.com/Zxy-MLlab/LIBERO-PRO","data":"https://huggingface.co/datasets/zhouxueyang/LIBERO-Pro"},"benchmark":{"simulator":"MuJoCo / robosuite / LIBERO fork","simulatorVersion":"Same runtime family as original LIBERO; official README Python 3.8.13. Exact MuJoCo build Not reported here.","taskCount":"Perturbation extensions over four commonly evaluated LIBERO suites; configurable and combinable conditions, not a fixed new semantic task count.","tasks":["Object generalization","Position generalization","Semantic generalization","Task generalization","Environment generalization"],"taskListScope":"Complete five current repository axes; paper abstract groups four broader dimensions. Original semantic tasks and generated BDDL/init files define variants.","capabilities":["Position robustness","Instruction understanding","Object substitution","Procedural generalization","Environment shift"],"metrics":"Success under original vs perturbed conditions; instruction/target-dependence diagnostics.","robots":"Franka Panda in LIBERO","sensors":"RGB, proprioception and language; no tactile stream.","dataCollection":"Reuses original LIBERO human demonstrations, collected with SpaceMouse teleoperation. New BDDL/initial-state configurations extend evaluation rather than establish a new collection interface.","data":"Original LIBERO demonstrations retained; generated BDDL and initial-state files released for evaluation.","protocol":"Configure perturbations through YAML; task generalization cannot be combined with other axes in current implementation; compare same trained checkpoint under shifted evaluation.","baselines":"OpenVLA, π0, π0.5, UniVLA and evolving VLA evaluations."},"release":{"code":"Available","weights":"Not applicable","data":"Available","license":"MIT code; added assets/data terms separate","notes":"Author repository is Zxy-MLlab; RLinf hosts a fork. Official BDDL/init data release linked by README."},"evidence":[{"url":"https://arxiv.org/abs/2510.03827","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://zxy-mllab.github.io/LIBERO-PRO-Webpage/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/Zxy-MLlab/LIBERO-PRO","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://github.com/Zxy-MLlab/LIBERO-PRO/blob/master/LICENSE","label":"Official source","fields":["release.license"],"note":"License text read directly; code license does not automatically cover models, data or simulator."},{"url":"https://arxiv.org/abs/2510.03827","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."},{"url":"https://arxiv.org/abs/2306.03310","label":"Original LIBERO demonstration capture","fields":["benchmark.dataCollection"],"note":"Primary source for the human SpaceMouse demonstrations inherited by this benchmark; new evaluation configurations are not a new capture interface."}],"caveats":["Paper four-dimensional grouping and current repository five axes are different descriptions, not contradictory task totals.","Use official author source rather than treating a fork as the primary release."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not applicable","data":"Available","license":"MIT code; added assets/data terms separate","notes":"Author repository is Zxy-MLlab; RLinf hosts a fork. Official BDDL/init data release linked by README."}},"relatedIds":[]},{"id":"mla","name":"MLA","title":"MLA: A Multisensory Language-Action Model for Multimodal Understanding and Forecasting in Robotic Manipulation","date":"2025-09-30","year":2025,"collections":["policy"],"categories":["tactile-foundation","rgbd-3d"],"resourceType":"policy","summary":"Aligns image, point-cloud and tactile tokens inside an LLM-based policy with auxiliary future forecasting.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Depth","Tactile","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2509.26642","code":"https://github.com/ZhuoyangLiu2005/MLA"},"policy":{"family":"VLA","baseModel":"LLaMA-2 7B with shared multimodal token space and diffusion action head","modelSize":"7B LLM backbone component; total not reported","inputs":"Multi-view RGB, depth-derived point clouds, tactile signals, language and state","outputs":"Diffusion action sequences; training also forecasts sensory futures","trainingData":"Pretraining: 570k trajectories / 36M frames from 28 OXE, DROID and RoboMIND sources; tactile added in downstream training","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Vision-language/action pretraining followed by spatially aligned multisensory fine-tuning","evaluation":"RLBench (without tactile) and six real-world tasks with multimodal sensing"},"release":{"code":"Available","weights":"Partial","data":"Not verified","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Repository provides training/evaluation; simulation checkpoint is not evidence for full real RGBDT release."},"evidence":[{"url":"https://arxiv.org/abs/2509.26642","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/ZhuoyangLiu2005/MLA","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2509.26642","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."},{"url":"https://github.com/ZhuoyangLiu2005/MLA/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["Large pretraining corpus has RGB/state/language, with empty 3D/tactile token slots.","RLBench evaluation has no tactile observations."],"verified":"2026-10-19","releaseByCollection":{"policy":{"code":"Available","weights":"Partial","data":"Not verified","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Repository provides training/evaluation; simulation checkpoint is not evidence for full real RGBDT release."}},"relatedIds":[]},{"id":"af-osil-ms","name":"AF-OSIL-MS","title":"Annotation-Free One-Shot Imitation Learning for Multi-Step Manipulation Tasks","date":"2025-09-29","year":2025,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"A VLM decomposes one unannotated robot demonstration into image-alignment and trajectory-replay primitives.","institution":"University of Cambridge / University of Lincoln","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2509.24972","code":"https://github.com/vijja-w/AF-OSIL-MS"},"policy":{"family":"Geometric transfer","baseModel":"Annotation-free multi-step alignment/replay imitation. Gemini 2.5 Pro, Spatial Perception Encoder, SAM2.1 Hiera Large and geometric 3D alignment/replay.","modelSize":"Not reported","inputs":"Wrist RGB-D demonstration with end-effector poses and gripper widths; VLM receives 1 fps RGB plus 1 Hz gripper context and task prompt.","outputs":"Sequenced bottleneck alignment, stored-waypoint execution and pullback primitives.","trainingData":"One robot demonstration, no additional target-task collection, model training or manual annotation.","trainingTime":"No target-task model training; hosted VLM inference is still required.","trainingHardware":"Physical execution on RTX 4070 SUPER and Intel i7-14700KF; cloud VLM resources not disclosed.","controlRate":"Not reported","trainingMethod":"VLM semantic decomposition and automatic visual targets; geometric visual servoing followed by open-loop replay.","evaluation":"Franka Panda multi-step Stack Blocks, Sort Fruits, Tea Preparation and Wipe tasks, plus single-step and correspondence-component studies."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"No repository-wide license found in inspected tree","notes":"Author-linked repository and actual README/tree inspected; weights/data are tracked separately. No installation or reproduction attempted."},"evidence":[{"url":"https://arxiv.org/abs/2509.24972","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://github.com/vijja-w/AF-OSIL-MS","label":"Official code","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."}],"caveats":["The interaction replay is open-loop; identical duplicate objects and out-of-view targets remain limitations.","Baseline KAT/Spectral used substituted Gemini rather than the original model/prompt, so direct method comparisons are confounded."],"verified":"2026-10-17"},{"id":"lyra","name":"LYRA","title":"Robotic Ultra-Long-Horizon Manipulation Skills via Human-guided Lifelong Code Generation","date":"2025-09-23","year":2025,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Distills human feedback into modular reusable skills and retrieves skill / example memory to generate ultra-long-horizon robot programs.","institution":"Technical University of Munich; BAAI; XYZ Embodied AI; Nanjing University","venue":"IEEE Robotics and Automation Letters 2026 (accepted)","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2509.18597"},"policy":{"family":"Robot agent","baseModel":"Language-model code generation with separate skill and few-shot-example vector memories; GPT-4o-family comparisons are reported.","modelSize":"Not reported","inputs":"Task, human corrections / hints, execution feedback and retrieved code examples / skill docstrings.","outputs":"Generated robot programs and incrementally extended library skills, retaining previous skill behavior.","trainingData":"Human-guided lifelong code-learning episodes; the paper also collects over 30 trajectories per selected task for separate π0 / OpenVLA comparison fine-tuning.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen code model in the skill-learning framework; human-guided program / external-memory updates. Fine-tuned VLA comparisons are separate methods.","evaluation":"Ravens, Franka Kitchen, LIBERO-long, MetaWorld and physical ultra-long-horizon manipulation."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The original paper is public; code, model and dataset release scopes are verified separately where established. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms."},"evidence":[{"url":"https://arxiv.org/abs/2509.18597","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."}],"caveats":["The current primary title is Robotic Ultra-Long-Horizon Manipulation Skills via Human-guided Lifelong Code Generation; the survey cites an earlier title.","Human feedback and retrieval hints are part of acquisition; baseline VLA training is not LYRA base-model training."],"verified":"2026-10-17"},{"id":"rynnvla-001","name":"RynnVLA-001","title":"RynnVLA-001: Using Human Demonstrations to Improve Robot Manipulation","date":"2025-09-18","year":2025,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Human-video generative priors are transferred into a robot action policy.","institution":"DAMO Academy, Alibaba Group; Hupan Lab","venue":"ICRA 2026","modalities":["RGB","Language","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2509.15212","project":"https://alibaba-damo-academy.github.io/RynnVLA-001/","code":"https://github.com/alibaba-damo-academy/RynnVLA-001"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Apache-2.0 for repository code; pretrained models, data and figure rights remain separate","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2509.15212","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://alibaba-damo-academy.github.io/RynnVLA-001/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/alibaba-damo-academy/RynnVLA-001","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/alibaba-damo-academy/RynnVLA-001/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/alibaba-damo-academy/RynnVLA-001/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"72 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["Included as a related Rynn policy foundation; the method does not establish measured-depth or tactile conditioning."],"verified":"2026-10-17","policy":{"family":"VLA","baseModel":"Lumina-mGPT / Chameleon-derived image-video-action architecture.","modelSize":"Not reported in the inspected primary-source sections","inputs":"RGB observations, language and robot state as required by the released control setting.","outputs":"Robot action sequences.","trainingMethod":"Human-demonstration generative pretraining followed by robot action finetuning.","trainingData":"Human demonstrations and robot manipulation trajectories; exact subset counts in manuscript.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Simulated and physical manipulation transfer.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"mimicdroid","name":"MimicDroid","title":"MimicDroid: In-Context Learning for Humanoid Robot Manipulation from Human Play Videos","date":"2025-09-11","year":2025,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Human play videos train a humanoid demonstration-conditioned policy without teleoperated robot training data.","institution":"The University of Texas at Austin","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Bimanual"],"links":{"paper":"https://arxiv.org/abs/2509.09769","project":"https://ut-austin-rpl.github.io/MimicDroid","code":"https://github.com/UT-Austin-RPL/mimicdroid-robocasa","data":"https://huggingface.co/datasets/Rutav/MimicDroidDataset"},"policy":{"family":"Transformer policy","baseModel":"Video-conditioned transformer policy with human-wrist retargeting and cross-trajectory behavior matching.","modelSize":"Not reported","inputs":"Few demonstration videos, current humanoid RGB observations and robot state.","outputs":"Retargeted humanoid manipulation controls learned from human wrist trajectories.","trainingData":"Eight hours of simulated free-hand play over 30 objects / eight kitchen environments; real human-video set has 45 minutes / 80k frames.","trainingTime":"200 epochs, approximately two days for the reported real-world model training.","trainingHardware":"Eight NVIDIA A5000 GPUs for the reported real-world run.","controlRate":"Not reported","trainingMethod":"Mine behavior-similar trajectory pairs; condition one on another, retarget human motion and apply random visual patch masking.","evaluation":"Humanoid manipulation with novel objects / environments in simulation and real world."},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"MIT for repository code; external model, dataset and dependency terms remain separate.","notes":"Official simulation implementation and human-play dataset are public."},"evidence":[{"url":"https://arxiv.org/abs/2509.09769","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://ut-austin-rpl.github.io/MimicDroid","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/UT-Austin-RPL/mimicdroid-robocasa","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."},{"url":"https://github.com/UT-Austin-RPL/mimicdroid-robocasa/blob/HEAD/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Actual license file inspected; code license does not automatically license data or weights."}],"caveats":["Human play-video duration is not teleoperated robot demonstration duration."],"verified":"2026-10-17"},{"id":"ta-vla","name":"TA-VLA","title":"TA-VLA: Elucidating the Design Space of Torque-aware Vision-Language-Action Models","date":"2025-09-09","year":2025,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Studies joint-torque history injection and auxiliary torque prediction in pretrained action models.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Force/Torque","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2509.07962"},"policy":{"family":"VLA","baseModel":"π0 main model; RDT-1B and ACT variants","modelSize":"π0 component sizes: 3B PaliGemma+300M action expert; RDT1B; total with adapters not reported","inputs":"RGB, language, proprioception and joint-torque history","outputs":"Action chunks and auxiliary future torque;π0 horizon 50, RDT64","trainingData":"Not reported","trainingTime":"π0 LoRA30k steps; RDT full40k; ACT600 epochs; full π0 variant1.640 s/iteration","trainingHardware":"4 NVIDIA L20 GPUs for training; RTX4090 inference","controlRate":"Full π0+torque observation/prediction latency93.96 ms; command rate not separately reported","trainingMethod":"Decoder-side torque token, history aggregation and joint action/torque prediction","evaluation":"Ten real tasks including five contact-rich tasks; cross-model and cross-embodiment comparisons"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."},"evidence":[{"url":"https://arxiv.org/abs/2509.07962","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://arxiv.org/html/2509.07962","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Joint torque is proprioceptive physical feedback; there is no dedicated tactile skin/image input.","Inference latency is a model measurement, not a verified end-to-end feedback rate."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."}},"relatedIds":[]},{"id":"text2touch","name":"Text2Touch","title":"Text2Touch: Tactile In-Hand Manipulation with LLM-Designed Reward Functions","date":"2025-09-09","year":2025,"collections":["policy"],"categories":["tactile-foundation","visual-precision-rl"],"resourceType":"policy","summary":"LLM-written rewards train tactile in-hand rotation policies through simulation and teacher–student transfer.","institution":"University of Bristol / Bristol Robotics Laboratory","venue":"CoRL 2025","modalities":["Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2509.07445","project":"https://hpfield.github.io/text2touch-website/","code":"https://github.com/hpfield/Text2Touch"},"policy":{"family":"RL","baseModel":"Eureka-style iterative reward generation with five LLMs; sim-to-real tactile policy distillation.","modelSize":"Not reported","inputs":"Deployed student: hand joint position/velocity and TacTip tactile observations; no runtime LLM required.","outputs":"Dexterous hand joint commands for multi-axis object rotation.","trainingData":"Reward search uses 150 million-step short runs; selected rewards receive 8 billion environment-step training;10 household objects in hardware evaluation.","trainingTime":"Desktop full 8 B-step policy~12 h; full reward-search experiment~24 h. P100 policy~3 days; search~4.5 days.","trainingHardware":"Desktop RTX4090or separate HPC single Tesla P100 jobs.","controlRate":"20 Hz deployed hand control.","trainingMethod":"LLM reward-code generation and selection followed by RL and privileged teacher–student distillation.","evaluation":"Allegro/TacTip palm-up and palm-down rotation across three axes."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT","notes":"Author-linked repository and actual LICENSE inspected; code, checkpoints anddatasets have independent availability."},"evidence":[{"url":"https://arxiv.org/abs/2509.07445","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://hpfield.github.io/text2touch-website/","label":"Official author project","fields":["links","release"],"note":"Live project artifact links inspected on2026-10-17."},{"url":"https://github.com/hpfield/Text2Touch","label":"Official released implementation and LICENSE","fields":["links","release"],"note":"Repository metadata,README and actualLICENSE inspected; availability scoped to the listed component."}],"caveats":["LLM designs training rewards; the deployed actor is a learned tactile controller, not a language-model action policy.","Reward-search and final training costs are distinct runs/hardware settings."],"verified":"2026-10-17"},{"id":"dexop","name":"DEXOP","title":"DEXOP: A Device for Robotic Transfer of Dexterous Human Manipulation","date":"2025-09-04","year":2025,"collections":["policy"],"categories":["tactile-il","tactile-foundation"],"resourceType":"policy","summary":"A passive robot-hand exoskeleton captures transferable visual/tactile demonstrations for bimanual dexterous imitation.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2509.04441","project":"https://dex-op.github.io"},"policy":{"family":"ACT","baseModel":"ACT with wrist images, tactile super-images and joint state queries.","modelSize":"Not reported","inputs":"Two wrist RGB, two hand tactile-delta image composites and arm/hand joint states.","outputs":"Arm/hand joint action chunks.","trainingData":"Evaluated mixture 160 DEXOP+40 teleop demos versus 200 teleop or 100 time-matched teleop demos; task-specific setting.","trainingTime":"800 epochs total; wall-clock Not reported.","trainingHardware":"One NVIDIA H100; batch 110.","controlRate":"Not reported","trainingMethod":"Mixed-data behavior cloning 500 epochs then 300 epochs teleop-only calibration.","evaluation":"Contact-rich dexterous manipulation and data-collection efficiency comparison."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2509.04441","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://dex-op.github.io","label":"Official author project","fields":["links","release"],"note":"Live artifact status reviewed separately from paper claims."}],"caveats":["Mixed teleop subset compensates arm-exoskeleton assembly errors; claimed transfer does not use solely passive device data."],"verified":"2026-10-17"},{"id":"maniflow","name":"ManiFlow","title":"ManiFlow: A General Robot Manipulation Policy via Consistency Flow Training","date":"2025-09-01","year":2025,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Unifies 2 D/3 D manipulation policies with consistency flow training for one- or two-step action generation.","institution":"University of California San Diego / University of Washington / NVIDIA","venue":"CoRL 2025","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Dexterous hand","Humanoid upper body"],"links":{"paper":"https://arxiv.org/abs/2509.01819","project":"https://maniflow-policy.github.io/","code":"https://github.com/geyan21/ManiFlow_Policy","data":"https://github.com/geyan21/ManiFlow_Policy#-data-preparation"},"policy":{"family":"Diffusion","baseModel":"DiT-X adaptive cross-attention; point-cloud or image encoder, optional language conditioning","modelSize":"Not reported","inputs":"RGB images or colored point clouds from measured depth, language when multi-task, and robot state","outputs":"High-dimensional action chunks for arm/gripper or dexterous hands","trainingData":"60+ simulated tasks over four benchmarks; eight real tasks, demonstration count task-dependent","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"1–2 generative steps per query; this is not an Hz measurement","trainingMethod":"Joint flow matching and consistency training without a pretrained teacher","evaluation":"MetaWorld, Adroit, DexArt, RoboTwin; Franka, dual xArm/Ability Hands and H1 humanoid"},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2509.01819","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/geyan21/ManiFlow_Policy","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://maniflow-policy.github.io/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/geyan21/ManiFlow_Policy/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["2D and 3D variants have different observation encoders; they are not simultaneously RGB-D input by default.","One-step generation does not by itself specify sensing or low-level control frequency."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Available","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"galaxea-g0","name":"Galaxea G0","title":"Galaxea Open-World Dataset and G0 Dual-System VLA Model","date":"2025-08-30","year":2025,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Uses a high-level VLM planner and a fast flow-matching VLA controller, trained with temporally annotated mobile-manipulation data.","institution":"Galaxea","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2509.00576","project":"https://opengalaxea.github.io/GalaxeaVLA/","code":"https://github.com/OpenGalaxea/GalaxeaVLA","data":"https://huggingface.co/datasets/OpenGalaxea/Galaxea-Open-World-Dataset","weights":"https://huggingface.co/OpenGalaxea/G0-VLA"},"policy":{"family":"VLA","baseModel":"Qwen2.5-VL-based high-level planner; VLM-conditioned flow action transformer for low-level G0-VLA","modelSize":"Released low-level G0 and G0Plus assets labelled 3B; manuscript total across planner and controller not reported","inputs":"Three RGB cameras, robot proprioception and task/subtask language","outputs":"Bimanual robot action chunks including mobile torso/chassis controls; representation depends on checkpoint","trainingData":"Galaxea Open-World Dataset: 100k trajectories, 500 hours, 150 task categories, 50 scenes; full-policy Stage 2 uses 400 h subset","trainingTime":"Stage 1 autoregressive action-token pretraining, Stage 2 200 h/400 h variants, then task post-training; wall-clock not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"FAST-token VLM pretraining → flow action pretraining → task adaptation; VLM subtask planning","evaluation":"Real Galaxea mobile manipulation, long-horizon tasks, few-shot transfer and high-level planning"},"release":{"code":"Available","weights":"Available","data":"Gated — automatic Hugging Face access terms","license":"Not reported","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested. The current official artifact inventory is gated (automatic access terms); files are listed but anonymous unrestricted download is not claimed."},"evidence":[{"url":"https://arxiv.org/abs/2509.00576","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/OpenGalaxea/GalaxeaVLA","label":"Official policy implementation","fields":["links"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://github.com/OpenGalaxea/GalaxeaVLA","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/datasets/OpenGalaxea/Galaxea-Open-World-Dataset","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/OpenGalaxea/G0-VLA","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["G0, G0Plus and G0Tiny are distinct released assets under the family; RoboDojo G0 uses a G0Plus configuration.","The dataset has RGB-D cameras; the reported G0 controller inputs inspected here are RGB, not measured depth."],"verified":"2026-10-19"},{"id":"simshear","name":"SimShear","title":"SimShear: Sim-to-Real Shear-based Tactile Servoing","date":"2025-08-28","year":2025,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Shear-conditioned sim-to-real tactile translation supports collaborative pose/shear servoing.","institution":"University of Bristol / Bristol Robotics Laboratory","venue":"Preprint","modalities":["Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2508.20561","project":"https://yijionglin.github.io/simshear/"},"policy":{"family":"Hybrid","baseModel":"sh Pix 2 pix conditional U-Net GAN; Gaussian-density pose/shear estimator and tactile servo controller.","modelSize":"Not reported","inputs":"Optical tactile images; inferred pose/shear fed to feedback controller.","outputs":"Follower-arm servo motions for tactile tracking and co-lifting.","trainingData":"5,000 paired training tuples+2,000 validation;130 min real collection and 60 s simulation collection.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Image translation 100 epochs; pose/shear estimator 50 epochs; feedback-control deployment.","evaluation":"Two Dobot MG400arms: collaborative tactile tracking and co-lift."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2508.20561","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://yijionglin.github.io/simshear/","label":"Official author project","fields":["links","release"],"note":"Live project artifact links inspected on2026-10-17."}],"caveats":["Co-lift evaluation uses relatively light objects; weight-dependent vertical-shear compensation is a stated limitation."],"verified":"2026-10-17"},{"id":"memoryvla","name":"MemoryVLA","title":"MemoryVLA: Perceptual-Cognitive Memory in Vision-Language-Action Models for Robotic Manipulation","date":"2025-08-26","year":2025,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Retrieves and consolidates perceptual/cognitive memory tokens to condition a diffusion robot policy.","institution":"Tsinghua University / Dexmal","venue":"ICLR2026 (official repository)","modalities":["RGB","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2508.19236","project":"https://shihao1895.github.io/MemoryVLA","code":"https://github.com/shihao1895/MemoryVLA/tree/openvla-codebase","data":"https://huggingface.co/datasets/shihao1895/libero-rlds","weights":"https://huggingface.co/collections/shihao1895/memoryvla"},"policy":{"family":"VLA","baseModel":"Prismatic VLM (OpenVLA initialization for LIBERO; CogACT-Large for Bridge/Fractal); DINOv2 + SigLIP and LLaMA-7B; diffusion action transformer","modelSize":"7B language backbone + approximately 300M action expert; image encoders and memory are additional","inputs":"RGB observation/history and language; paper architecture does not require measured depth or tactile","outputs":"Continuous 7-DoF actions through diffusion, with future action sequence generation","trainingData":"OXE-pretrained backbone; BridgeV2, RT-1, LIBERO/MIKASA and real-task demonstrations; LIBERO50 demos/task","trainingTime":"Bridge50k, RT-1 80k; LIBERO20k per short suite, 40k joint Long10/90; wall-clock not reported","trainingHardware":"8 × NVIDIA A100, global batch 256","controlRate":"10DDIM sampling steps; true closed-loop feedback rate not reported","trainingMethod":"Supervised policy learning with perceptual-cognitive memory retrieval, consolidation and diffusion actions","evaluation":"SimplerEnv Bridge/Fractal, LIBERO, MIKASA-Robo and real Franka/temporal/OOD tasks"},"release":{"code":"Available","weights":"Available","data":"Available","license":"Not reported","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2508.19236","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/shihao1895/MemoryVLA","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."},{"url":"https://github.com/shihao1895/MemoryVLA/tree/openvla-codebase#training","label":"Official training initialization and model zoo","fields":["policy.baseModel","release.weights","release.data"],"note":"LIBERO uses OpenVLA pretrained model; Bridge/Fractal use CogACT pretrained model; actual model and processed data links are public."}],"caveats":["Original7B Prismatic implementation differs from later QwenOFT reproductions used by EventVLA/RMBench."],"verified":"2026-10-17"},{"id":"gaussian-world-model","name":"Gaussian World Model (GWM)","title":"GWM: Towards Scalable Gaussian World Models for Robotic Manipulation","date":"2025-08-25","year":2025,"collections":["policy"],"categories":["geometry-models","rgbd-3d"],"resourceType":"model","summary":"Action-conditioned Gaussian scene dynamics supply geometric features and a neural simulator for robot policy learning.","institution":"Tsinghua University; BIGAI; Nanyang Technological University","venue":"ICCV 2025","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2508.17600","project":"https://gaussian-world-model.github.io/","code":"https://github.com/Gaussian-World-Model/gaussianwm"},"release":{"code":"Partial — work-in-progress training implementation","weights":"Not verified","data":"Not verified","license":"MIT","notes":"Official repository identifies itself as work in progress and releases training code. Upstream Splatt3r weights are not a released GWM checkpoint."},"evidence":[{"url":"https://arxiv.org/abs/2508.17600","label":"Original manuscript and first submission history","fields":["title","date","summary","model","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://github.com/Gaussian-World-Model/gaussianwm","label":"Official implementation, README and file/license inspection on 19 October 2026","fields":["links","release"],"note":"Official repository identifies itself as work in progress and releases training code. Upstream Splatt3r weights are not a released GWM checkpoint. MIT."}],"caveats":["World-model outputs are 3D scene states; downstream policy training and action selection are separate components."],"verified":"2026-10-19","model":{"role":"Geometric representation and learned simulator for IL / model-based RL","family":"Predictive world model","baseModel":"Splatt3R/MASt3R reconstruction, 3D Gaussian VAE and latent diffusion transformer; downstream IL / model-based RL.","modelSize":"Not reported in the inspected primary-source sections","inputs":"RGB observations and actions; geometry is reconstructed in the Gaussian representation.","outputs":"Future 3D Gaussians/rendered views; a separately trained downstream agent produces robot actions.","trainingMethod":"Self-supervised world-model learning followed by imitation or model-based reinforcement learning.","trainingData":"Robot interaction sequences across MetaWorld, RoboCasa and real Franka pick-place settings.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"31 robotic tasks across three domains and physical pick-place variations.","inference":"Not reported in the inspected primary-source sections"}},{"id":"icpi","name":"In-Context Policy Improvement (ICPI)","title":"In-Context Iterative Policy Improvement for Dynamic Manipulation","date":"2025-08-20","year":2025,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A frozen language model predicts adjustments to a low-dimensional dynamic-manipulation controller from preceding trials and example policy improvements.","institution":"University of Michigan; Mitsubishi Electric Research Laboratories","venue":"CoRL 2025","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Robot arm with task-specific tool"],"links":{"paper":"https://arxiv.org/abs/2508.15021"},"policy":{"family":"Robot agent","baseModel":"GPT-4o in the default method; GPT-3.5-turbo and GPT-4o-mini are separate ablations.","modelSize":"Not reported","inputs":"Current parameterized controller, task / goal state or measured outcome error, and example input–output policy-improvement pairs serialized as text.","outputs":"Changes to a three-dimensional controller parameter vector for slide, rope-swing or ball-roll motions.","trainingData":"At most 300 policy-improvement examples in the reported low-data study; simulation examples use brute-force-derived good parameters and the physical setup uses measured trials.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen LLM weights; iterative in-context parameter adjustment modifies an explicit controller rather than retraining a neural motor policy.","evaluation":"Slide, goal-conditioned slide, rope swing and goal-conditioned rope swing in simulation, plus real goal-conditioned ball rolling."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The original paper is public; code, model and dataset release scopes are verified separately where established. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms."},"evidence":[{"url":"https://arxiv.org/abs/2508.15021","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."}],"caveats":["The language model receives compact numerical state / error, not an unprocessed tactile stream or a universal visual action interface.","Low-dimensional controller updates are not arbitrary joint-action prediction."],"verified":"2026-10-17"},{"id":"geovla","name":"GeoVLA","title":"GeoVLA: Empowering 3D Representations in Vision-Language-Action Models","date":"2025-08-12","year":2025,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Adds an independent point embedding network and a geometry-conditioned action expert to a pretrained VLM.","institution":"Tianjin University / Dexmal","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2508.09071","project":"https://linsun449.github.io/GeoVLA/","code":"https://github.com/linsun449/geovla.code"},"policy":{"family":"VLA","baseModel":"Prismatic-7B initialized from OpenVLA + point embedding network + 3D-enhanced diffusion action expert","modelSize":"7B VLM; full model with PEN/3DAE not reported","inputs":"RGB, measured depth-to-point-cloud geometry, instruction and state","outputs":"Continuous end-effector/gripper action sequences","trainingData":"LIBERO and ManiSkill2 demos; eight real task families with geometric variations","trainingTime":"Real task training about 8 h; full simulation wall-clock not reported","trainingHardware":"8 NVIDIA A100 GPUs, total batch 256","controlRate":"Not reported","trainingMethod":"Multimodal action diffusion; VLM initialized from OXE-pretrained OpenVLA","evaluation":"LIBERO, ManiSkill2 and real height/size/viewpoint tests including insertion and stacking"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2508.09071","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/linsun449/geovla.code","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://linsun449.github.io/GeoVLA/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/linsun449/geovla.code/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["Paper explicitly measures depth; do not conflate with RGB-only inferred geometry.","Current repository may differ from the 2025 paper; exact checkpoint release not verified."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"omnivtla","name":"OmniVTLA","title":"OmniVTLA: Vision-Tactile-Language-Action Models with Semantic-Aligned Tactile Sensing","date":"2025-08-12","year":2025,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Uses semantically aligned tactile encoders to integrate optical and force-array sensors with a π0-based policy.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2508.08706"},"policy":{"family":"VLA","baseModel":"π0; SigLIP; semantically aligned tactile ViT","modelSize":"Paper reports Gemma-2.6B backbone; total policy not reported","inputs":"Third-person/wrist RGB, tactile images or array maps, state and language","outputs":"50-step chunks; 10D gripper or 25D four-finger action representation","trainingData":"ObjTac: 135k tri-modal samples, 56 objects/10 categories; downstream 40 demos/object plus insertion demos","trainingTime":"VLA fine-tuning 30k steps; wall-clock not reported","trainingHardware":"NVIDIA A100 80GB; count not reported","controlRate":"Not reported","trainingMethod":"Semantic tactile pretraining then flow-matching action fine-tuning","evaluation":"Gripper/dexterous pick-and-place and shape-block insertion with unseen objects"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."},"evidence":[{"url":"https://arxiv.org/abs/2508.08706","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://arxiv.org/html/2508.08706","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Tactile semantic pretraining samples are not action demonstrations.","Recorded sensor modalities differ by embodiment."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."}},"relatedIds":[]},{"id":"iclhf","name":"ICLHF","title":"In-situ Value-aligned Human-Robot Interactions with Physical Constraints","date":"2025-08-11","year":2025,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Uses textual human feedback and physical-constraint feedback to revise a household task plan while a graph-based optimizer grounds spatial relations.","institution":"Hohai University; Beijing Institute for General Artificial Intelligence","venue":"Preprint","modalities":["Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2508.07606","project":"https://iclhf.github.io","code":"https://github.com/ICLHF/ICLHF"},"policy":{"family":"Robot agent","baseModel":"GPT-3.5 Turbo planner with Planning over Graphs (POG) geometric optimization.","modelSize":"Not reported","inputs":"Instruction, human preference corrections, symbolic object / relation state and feedback from physical-constraint checking.","outputs":"Revised plans over predefined primitives such as grouping, placement and slicing, with optimized object arrangement geometry.","trainingData":"In-situ human feedback provides prompt context; zero-shot / one-shot preference settings are evaluated.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen LLM weights; upper-loop human preference alignment and lower-loop physical-feasibility checking iteratively refine plans.","evaluation":"Four categories of household chores in OmniGibson; table-arrangement geometry comparisons and real robot table-tidying experiments."},"release":{"code":"Available","weights":"Not applicable","data":"Not verified","license":"MIT original code, verified from LICENSE; OmniGibson assets and model dependencies retain separate terms.","notes":"Actual simulator/environment wrappers, LLM and physical-object-graph planners, worker/scene configurations and main entry point are public. Local model endpoints and OmniGibson assets must be installed separately; no method-specific checkpoint or independent full interaction-corpus release was verified."},"evidence":[{"url":"https://arxiv.org/abs/2508.07606","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://github.com/ICLHF/ICLHF","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["The planning state includes explicit object poses and properties; the paper is not an end-to-end raw-image motor policy.","The primary contribution is preference / constraint alignment rather than a new learned contact controller."],"verified":"2026-10-19"},{"id":"molmoact","name":"MolmoAct","title":"MolmoAct: Action Reasoning Models that can Reason in Space","date":"2025-08-11","year":2025,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Autoregressively predicts spatial perception tokens, visual reasoning traces and executable robot actions from images and instructions.","institution":"Allen Institute for AI / University of Washington","venue":"Preprint","modalities":["RGB","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2508.07917","project":"https://allenai.org/blog/molmoact","code":"https://github.com/allenai/molmoact","data":"https://huggingface.co/datasets/allenai/MolmoAct-Dataset","weights":"https://huggingface.co/allenai/MolmoAct-7B-D-0812"},"policy":{"family":"VLA","baseModel":"7B-O: OLMo2-7B/OpenCLIP; 7B-D: Qwen2.5-7B/SigLIP2; action-reasoning token decoder","modelSize":"7B language backbones; visual encoder-inclusive aggregate not separately reported","inputs":"RGB images and language instruction or visual trajectory prompt; predicts depth tokens from RGB","outputs":"Spatial/depth tokens, 2D trajectory reasoning and quantized low-level robot actions; post-training chunk8","trainingData":"26.3M pretraining samples from robot and multimodal data; MolmoAct robot dataset and RT-1 mid-training","trainingTime":"7B-D pretraining100k selected-step checkpoint≈ 38 h/9,728 GPU-h (fullrun150k); mid-training50k≈ 18 h/2,304 GPU-h","trainingHardware":"Pretraining256 × H100; mid-training128 × H100; real/sim adaptation32/64 × H100","controlRate":"Autoregressive decoding; universal measured feedback rate not reported","trainingMethod":"Spatial action-reasoning pretraining → action mid-training → LoRA task/embodiment adaptation","evaluation":"SimplerEnv, LIBERO; real single-/dual-arm Franka, in-domain/OOD and visual-line steering"},"release":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 code; foundation component licenses separate","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2508.07917","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/allenai/molmoact","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."}],"caveats":["Paper introduction mentions9,216 GPU-hours while detailed recipe/Table 4 reports9,728 for 7B-D; detailed setting retained explicitly.","Depth tokens are predicted from RGB, not measured sensor input."],"verified":"2026-10-17"},{"id":"ricl","name":"RICL","title":"RICL: Adding In-Context Adaptability to Pre-Trained Vision-Language-Action Models","date":"2025-08-04","year":2025,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Post-trains a pretrained VLA to use retrieved demonstration groups, enabling new-task adaptation through context without mandatory task-specific weight updates.","institution":"Not reported","venue":"CoRL 2025","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2508.02062","project":"https://ricl-vla.github.io","code":"https://github.com/ricl-vla/ricl_openpi","weights":"https://huggingface.co/ricl-vla/pi0_fast_droid_ricl_checkpoint","data":"https://huggingface.co/datasets/ricl-vla/collected_demos_training","retrieval-data":"https://huggingface.co/datasets/ricl-vla/collected_demos"},"policy":{"family":"VLA","baseModel":"π0-FAST-DROID with a PaliGemma vision-language backbone; language-model weights are fine-tuned while the image encoder remains frozen.","modelSize":"PaliGemma includes a 3B Gemma language backbone; the paper does not equate this component count with the complete policy size.","inputs":"RGB views, seven joint angles plus gripper position, language and four retrieved groups of demonstration context.","outputs":"15-step chunks, each containing seven joint velocities and one gripper position; the default deployment executes three predicted actions before replanning.","trainingData":"20 demonstrations per priming task, collected with a Franka DROID setup; new-task retrieval uses the available target demonstrations.","trainingTime":"Three epochs of priming, batch size 16; the optional target-task fine-tuning comparison uses 1,000 additional steps.","trainingHardware":"Two NVIDIA A100 GPUs for the reported priming setup.","controlRate":"Demonstrations are collected at 15 Hz; action chunks contain 15 steps, with three normally executed per query.","trainingMethod":"Offline retrieved-context post-training teaches the VLA to use examples. At deployment, the main RICL condition retrieves examples with fixed weights; optional fine-tuning is a separate variant.","evaluation":"Real Franka DROID tasks with unseen objects and novel motions, compared with direct π0 fine-tuning and retrieval variants."},"release":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 code; checkpoint and collected-demo cards specify MIT","notes":"Official openpi implementation and separately hosted trained checkpoint, priming data and retrieval demonstrations are public."},"evidence":[{"url":"https://arxiv.org/abs/2508.02062","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/ricl-vla/ricl_openpi","label":"Official implementation, README and file/license inspection on 19 October 2026","fields":["links","release"],"note":"Official openpi implementation and separately hosted trained checkpoint, priming data and retrieval demonstrations are public. Apache-2.0 code; checkpoint and collected-demo cards specify MIT."},{"url":"https://huggingface.co/ricl-vla/pi0_fast_droid_ricl_checkpoint","label":"Official released artifact inventory and model/dataset card","fields":["release.weights","links"],"note":"File inventory and artifact-specific terms checked independently; no upstream artifact is counted as this work’s trained release."},{"url":"https://huggingface.co/datasets/ricl-vla/collected_demos_training","label":"Official released artifact inventory and model/dataset card","fields":["release.data","links"],"note":"File inventory and artifact-specific terms checked independently; no upstream artifact is counted as this work’s trained release."},{"url":"https://huggingface.co/datasets/ricl-vla/collected_demos","label":"Official released artifact inventory and model/dataset card","fields":["release.data","links"],"note":"File inventory and artifact-specific terms checked independently; no upstream artifact is counted as this work’s trained release."}],"caveats":["Post-training the VLA to acquire in-context ability is different from target-task fine-tuning at evaluation.","The retrieved target demonstration set and additional fine-tuning data must be retained when comparing variants."],"verified":"2026-10-19"},{"id":"tacman-turbo","name":"TacMan-Turbo","title":"TacMan-Turbo: Proactive Tactile Control for Robust and Efficient Articulated Object Manipulation","date":"2025-08-04","year":2025,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"An analytical tactile controller predicts local handle motion and proactively adjusts gripper velocity for articulated-object manipulation.","institution":"Peking University / Tsinghua University / University of Chinese Academy of Sciences / Shenyang Institute of Automation, CAS / LeapZenith AI Research","venue":"IEEE T-ASE 2026","modalities":["Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2508.02204","project":"https://tacman-aom.github.io/tacman-turbo/","code":"https://github.com/YuyangLee/Tac-Man-Simulation"},"policy":{"family":"Model-based planning","baseModel":"Analytical contact-point registration, local handle-motion prediction and constrained offset-velocity computation; no learned action backbone.","modelSize":"Not applicable to analytical controller.","inputs":"Sequential bilateral GelSight-type contact-point geometry, robot/gripper pose and a rough task-direction base velocity. No external RGB-D perception is required by this controller.","outputs":"Six-DoF gripper velocity corrections combined with a base velocity to maintain stable contact.","trainingMethod":"Training-free analytical controller; contact calibration and threshold selection are separate from learned policy training.","trainingData":"Not applicable: no policy demonstrations or RL training. Simulation tests use 200 objects: 50 prismatic, 50 revolute and 100 Bézier-trajectory mechanisms.","trainingTime":"Not applicable to analytical controller.","trainingHardware":"Not applicable to policy training. Runtime uses Ryzen 9 5950X and RTX 3090.","controlRate":"Real tactile sensing 120 Hz, offset-velocity computation 50 Hz and Kinova low-level loop 1000 Hz; Isaac Sim simulation 60 Hz, simulator version Not reported.","evaluation":"Franka in Isaac Sim; Kinova Gen3 / Robotiq 2F-85 with two GelSight-type sensors in real tests on drawer, microwave and vise, plus charging dock, power switch, coffee maker and sewing machine. Stable grasp is manually initialized."},"release":{"code":"Partial","weights":"Not applicable","data":"Not verified","license":"Not reported in linked repository; no root LICENSE file.","notes":"Official Turbo page links the earlier Tac-Man simulation repository. Inspected implementation and README identify Tac-Man; a Turbo-specific predictive/velocity-controller implementation was not established."},"evidence":[{"url":"https://arxiv.org/abs/2508.02204","label":"Original paper and reviewed full text","fields":["title","date","institution","summary","policy","caveats"],"note":"Full primary text, including available supplementary implementation details; first public date retained separately from reviewed revision."},{"url":"https://tacman-aom.github.io/tacman-turbo/","label":"Official author project","fields":["links","release"],"note":"Current project links and release claims checked independently of paper prose."},{"url":"https://github.com/YuyangLee/Tac-Man-Simulation","label":"Official implementation and repository files","fields":["release","links"],"note":"Training/deployment implementation, README and actual LICENSE inspected where present."}],"caveats":["Assumes stable initial grasp, non-slipping contact and a rough interaction direction; it does not solve perception, approach or grasp acquisition.","First public date is 2025-08-04; current journal revision and author page identify T-ASE 2026.","The linked code release must not be presented as verified complete TacMan-Turbo code."],"verified":"2026-10-17"},{"id":"h-rdt","name":"H-RDT","title":"H-RDT: Human Manipulation Enhanced Bimanual Robotic Manipulation","date":"2025-07-31","year":2025,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Pretrains a flow action transformer on human hand trajectories and transfers through robot-specific action adapters.","institution":"Tsinghua University / Embodied Foundation","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2507.23523","project":"https://embodiedfoundation.github.io/hrdt","code":"https://github.com/HongzheBi/H_RDT","weights":"https://huggingface.co/embodiedfoundation/H-RDT"},"policy":{"family":"Diffusion","baseModel":"2B diffusion transformer; DINOv2 and SigLIP image encoders; T5-XXL language encoder","modelSize":"2B transformer scale; pretrained encoders are identified separately","inputs":"One to three RGB views, proprioceptive state and task language","outputs":"48-D hand pose for human pretraining; robot-specific action decoder, e.g. 14-D bimanual actions","trainingData":"Large egocentric human manipulation corpus with 3D hand pose; robot-specific simulation/real demonstrations","trainingTime":"Reported task recipes use 10k–100k steps; overall pretraining wall-clock not reported","trainingHardware":"Task-specific settings include 4/8 × H100 and 8 × H20; scopes differ by evaluation","controlRate":"Not reported","trainingMethod":"Human-hand flow-matching pretraining → modular state/action adapter fine-tuning on target robots","evaluation":"Simulation and real ALOHA/Franka, bimanual single-/multi-task, few-shot and robustness settings"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Not reported","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2507.23523","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/HongzheBi/H_RDT","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."}],"caveats":["The stated 2B transformer size should not be read as an encoder-inclusive total; paper Appendix B gives modular component configuration."],"verified":"2026-10-17"},{"id":"vla-touch","name":"VLA-Touch","title":"VLA-Touch: Enhancing Vision-Language-Action Models with Dual-Level Tactile Feedback","date":"2025-07-23","year":2025,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"Adds semantic tactile feedback for planning and a tactile diffusion controller for refining base-VLA actions.","institution":"National University of Singapore","venue":"IEEE RA-L 2026","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2507.17294","code":"https://github.com/clear-nus/vla-touch"},"policy":{"family":"Hybrid","baseModel":"RDT-1B + Octopi tactile-language model + DINOv2 interpolant controller","modelSize":"1B base action policy component; total system not reported","inputs":"RGB, instruction, robot state, GelSight images and marker-derived tactile summaries","outputs":"Refined action chunks from a frozen task-adapted VLA","trainingData":"Cup: 100 episodes/8 min; Wipe: 100/15 min; Peel: 180/30 min","trainingTime":"Base embodiment alignment 100k steps ≈80 h; task adaptation 20k steps ≈16 h; controller additional","trainingHardware":"One RTX 4090","controlRate":"Interpolant correction/actions at most 8 Hz; recordings 10 Hz; tactile sensor ≥25 Hz","trainingMethod":"Base vision-only adaptation, then tactile interpolant-controller training; Octopi informs task planner","evaluation":"Cup pick/place, wiping and mango peeling"},"release":{"code":"Partial","weights":"Not verified","data":"Not verified","license":"MIT for repository","notes":"Controller/Octopi integration code public; README says complete modified inference release is forthcoming."},"evidence":[{"url":"https://arxiv.org/abs/2507.17294","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/clear-nus/vla-touch","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2507.17294","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["“Without fine-tuning the base VLA” refers to tactile integration; the base model is first embodiment/task adapted.","Eight-Hz interpolant does not consume every tactile frame."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Partial","weights":"Not verified","data":"Not verified","license":"MIT for repository","notes":"Controller/Octopi integration code public; README says complete modified inference release is forthcoming."}},"relatedIds":[]},{"id":"egovla","name":"EgoVLA","title":"EgoVLA: Learning Vision-Language-Action Models from Egocentric Human Videos","date":"2025-07-16","year":2025,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Learns a unified human/robot hand-action space from egocentric video, then adapts to dexterous humanoid manipulation.","institution":"MIT / NVIDIA / UC San Diego","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Bimanual","Dexterous hand","Humanoid"],"links":{"paper":"https://arxiv.org/abs/2507.12440","project":"https://rchalyang.github.io/EgoVLA","code":"https://github.com/RchalYang/EgoVLA_Release","data":"https://huggingface.co/datasets/quincyu/EgoVLA-Humanoid-Sim","weights":"https://huggingface.co/rchal97/egovla"},"policy":{"family":"VLA","baseModel":"NVILA-2B VLM with action queries and human/robot hand-action heads","modelSize":"2B VLM backbone; complete action-head aggregate not reported","inputs":"Current/history egocentric RGB, language and human/robot proprioception","outputs":"One-second30-step hand trajectories; robot IK plus direct hand actuation, 36D in humanoid simulation","trainingData":"Approximately 500k image-action pairs from TACO, HOI4D, HoloAssist and human video; target robot demonstrations for adaptation","trainingTime":"20 human-video pretraining epochs, 115 robot post-training epochs; wall-clock not reported","trainingHardware":"32 × NVIDIA A100","controlRate":"30 Hz action/control timeline; full policy chunk-replanning latency not reported","trainingMethod":"Human unified-hand-action pretraining → robot adaptation and hand-action retargeting","evaluation":"12-task Ego Humanoid Manipulation Benchmark in Isaac Lab and real robot transfer/generalization"},"release":{"code":"Available","weights":"Available","data":"Available","license":"MIT original code, verified from actual LICENSE; derived VILA portions retain Apache-2.0 notices and model/data terms remain separate.","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2507.12440","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/RchalYang/EgoVLA_Release","label":"Official policy implementation","fields":["links"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://raw.githubusercontent.com/RchalYang/EgoVLA_Release/main/LICENSE","label":"Repository license file","fields":["links"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://github.com/RchalYang/EgoVLA_Release","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/datasets/quincyu/EgoVLA-Humanoid-Sim","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/rchal97/egovla","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["Paper reports0% success without robot-specific adaptation; human pretraining alone is not a zero-shot executable robot policy."],"verified":"2026-10-19"},{"id":"octopi-15","name":"Octopi-1.5","title":"Demonstrating the Octopi-1.5 Visual-Tactile-Language Model","date":"2025-07-14","year":2025,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Adds multi-part tactile reasoning, scene vision and a retrieval module to a tactile-language interface demonstrated through a handheld sensor device.","institution":"National University of Singapore","venue":"RSS 2025 Demo Track","modalities":["Tactile","RGB","Language"],"environments":["Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2507.09985","code":"https://github.com/clear-nus/octopi-1.5","data":"https://drive.google.com/file/d/1ckSzE4DxSiq4U34gWBIUGreImryLw94c/view?usp=drive_link","weights":"https://drive.google.com/file/d/1awBk019Ad3xdcQ0thsFCCdCnoEYREcf9/view?usp=sharing"},"model":{"role":"Reusable representation or perception model; downstream controllers are separate systems","family":"Tactile-language model","baseModel":"Qwen2-VL 7B plus a tactile CLIP module, projection layers and retrieval-augmented generation","modelSize":"Qwen2-VL 7B base; the paper comparison table labels the complete Octopi-1.5 variant “8B”, without a separate parameter audit","inputs":"Tactile clips from multiple object parts, optional RGB observation, text prompt and retrieved tactile-object examples","outputs":"Physical-property scores, comparative judgments, object guesses and language reasoning","trainingMethod":"Tactile CLIP pretraining with contrastive/regression objectives; frozen encoder with VPT/LoRA language adaptation; retrieval at inference","trainingData":"Video counts: PhysiCLeAR Plain 2,689; PhysiCLeAR Dotted 1,939; Hardness 1,860; ObjectFolder Real 3,550. Object/sample counts differ across these sources.","trainingTime":"Tactile CLIP module: 30 epochs; other stages and wall-clock duration not reported","trainingHardware":"Not reported","inference":"Not reported","evaluation":"RSS demonstration: guessing game, tactile feature guessing and property comparison; held-out object tests with/without retrieval."},"release":{"code":"Available","weights":"Available — official download link","data":"Available — official resource link","license":"No standalone top-level repository LICENSE identified; Qwen2-VL and component terms apply separately","notes":"Code, pretrained encoders and downstream task data are separate artifacts; no policy checkpoint is implied by an encoder release."},"evidence":[{"url":"https://arxiv.org/abs/2507.09985","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Method, experiments and implementation sections; specifications retain component and evaluation scope."},{"url":"https://github.com/clear-nus/octopi-1.5","label":"Official implementation and current release instructions","fields":["release","links"],"note":"README and repository files inspected on 2026-10-17."}],"caveats":["The live interface is handheld and explicitly does not require a robot.","Retrieval adds remembered tactile–object examples; it is not gradient-based robot policy adaptation.","The prose calls the base Qwen2-VL 7B, while Table III labels Octopi-1.5 “8B”; these source descriptions have different scope."],"relatedIds":["octopi","tvl"],"verified":"2026-10-17"},{"id":"tactile-vla","name":"Tactile-VLA","title":"Tactile-VLA: Unlocking Vision-Language-Action Model's Physical Knowledge for Tactile Generalization","date":"2025-07-12","year":2025,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Grounds language-conditioned motion and force targets in tactile feedback, with a hybrid position–force controller.","institution":"Tsinghua University","venue":"Preprint","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2507.09160"},"policy":{"family":"VLA","baseModel":"π0-style vision-language/action model","modelSize":"Not reported","inputs":"RGB, language, tactile arrays and proprioception","outputs":"Target position and force for hybrid control; optional reasoning tokens","trainingData":"USB: 100 soft +100 hard demos; charger: 100; grasping: 50 per each of six training objects; wiping: 100 success+100 failure","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Joint tactile-aware action learning and language/force grounding","evaluation":"Charger/USB insertion/extraction, tabletop grasping and whiteboard-to-blackboard wiping generalization"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."},"evidence":[{"url":"https://arxiv.org/abs/2507.09160","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://arxiv.org/html/2507.09160","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Zero-shot generalization is evaluated on specified tasks, modifiers and objects, not arbitrary skills.","No verified public action-policy implementation/download in inspected primary sources."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."}},"relatedIds":[]},{"id":"behavioral-exploration","name":"Behavioral Exploration","title":"Behavioral Exploration: Learning to Explore via In-Context Adaptation","date":"2025-07-11","year":2025,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A long-context generative policy learns to select new expert behaviors conditioned on what it has already tried.","institution":"UC Berkeley","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2507.09041"},"policy":{"family":"Transformer policy","baseModel":"Long-context generative behavior model with an exploration-measure conditioning signal.","modelSize":"Not reported","inputs":"Current robot observations, previous interaction history and a measure of exploration relative to that history.","outputs":"Expert-like continuous actions that adapt exploratory behavior across interactions.","trainingData":"Offline expert demonstrations; real arm experiments use Bridge-style observations and demonstrations.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Real WidowX end-effector delta control at 5 Hz; 128×128 top-down RGB observations.","trainingMethod":"Supervised expert action prediction conditioned on history and behavioral novelty, enabling fixed-weight in-context exploration.","evaluation":"D4RL Kitchen manipulation, other RL domains and three real WidowX interaction tasks."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/2507.09041","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."}],"caveats":["Included for both simulated Kitchen and real arm manipulation; AntMaze results are not manipulation evidence."],"verified":"2026-10-17"},{"id":"spherical-diffusion-policy","name":"Spherical Diffusion Policy (SDP)","title":"SE(3)-Equivariant Diffusion Policy in Spherical Fourier Space","date":"2025-07-02","year":2025,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"End-to-end SE(3)-equivariant diffusion control embeds scene, actions and denoising in spherical Fourier space.","institution":"Northeastern University; Amazon; collaborating authors","venue":"ICML 2025","modalities":["RGB","Depth","Point cloud","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2507.01723","code":"https://github.com/amazon-science/Spherical_Diffusion_Policy"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT for repository code; pretrained models, data and figure rights remain separate","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2507.01723","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://github.com/amazon-science/Spherical_Diffusion_Policy","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/amazon-science/Spherical_Diffusion_Policy/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/amazon-science/Spherical_Diffusion_Policy/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"256 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["Rigid-transform equivariance can be disturbed by gravity, visibility changes and task constraints."],"verified":"2026-10-17","policy":{"family":"Diffusion","baseModel":"Equiformer-style scene encoding, spherical FiLM and spherical denoising temporal U-Net.","modelSize":"Not reported in the inspected primary-source sections","inputs":"RGB-D-derived point clouds and end-effector/gripper state histories.","outputs":"SE(3) end-effector and gripper action trajectories.","trainingMethod":"Equivariant behavior cloning with diffusion denoising.","trainingData":"MimicGen and physical manipulation demonstrations; per-task budgets in manuscript.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Twenty simulation tasks and five physical tasks with single-/dual-arm robots.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"4d-vla","name":"4D-VLA","title":"4D-VLA: Spatiotemporal Vision-Language-Action Pretraining with Cross-Scene Calibration","date":"2025-06-27","year":2025,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Depth-aware temporal features and memory-bank sampling align robot/scenario coordinates in VLA pretraining.","institution":"Fudan University; Huawei Noah’s Ark Lab","venue":"NeurIPS 2025","modalities":["RGB","Depth","Point cloud","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2506.22242","code":"https://github.com/LogosRoboticsGroup/4D-VLA"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT for repository code; pretrained models, data and figure rights remain separate","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2506.22242","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://github.com/LogosRoboticsGroup/4D-VLA","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/LogosRoboticsGroup/4D-VLA/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/LogosRoboticsGroup/4D-VLA/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"548 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["Depth availability and estimation differ across source datasets; retain the selected experiment’s sensing/calibration assumptions."],"verified":"2026-10-17","policy":{"family":"VLA","baseModel":"InternVL-4B VLM with sequential RGB-D spatial coordinates, historical memory-bank sampling and cross-scene calibration.","modelSize":"4B-class InternVL backbone; added component total not reported","inputs":"Sequential RGB-D observations, camera geometry, language and robot state.","outputs":"Robot manipulation actions.","trainingMethod":"Cross-scene calibrated action pretraining and target task finetuning.","trainingData":"DROID pretraining; LIBERO and real Franka task finetuning; dataset totals are not filtered training counts.","trainingTime":"Pretraining: one epoch / ~20K steps / 96 hours; LIBERO finetuning 20 epochs.","trainingHardware":"8 NVIDIA A6000 GPUs for pretraining","evaluation":"Simulation, physical manipulation and MV-Bench novel-view generalization.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"convitac","name":"ConViTac","title":"ConViTac: Aligning Visual-Tactile Fusion with Contrastive Representations","date":"2025-06-25","year":2025,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Conditions visual–tactile feature fusion with contrastively aligned embeddings for tactile property and grasp-outcome prediction.","institution":"Department of Engineering, King’s College London","venue":"IROS 2025 (official repository)","modalities":["Tactile","RGB"],"environments":["Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2506.20757","project":"https://georgewuzy.github.io/ConViTac-website/","code":"https://github.com/GeorgeWuzy/ConViTac"},"model":{"role":"Reusable representation or perception model; downstream controllers are separate systems","family":"Multimodal representation","baseModel":"Contrastive visual/tactile encoder; Contrastive Embedding Conditioning with cross-modal attention","modelSize":"Not reported","inputs":"Paired visual and tactile image/sequence observations","outputs":"Fused representations; material/property labels and grasp-success predictions","trainingMethod":"SimCLR-style contrastive pretraining followed by conditioned supervised fusion","trainingData":"Touch and Go, ObjectFolder Real and grasp-outcome datasets under task-specific splits","trainingTime":"Typically converges within 30 epochs per task/dataset; wall-clock duration not reported","trainingHardware":"1 NVIDIA RTX 3080 Ti; batch size 16","inference":"Not reported","evaluation":"Material classification and grasp-success prediction; ablations isolate Contrastive Embedding Conditioning."},"release":{"code":"Partial — model.py released; remaining code announced","weights":"Not verified","data":"Not verified","license":"MIT repository LICENSE","notes":"Code, pretrained encoders and downstream task data are separate artifacts; no policy checkpoint is implied by an encoder release."},"evidence":[{"url":"https://arxiv.org/abs/2506.20757","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Method, experiments and implementation sections; specifications retain component and evaluation scope."},{"url":"https://georgewuzy.github.io/ConViTac-website/","label":"Official project","fields":["links","release"],"note":"Author project; author-reported release scope is distinguished from an executed reproduction."},{"url":"https://github.com/GeorgeWuzy/ConViTac","label":"Official implementation and current release instructions","fields":["release","links"],"note":"README and repository files inspected on 2026-10-17."},{"url":"https://github.com/GeorgeWuzy/ConViTac/blob/main/LICENSE","label":"Repository license file","fields":["release.license"],"note":"Applies to repository scope; model/data/base-model terms can differ."}],"caveats":["The official README explicitly says only model.py is released and further details are pending.","Classification and success prediction should not be counted as an executable manipulation policy."],"relatedIds":["mvitac"],"verified":"2026-10-17"},{"id":"demodiffusion","name":"DemoDiffusion","title":"DemoDiffusion: One-Shot Human Imitation using pre-trained Diffusion Policy","date":"2025-06-25","year":2025,"collections":["policy"],"categories":["agent-rsi-icl","generalist-foundation"],"resourceType":"policy","summary":"A retargeted human hand trajectory guides a pretrained diffusion/flow policy to imitate one demonstration without task-specific training.","institution":"Carnegie Mellon University","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2506.20668","project":"https://demodiffusion.github.io/","code":"https://github.com/demodiffusion/demodiffusion"},"policy":{"family":"Hybrid","baseModel":"Pretrained π0-DROID action policy with kinematic human-hand retargeting and trajectory-guided denoising.","modelSize":"Not reported","inputs":"One human demonstration, extracted 3D hand trajectory, current robot RGB observations and state.","outputs":"Robot action chunks constrained by the retargeted motion prior; joint-space controls are converted from end-effector trajectories.","trainingData":"Uses the pretrained policy’s existing data; no new task-specific paired human-robot training corpus required.","trainingTime":"No task-specific policy training; upstream policy pretraining cost is not a DemoDiffusion cost.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Inference-time guidance / modification of a kinematic motion prior using a pretrained generalist policy.","evaluation":"Eight real manipulation tasks; appendix additionally evaluates dexterous retargeting."},"release":{"code":"Available","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"Deployment code uses the public upstream pi0_droid checkpoint; no separately trained DemoDiffusion policy weights are required."},"evidence":[{"url":"https://arxiv.org/abs/2506.20668","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://demodiffusion.github.io/","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/demodiffusion/demodiffusion","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."}],"caveats":["Human-trajectory guidance is not a measured tactile stream.","Upstream π0 training should not be counted as task-specific adaptation."],"verified":"2026-10-17"},{"id":"unitac-nv","name":"UniTac-NV","title":"UniTac-NV: A Unified Tactile Representation For Non-Vision-Based Tactile Sensors","date":"2025-06-24","year":2025,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Aligns magnetic and non-optical tactile arrays using matched contacts and an autoencoder, enabling sensor transfer and contact-geometry estimation.","institution":"Manipulation and Touch Lab, Imperial College London","venue":"Preprint","modalities":["Tactile"],"environments":["Real world"],"embodiments":["UR5e robot arm"],"links":{"paper":"https://arxiv.org/abs/2506.19699"},"model":{"role":"Reusable representation or perception model; downstream controllers are separate systems","family":"Tactile encoder","baseModel":"Sensor-specific MLP encoders with a shared latent space and decoder","modelSize":"Not reported","inputs":"Xela uSkin uSPa 46 or Contactile PapillArray non-optical tactile readings","outputs":"Sensor-agnostic latent vector, self/cross-sensor reconstruction, optional 11-dimensional contact-geometry prediction","trainingMethod":"Joint autoencoder training on matched interactions; separately trained contact-geometry head","trainingData":"UR5e force-controlled pressing over six seen shape/material objects (three shapes × PLA/TPU), with an additional irregular unseen object; angle-stratified 90/10 seen split","trainingTime":"Autoencoder 1,000 epochs; contact-geometry head 80 epochs; wall-clock not reported","trainingHardware":"Not reported","inference":"Not reported","evaluation":"Self and cross-sensor reconstruction and contact-geometry regression on held-out approach angles/unseen object; no autonomous manipulation evaluation."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Paper CC BY 4.0; code/model/data release license not verified","notes":"Code, pretrained encoders and downstream task data are separate artifacts; no policy checkpoint is implied by an encoder release."},"evidence":[{"url":"https://arxiv.org/abs/2506.19699","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Method, experiments and implementation sections; specifications retain component and evaluation scope."}],"caveats":["The Xela/Contactile signals are skin tactile arrays, not an optical image or wrist F/T sensor.","Matched pressing actions collect data; the learned model estimates contact geometry and does not output control actions."],"relatedIds":["htt","tactile-jepa"],"verified":"2026-10-17"},{"id":"robotwin2","name":"RoboTwin 2.0","title":"RoboTwin 2.0: A Scalable Data Generator and Benchmark with Strong Domain Randomization for Robust Bimanual Robotic Manipulation","date":"2025-06-22","year":2025,"collections":["benchmark"],"categories":["general-manipulation","robustness-evaluation"],"resourceType":"benchmark","summary":"Automated dual-arm task/data generation with embodiment-adaptive experts, 50 tasks and strong visual/physical randomization.","institution":"Shanghai Jiao Tong University / collaborating institutions","venue":"ICML 2026","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2506.18088","project":"https://robotwin-platform.github.io/","code":"https://github.com/robotwin-Platform/RoboTwin","data":"https://huggingface.co/datasets/TianxingChen/RoboTwin2.0"},"benchmark":{"simulator":"SAPIEN","simulatorVersion":"Current official scripts/requirements.txt pins sapien==3.0.0b1 and mplib==0.2.1.","taskCount":"50 dual-arm tasks / 5 robot embodiments","tasks":["adjust_bottle","beat_block_hammer","blocks_ranking_rgb","blocks_ranking_size","click_alarmclock","click_bell","dump_bin_bigbin","grab_roller","handover_block","handover_mic","hanging_mug","lift_pot","move_can_pot","move_pillbottle_pad","move_playingcard_away","move_stapler_pad","open_laptop","open_microwave","pick_diverse_bottles","pick_dual_bottles","place_a2b_left","place_a2b_right","place_bread_basket","place_bread_skillet","place_burger_fries","place_can_basket","place_cans_plasticbox","place_container_plate","place_dual_shoes","place_empty_cup","place_fan","place_mouse_pad","place_object_basket","place_object_scale","place_object_stand","place_phone_stand","place_shoe","press_stapler","put_bottles_dustbin","put_object_cabinet","rotate_qrcode","scan_object","shake_bottle","shake_bottle_horizontally","stack_blocks_three","stack_blocks_two","stack_bowls_three","stack_bowls_two","stamp_seal","turn_switch"],"taskListScope":"Complete 50 task_instruction JSON identifiers in current official repository.","capabilities":["Bimanual cooperation","Tool use","Stacking","Articulated objects","Language grounding","Domain-randomized robustness"],"metrics":"Per-task success and task mean; easy clean-to-clean and hard clean-to-random transfer scores.","robots":"Five bimanual embodiments; released configs include ALOHA AgileX and ARX X5, with embodiment-specific expert planning.","sensors":"Head/wrist visual cameras, optional depth/point clouds and robot state; original suite does not provide tactile sensing.","dataCollection":"MLLM-generated expert task programs with simulation feedback; annotated skills and cuRobo motion planning execute the trajectories. Program generation and trajectory planning are separate stages.","data":"Current repository advertises 100,000+ pre-collected trajectories; automatic task/embodiment-specific expert generation also available. Standard co-training leaderboard uses 50 demo_clean trajectories ×50 tasks.","protocol":"demo_clean and demo_randomized conditions; historical paper uses single-task baselines while current leaderboard distinguishes co-train vs single-task; held-out instructions and assets vary by recipe.","baselines":"ACT, Diffusion Policy, DP3, RDT and π0 in paper; current XPolicyLab integrations expand model set."},"release":{"code":"Available","weights":"Partial","data":"Available","license":"MIT code; assets and individual dataset/model terms separate","notes":"Task experts, training/evaluation adapters, object assets and generated-data resources public."},"evidence":[{"url":"https://arxiv.org/abs/2506.18088","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://robotwin-platform.github.io/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/robotwin-Platform/RoboTwin","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://github.com/robotwin-Platform/RoboTwin/blob/main/scripts/requirements.txt","label":"Official implementation / documentation","fields":["benchmark.simulatorVersion"],"note":"Current released simulator dependency pin."},{"url":"https://arxiv.org/abs/2506.18088","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original task-program generation and embodiment-adaptation sections; MLLM generates programs while cuRobo plans robot motion."}],"caveats":["Clean-trained evaluation on randomized scenes differs from training on randomized demonstrations.","Five supported embodiments are not necessarily a fully crossed 50-task experiment in every result table."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Partial","data":"Available","license":"MIT code; assets and individual dataset/model terms separate","notes":"Task experts, training/evaluation adapters, object assets and generated-data resources public."}},"relatedIds":[]},{"id":"robomonkey","name":"RoboMonkey","title":"RoboMonkey: Scaling Test-Time Sampling and Verification for Vision-Language-Action Models","date":"2025-06-21","year":2025,"collections":["policy"],"categories":["agent-rsi-icl","generalist-foundation"],"resourceType":"policy","summary":"A trained action verifier selects among VLA samples and perturbed proposals to improve manipulation at inference time.","institution":"Stanford University / UC Berkeley","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2506.17811","project":"https://robomonkey-vla.github.io","code":"https://github.com/robomonkey-vla/RoboMonkey","weights":"https://huggingface.co/robomonkey-vla/monkey-verifier-7b"},"policy":{"family":"Hybrid","baseModel":"Existing VLA policy, including OpenVLA experiments, plus a trained 7B vision-language action verifier.","modelSize":"7B verifier component, in addition to the chosen base VLA; variants are evaluated separately.","inputs":"Robot RGB observations, task and candidate robot actions.","outputs":"Verified action selected from sampled / perturbed candidate controls.","trainingData":"20 million synthetic action comparisons generated from Bridge V2 data.","trainingTime":"Not reported","trainingHardware":"Eight NVIDIA H100 GPUs, batch size 256, LoRA rank 512 for verifier training.","controlRate":"Approximately 1.5 Hz overall system on one H100; 16 candidate actions sampled/verified in approximately 650 ms under the reported configuration.","trainingMethod":"Synthetic action-preference verifier training; test-time Gaussian perturbation, majority-vote proposals and learned verification.","evaluation":"Simulation and real manipulation, evaluating robustness / generalization of existing VLAs."},"release":{"code":"Available","weights":"Available","data":"Partial","license":"MIT for repository code; external model, dataset and dependency terms remain separate.","notes":"Official verifier checkpoint and code; upstream VLA checkpoints retain their own terms."},"evidence":[{"url":"https://arxiv.org/abs/2506.17811","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://robomonkey-vla.github.io","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/robomonkey-vla/RoboMonkey","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."},{"url":"https://github.com/robomonkey-vla/RoboMonkey/blob/HEAD/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Actual license file inspected; code license does not automatically license data or weights."}],"caveats":["The verifier is trained; test-time scaling does not mean the whole method requires no training.","Verifier and base-policy parameters are distinct components."],"verified":"2026-10-17"},{"id":"vitacformer","name":"ViTacFormer","title":"ViTacFormer: Learning Cross-Modal Representation for Visuo-Tactile Dexterous Manipulation","date":"2025-06-19","year":2025,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"Learns a cross-attention visuotactile representation with autoregressive tactile prediction for long-horizon dexterous imitation.","institution":"Peking University / Sharpa","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2506.15953","project":"https://roboverseorg.github.io/ViTacFormerPage/","code":"https://github.com/RoboVerseOrg/ViTacFormer"},"policy":{"family":"ACT","baseModel":"ACT-style latent action policy; cross-attention encoder and tactile prediction head","modelSize":"Not reported","inputs":"High-resolution RGB, tactile signals and robot state","outputs":"Dexterous action chunks and auxiliary future tactile signals","trainingData":"50 expert demonstrations per task","trainingTime":"100 epochs; short tasks ≈12 h; long tasks up to 2 days","trainingHardware":"2 NVIDIA H20 GPUs","controlRate":"Not reported","trainingMethod":"Easy-to-hard curriculum and joint action/tactile prediction imitation","evaluation":"Real multi-finger skills including long-horizon Make Hamburger, up to 11 sequential stages"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Apache-2.0 for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Author project links implementation; weights/data download scope not verified."},"evidence":[{"url":"https://arxiv.org/abs/2506.15953","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/RoboVerseOrg/ViTacFormer","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2506.15953","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."},{"url":"https://github.com/RoboVerseOrg/ViTacFormer/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["Long-horizon duration and stage counts are task-specific results, not a general reliability guarantee."],"verified":"2026-10-19","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Apache-2.0 for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Author project links implementation; weights/data download scope not verified."}},"relatedIds":[]},{"id":"dsrl","name":"DSRL","title":"Steering Your Diffusion Policy with Latent Space Reinforcement Learning","date":"2025-06-18","year":2025,"collections":["policy"],"categories":["visual-precision-rl"],"resourceType":"policy","summary":"Learns to steer the latent noise of a frozen diffusion/flow policy, allowing RL adaptation without updating the large base policy.","institution":"UC Berkeley / University of Washington","venue":"CoRL 2025","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2506.15799","project":"https://diffusion-steering.github.io/","code":"https://github.com/nakamotoo/dsrl_pi0"},"policy":{"family":"RL","baseModel":"Small SAC-style latent policy over a frozen task diffusion policy or π0","modelSize":"Base-model dependent; π0 branch inherits ~3.3B plus a small steering network","inputs":"Base-policy visual/proprioceptive observations; instruction for VLA settings","outputs":"Latent noise mapped by the frozen generator into continuous action chunks","trainingData":"Original robot cube task base uses 10 demos; π0 experiments use pretrained generalist data and online rollouts","trainingTime":"Not reported","trainingHardware":"Original online/offline benchmark experiments use NVIDIA A5000; π0 remote inference hardware not reported","controlRate":"Not reported","trainingMethod":"Off-policy RL in latent-noise action space; base diffusion model frozen","evaluation":"Online/offline simulated manipulation, real Franka pick-and-place, π0 in LIBERO/ALOHA/real Franka"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2506.15799","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/nakamotoo/dsrl_pi0","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://diffusion-steering.github.io/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/nakamotoo/dsrl_pi0/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["A frozen policy still incurs its inference cost.","The linked repository is the author π0 implementation; smaller diffusion-policy experiments use a separate author codebase."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"robust-instant-policy","name":"Robust Instant Policy (RIP)","title":"Robust Instant Policy: Leveraging Student's t-Regression Model for Robust In-context Imitation Learning of Robot Manipulation","date":"2025-06-18","year":2025,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Student-t trajectory regression suppresses outliers from several frozen-LLM in-context action predictions.","institution":"AIST","venue":"Preprint","modalities":["RGB","Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2506.15157","project":"https://sites.google.com/view/robustinstantpolicy"},"policy":{"family":"Robot agent","baseModel":"GPT-4o instant-policy predictor, DINO RGB-D keypoint representation and Student-t regression.","modelSize":"Not reported","inputs":"Demonstration 3D keypoints/actions plus current RGB-D-derived visual keypoints.","outputs":"Five candidate trajectories are robustly aggregated into an executable gripper trajectory.","trainingData":"Ten prompt demonstrations per task in the principal comparison; no LLM weight update.","trainingTime":"No task-specific LLM fine-tuning.","trainingHardware":"Not reported","controlRate":"Demonstrations recorded at 55 Hz; test trajectories played at 5.5 Hz for stability.","trainingMethod":"Training-free in-context trajectory generation followed by robust Student-t aggregation; ν=1.5 and five LLM queries.","evaluation":"Simulated and real manipulation, including banana picking and object interaction tasks."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"Official project and original experiments verified; hosted GPT-4o is a dependency rather than released RIP-trained weights."},"evidence":[{"url":"https://arxiv.org/abs/2506.15157","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://sites.google.com/view/robustinstantpolicy","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."}],"caveats":["Distinct from Instant Policy: this work uses an LLM and robust trajectory aggregation.","Playback rate is not hosted LLM inference speed."],"verified":"2026-10-17"},{"id":"sparsh-x","name":"Sparsh-X","title":"Tactile Beyond Pixels: Multisensory Touch Representations for Robot Manipulation","date":"2025-06-17","year":2025,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Fuses optical touch, contact audio, inertial and pressure measurements into self-supervised tactile embeddings for physical interaction.","institution":"FAIR at Meta; University of Washington; Carnegie Mellon University","venue":"CoRL 2025","modalities":["Tactile","Audio","IMU","Pressure"],"environments":["Simulation","Real world"],"embodiments":["Allegro hand","Handheld picker"],"links":{"paper":"https://arxiv.org/abs/2506.14754","project":"https://akashsharma02.github.io/sparsh-x-ssl/","code":"https://github.com/facebookresearch/sparsh-multisensory-touch","data":"https://huggingface.co/datasets/facebook/sparsh-x-dataset","weights":"https://huggingface.co/facebook/sparsh-x-all"},"model":{"role":"Reusable representation or perception model; downstream controllers are separate systems","family":"Tactile encoder","baseModel":"Multisensory transformer with unimodal layers, bottleneck-attention fusion, and self-distillation","modelSize":"Not reported","inputs":"Digit 360 tactile images, contact microphones, accelerometer and internal pressure; missing-modality variants are available","outputs":"Unified multisensory touch embeddings; downstream physical-property heads and robot-policy features","trainingMethod":"Student–teacher self-distillation with multisensory masking/dropping; separate imitation and sim-policy tactile adaptation","trainingData":"Approximately 1M samples from 18.6 hours of recordings: 14.1 hours with the handheld picker and 4.5 hours with the Allegro hand.","trainingTime":"200 pretraining epochs; wall-clock duration not reported","trainingHardware":"16 NVIDIA A100 GPUs; batch size 128","inference":"Encoder: 50 Hz on RTX 4090 after input preprocessing. Four-finger end-to-end deployment: about 20 Hz; log-mel construction is a bottleneck.","evaluation":"Object/action/surface, material/quantity and force probes; plug insertion imitation and tactile adaptation of sim-trained in-hand rotation."},"release":{"code":"Available","weights":"Available — official download link","data":"Available — official resource link","license":"CC BY-NC 4.0 repository LICENSE.md; model/data cards and upstream controller terms apply separately.","notes":"Code, pretrained encoders and downstream task data are separate artifacts; no policy checkpoint is implied by an encoder release."},"evidence":[{"url":"https://arxiv.org/abs/2506.14754","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Method, experiments and implementation sections; specifications retain component and evaluation scope."},{"url":"https://akashsharma02.github.io/sparsh-x-ssl/","label":"Official project","fields":["links","release"],"note":"Author project; author-reported release scope is distinguished from an executed reproduction."},{"url":"https://github.com/facebookresearch/sparsh-multisensory-touch","label":"Official implementation and current release instructions","fields":["release","links"],"note":"README and repository files inspected on 2026-10-17."},{"url":"https://github.com/facebookresearch/sparsh-multisensory-touch/blob/main/LICENSE.md","label":"Repository license file","fields":["release.license"],"note":"Applies to repository scope; model/data/base-model terms can differ."}],"caveats":["Audio, IMU and internal pressure are Digit 360 tactile channels; they do not indicate a scene-depth sensor.","Encoder throughput differs from four-fingertip end-to-end controller frequency.","Sim-trained policies use privileged state; tactile adaptation learns to replace or refine deployment state estimates."],"relatedIds":["sparsh","sparsh-skin"],"verified":"2026-10-17"},{"id":"vital-local","name":"ViTaL — local skills","title":"Touch begins where vision ends: Generalizable policies for contact-rich manipulation","date":"2025-06-16","year":2025,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"Separates global reaching from reusable local visuotactile interaction and improves local precision with residual RL.","institution":"NYU Shanghai / NYU","venue":"CoRL 2025 Workshop","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2506.13762","project":"https://vitalprecise.github.io/","code":"https://github.com/Exiam6/ViTaL"},"policy":{"family":"Hybrid","baseModel":"Semantic-augmented BC transformer; residual RL; optional Molmo reaching","modelSize":"BC policy7.6M; additional residual-RL component2.46M (paper component counts)","inputs":"Local egocentric RGB, AnySkin tactile signals and state; optional global RGBD for reaching","outputs":"Local contact skill actions; global reaching pose","trainingData":"Not reported","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Visual-background augmentation, imitation pretraining and online residual RL","evaluation":"Precise contact-rich tasks in novel scenes and distractor settings"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Official local skill training/deployment implementation available."},"evidence":[{"url":"https://arxiv.org/abs/2506.13762","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/Exiam6/ViTaL","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2506.13762","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."},{"url":"https://github.com/Exiam6/ViTaL/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["Optional reaching depth is separate from the local policy inputs.","Distinct from VITaL Pretraining and another later ViTaL steering paper.","Paper full-model total9.06M is inconsistent with stated7.6M+2.46M components; catalogue does not infer a corrected total."],"verified":"2026-10-19","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Official local skill training/deployment implementation available."}},"relatedIds":[]},{"id":"bridgevla","name":"BridgeVLA","title":"BridgeVLA: Input-Output Alignment for Efficient 3D Manipulation Learning with Vision-Language Models","date":"2025-06-09","year":2025,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Aligns perception and action in a 2 D heatmap space by projecting 3 D observations into virtual views.","institution":"CASIA / ByteDance Seed","venue":"NeurIPS 2025","modalities":["RGB","Depth","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2506.07961","project":"https://bridgevla.github.io/home_page.html","code":"https://github.com/BridgeVLA/BridgeVLA/tree/bridgevla","data":"https://huggingface.co/datasets/LPY/BridgeVLA","weights":"https://huggingface.co/datasets/LPY/BridgeVLA/tree/main/checkpoints/bridgevla"},"policy":{"family":"VLA","baseModel":"PaliGemma VLM with heatmap output head and view projection","modelSize":"PaliGemma backbone (3B class); total policy parameters not separately reported","inputs":"Calibrated RGB-D point clouds rendered as three orthographic views, and language instruction","outputs":"2D action heatmaps fused into 3D end-effector keyposes; rotation/gripper predictions","trainingData":"120k RoboPoint object-grounding images, then RLBench/GemBench and real robot demos","trainingTime":"Grounding ~2 h; RLBench/COLOSSEUM ~20 h; real fine-tuning ~1.5 h","trainingHardware":"Pretrain 8 A100; RLBench/COLOSSEUM 48 H100; real fine-tune 8 A100","controlRate":"Not reported","trainingMethod":"2D heatmap grounding pretraining followed by action fine-tuning","evaluation":"RLBench, COLOSSEUM, GemBench and real precision/generalization tasks"},"release":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2506.07961","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/BridgeVLA/BridgeVLA/tree/bridgevla","label":"Official repository","fields":["release","policy.baseModel"],"note":"Original BridgeVLA implementation is preserved on the bridgevla branch; main now hosts the distinct BridgeVLA++ release."},{"url":"https://bridgevla.github.io/home_page.html","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/BridgeVLA/BridgeVLA/bridgevla/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."},{"url":"https://arxiv.org/html/2506.07961","label":"Independent review:Section3.3 input/output definition","fields":["policy.inputs","modalities"],"note":"The policy omits robot-state inputs; rotation/gripper/collision heads use global/local visual tokens. Low-level motion planning is separate."}],"caveats":["A virtual-view keypose method; motion execution adds planner/controller dependencies.","Pretraining here means additional grounding training, not the cost of training PaliGemma.","Repository main now contains BridgeVLA++; use the linked bridgevla branch and original checkpoint subdirectory for this paper."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"silvr","name":"SILVR","title":"Self-Improving Loops for Visual Robotic Planning","date":"2025-06-07","year":2025,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Self-collected robot trajectories iteratively improve an in-domain visual planner while a frozen internet video prior supplies broader motion guidance.","institution":"Brown University / Harvard University","venue":"Preprint","modalities":["RGB","Language","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2506.06658","project":"https://diffusion-supervision.github.io/silvr/","code":"https://github.com/brown-palm/silvr"},"policy":{"family":"Model-based planning","baseModel":"Self-improving visual planning with inverse dynamics. Small in-domain video diffusion model, frozen AnimateDiff/SparseCtrl prior and inverse dynamics; optional distilled Diffusion Policy.","modelSize":"In-domain model: 179.91M (MetaWorld) or 156.58M (real). Internet video model: 2.005B; these are separate components.","inputs":"Current RGB, text task prompt and visual plans. Inverse dynamics translates generated futures into control; long-horizon real extension uses Gemini 2.5 skill switching.","outputs":"Eight future-frame plans and eight-action controls; optional direct diffusion-policy action output.","trainingData":"MetaWorld in-domain initialization uses eight seen tasks with 25 demonstrations/task; each improvement iteration collects 30 new trajectories. Real experiments cover cup pushing and drawer opening.","trainingTime":"In-domain initialization: 70,000 MetaWorld or 88,000 Panda steps. Per iteration: 10,000 MetaWorld, 8,000 cup-pushing or 10,000 drawer steps; wall time not reported.","trainingHardware":"A single NVIDIA A6000 or RTX 3090.","controlRate":"Evaluates open-loop, per-action replanning and intermediate control schedules; a single universal Hz is not reported.","trainingMethod":"Inverse probabilistic adaptation combines internet and in-domain visual models; robot interaction updates in-domain video and inverse-dynamics weights.","evaluation":"MetaWorld novel tasks and physical Panda cup pushing/drawer opening, with optional language-ordered multi-cup extension."},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"No repository-wide license found in inspected tree","notes":"Author-linked repository and actual README/tree inspected; weights/data are tracked separately. No installation or reproduction attempted."},"evidence":[{"url":"https://arxiv.org/abs/2506.06658","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://diffusion-supervision.github.io/silvr/","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://github.com/brown-palm/silvr","label":"Official code","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."}],"caveats":["This method updates model weights from robot interaction; it is not frozen-model ICL.","Dataset demonstrations, model-training steps and robot interaction iterations are different quantities."],"verified":"2026-10-17"},{"id":"3dflowaction","name":"3DFlowAction","title":"3DFlowAction: Learning Cross-Embodiment Manipulation from 3D Flow World Model","date":"2025-06-06","year":2025,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Language-conditioned 3D object-flow generation guides a constrained optimizer, with rendered-plan checking by GPT-4o.","institution":"South China University of Technology; Tencent Robotics X; HKUST; Pazhou Laboratory","venue":"arXiv preprint","modalities":["RGB","Depth","Point cloud","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2506.06199","code":"https://github.com/Hoyyyaard/3DFlowAction"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2506.06199","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://github.com/Hoyyyaard/3DFlowAction","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/Hoyyyaard/3DFlowAction/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"1530 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["The planning checker uses GPT-4o; an optimization policy is distinct from a directly trained action decoder."],"verified":"2026-10-17","policy":{"family":"Planning / hybrid","baseModel":"Stable-Diffusion/AnimateDiff flow world model, flow-guided renderer and SciPy action optimizer.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Calibrated RGB-D scene, language and selected object geometry.","outputs":"Constrained SE(3) end-effector pose chunks.","trainingMethod":"Video-flow pretraining and human-video adaptation; robot actions recovered through optimization.","trainingData":"ManiFlow-110K; 30 human demonstrations per target task.","trainingTime":"Approximately two days of pretraining","trainingHardware":"Manuscript reports an 8 × 8 V100 setup for pretraining","evaluation":"Four real tasks across XTrainer and Franka, with unseen object/background tests.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"freetacman","name":"FreeTacMan","title":"FreeTacMan: Robot-free Visuo-Tactile Data Collection System for Contact-rich Manipulation","date":"2025-06-02","year":2025,"collections":["policy"],"categories":["tactile-il","tactile-foundation"],"resourceType":"policy","summary":"A robot-free wearable gripper collects high-fidelity touch demonstrations used for tactile-pretrained manipulation policies.","institution":"Shanghai Innovation Institute / University of Hong Kong / Shanghai University / Fudan University / Shanghai Jiao Tong University","venue":"ICRA 2026","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2506.01941","project":"https://opendrivelab.com/FreeTacMan","code":"https://github.com/OpenDriveLab/FreeTacMan","data":"https://huggingface.co/datasets/OpenDriveLab/FreeTacMan"},"policy":{"family":"ACT","baseModel":"ACT with pretrained visual/tactile representations.","modelSize":"Not reported","inputs":"RGB, tactile images and robot proprioception.","outputs":"Robot end-effector/gripper action sequences.","trainingData":"Dataset>10k trajectories, 50 collection tasks,>3 M paired visual/tactile images; separate five-task policy subsets and 50/100 demo ablation settings.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"RGB/touch 30 Hz; measured inference latency<20 ms, not fixed control Hz.","trainingMethod":"Self-collected tactile representation pretraining followed by ACT imitation.","evaluation":"PIPER arm:fragile cup, calligraphy, texture sorting, stamp press, USB plug; dataset coverage is broader than evaluated policy tasks."},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"Apache-2.0","notes":"Author-linked repository and actual LICENSE inspected; code, checkpoints anddatasets have independent availability."},"evidence":[{"url":"https://arxiv.org/abs/2506.01941","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://opendrivelab.com/FreeTacMan","label":"Official author project","fields":["links","release"],"note":"Live artifact status reviewed separately from paper claims."},{"url":"https://github.com/OpenDriveLab/FreeTacMan","label":"Official released implementation and LICENSE","fields":["links","release"],"note":"Repository metadata,README and actualLICENSE inspected; availability scoped to the listed component."},{"url":"https://huggingface.co/datasets/OpenDriveLab/FreeTacMan","label":"Author-linked released dataset","fields":["links","release"],"note":"Public repository file inventory inspected; component/data scope follows model/dataset card."}],"caveats":["Dataset collection task count is not the number of evaluated robot policy tasks."],"verified":"2026-10-17"},{"id":"smolvla","name":"SmolVLA","title":"SmolVLA: A Vision-Language-Action Model for Affordable and Efficient Robotics","date":"2025-06-02","year":2025,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Compact VLA combines a small frozen VLM with flow-matching actions and optional asynchronous execution.","institution":"Hugging Face","venue":"Technical report","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2506.01844","project":"https://huggingface.co/blog/smolvla","code":"https://github.com/huggingface/lerobot","weights":"https://huggingface.co/lerobot/smolvla_base"},"policy":{"family":"VLA","baseModel":"Truncated SmolVLM2-500M-Video-Instruct + ~100M action expert","modelSize":"Paper main model450M total /~100M action expert; release named500M","inputs":"RGB views, language and robot state","outputs":"50-step continuous joint/gripper action chunks","trainingData":"481 community datasets: 22.9k episodes /10.6M frames; downstream SO100/SO101 demos","trainingTime":"200k pretraining steps; paper project-wide expenditure~30k GPU-hours includes experiments","trainingHardware":"Paper supports single-GPU training; exact pretraining GPU model not reported","controlRate":"50-action chunks; asynchronous queue improves runtime, not a universal specified Hz","trainingMethod":"Flow-matching behavior cloning with frozen VLM; synchronous or asynchronous action-queue inference","evaluation":"LIBERO, MetaWorld and real SO100/SO101 pick-place, stacking and sorting"},"release":{"code":"Available","weights":"Available","data":"Partial","license":"Apache-2.0 (LeRobot code and smolvla_base weights); community dataset licenses vary","notes":"Community dataset IDs listed in paper appendix; complete unified archive not verified."},"evidence":[{"url":"https://arxiv.org/abs/2506.01844","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/huggingface/lerobot","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://huggingface.co/blog/smolvla","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/huggingface/lerobot/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["Table caption mistakenly calls10.6M frames episodes; the dataset table itself gives22.9k episodes.","Project-wide GPU-hours are not a single model training bill."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Partial","license":"Apache-2.0 (LeRobot code and smolvla_base weights); community dataset licenses vary","notes":"Community dataset IDs listed in paper appendix; complete unified archive not verified."}},"relatedIds":[]},{"id":"og-vla","name":"OG-VLA","title":"OG-VLA: Orthographic Image Generation for 3D-Aware Vision-Language Action Model","date":"2025-06-01","year":2025,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Canonical orthographic rendering aligns multi-view RGB-D inputs with image-generated keyframe actions.","institution":"University of Southern California; NVIDIA","venue":"IROS 2026","modalities":["RGB","Depth","Point cloud","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2506.01196","project":"https://og-vla.github.io/"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2506.01196","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://og-vla.github.io/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."}],"caveats":["Canonical views require calibrated 3D geometry; image diffusion produces keyframe actions rather than high-rate continuous control."],"verified":"2026-10-17","policy":{"family":"VLA","baseModel":"X-VILA / Vicuna-7B v1.5, ImageBind and Stable Diffusion1.5 action-image generator.","modelSize":"Vicuna-7B language backbone; full multimodal total not reported","inputs":"Calibrated multi-view RGB-D and natural language.","outputs":"Images encoding next gripper position/orientation, decoded into quasi-static keyframe actions.","trainingMethod":"LoRA VLM tuning with action-image generation and SE(3) augmentation.","trainingData":"ARNOLD: about 500 demos/task for eight tasks; Colosseum: 100 demos/task; real adaptation uses 3–5 demos.","trainingTime":"ARNOLD 30K steps ~1.5 days; 100K ~5 days; distinct from 250K-step Colosseum run","trainingHardware":"8 A100 GPUs for main training; inference on one A100","evaluation":"ARNOLD, Colosseum and physical manipulation generalization.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"dexumi","name":"DexUMI","title":"DexUMI: Using Human Hand as the Universal Manipulation Interface for Dexterous Manipulation","date":"2025-05-28","year":2025,"collections":["policy"],"categories":["tactile-il","tactile-foundation"],"resourceType":"policy","summary":"Human-hand exoskeleton demonstrations and visual robot-hand replacement train dexterous manipulation policies.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Tactile"],"environments":["Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2505.21864","project":"https://dex-umi.github.io/","code":"https://github.com/real-stanford/DexUMI"},"policy":{"family":"Diffusion","baseModel":"Fine-tuned DINO-V2 image encoder and diffusion action policy.","modelSize":"Not reported","inputs":"Wrist RGB and tactile readings.","outputs":"16-step end-effector relative pose/hand action chunks;6 D Inspire or 12 DX Hand hand commands.","trainingData":"Human exoskeleton interaction demonstrations across two hand platforms; exact retained counts Not reported here.","trainingTime":"400 epochs; wall-clock Not reported.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Robot-hand image inpainting/embodiment alignment and 400 epoch diffusion imitation.","evaluation":"Real Inspire Hand and XHand dexterous tasks."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT","notes":"Author-linked repository and actual LICENSE inspected; code, checkpoints anddatasets have independent availability."},"evidence":[{"url":"https://arxiv.org/abs/2505.21864","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://dex-umi.github.io/","label":"Official author project","fields":["links","release"],"note":"Live project artifact links inspected on2026-10-17."},{"url":"https://github.com/real-stanford/DexUMI","label":"Official released implementation and LICENSE","fields":["links","release"],"note":"Repository metadata,README and actualLICENSE inspected; availability scoped to the listed component."}],"caveats":["Collection hardware fits robot kinematics; cross-hand results do not mean one undifferentiated policy transfers without adaptation."],"verified":"2026-10-17"},{"id":"forcevla","name":"ForceVLA","title":"ForceVLA: Enhancing VLA Models with a Force-aware MoE for Contact-rich Manipulation","date":"2025-05-28","year":2025,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Integrates external six-axis force feedback into a VLA action decoder through a force-aware mixture of experts.","institution":"Shanghai Jiao Tong University","venue":"Preprint","modalities":["RGB","Force/Torque","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2505.22159","project":"https://sites.google.com/view/forcevla2025"},"policy":{"family":"VLA","baseModel":"π0 / PaliGemma-SigLIP with force-aware MoE","modelSize":"Not reported","inputs":"RGB, language, proprioception and external six-axis force/torque","outputs":"Conditional flow-matching action trajectories","trainingData":"ForceVLA-Data: synchronized observations for five contact-rich tasks","trainingTime":"Multi-task 30k steps ≈12 h; single-task 10k steps ≈9 h","trainingHardware":"Multi-task: 2 RTX 4090; single-task: 1 RTX 4090 (cluster nodes contain 8)","controlRate":"Not reported","trainingMethod":"Force-aware routed fusion and action fine-tuning","evaluation":"Five real contact-rich tasks including plug insertion"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."},"evidence":[{"url":"https://arxiv.org/abs/2505.22159","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://sites.google.com/view/forcevla2025","label":"Author project page","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2505.22159","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Uses external force/torque, not tactile images or skin arrays.","Node GPU count differs from GPUs actually used per training run."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."}},"relatedIds":[]},{"id":"vtv-llm","name":"VTV-LLM / VTV150K","title":"Universal Visuo-Tactile Video Understanding for Embodied Interaction","date":"2025-05-28","year":2025,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Connects tactile video to language through a sensor-diverse video encoder and staged instruction adaptation.","institution":"Tsinghua University; Sun Yat-sen University; Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ)","venue":"NeurIPS 2025 (official repository)","modalities":["Tactile","Language"],"environments":["Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2505.22566","code":"https://github.com/IvanXie416/VTV-LLM","data":"https://huggingface.co/datasets/Ivan416/VBTS_video","weights":"https://huggingface.co/Ivan416/VBTS_video_encoder"},"model":{"role":"Reusable representation or perception model; downstream controllers are separate systems","family":"Tactile-language model","baseModel":"VideoMAE-derived ViT-Base tactile video encoder with a Qwen2.5 language backbone","modelSize":"Qwen2.5 language variants: 3B, 7B and 14B; tactile video encoder is additional","inputs":"Optical tactile video or rendered Tac3 Dforce-field video plus text prompt","outputs":"Tactile-property assessment, comparisons and scenario answers in text","trainingMethod":"Tactile-video enhancement, VTV–text alignment and text-prompt fine-tuning","trainingData":"VTV150K: 150,000 frames from 100 objects and three sensors (GelSight Mini, DIGIT and Tac3D), labeled for hardness, protrusion, elasticity and friction.","trainingTime":"Not reported","trainingHardware":"4 NVIDIA RTX 6000 Ada GPUs","inference":"Not reported","evaluation":"Video tactile understanding and language reasoning across source sensors; no closed-loop robot action evaluation."},"release":{"code":"Available","weights":"Partial — tactile video encoder released; complete LLM weights not independently confirmed","data":"Available — official resource link","license":"No standalone repository LICENSE identified; base-model, dataset and checkpoint terms apply separately.","notes":"Code, pretrained encoders and downstream task data are separate artifacts; no policy checkpoint is implied by an encoder release."},"evidence":[{"url":"https://arxiv.org/abs/2505.22566","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Method, experiments and implementation sections; specifications retain component and evaluation scope."},{"url":"https://github.com/IvanXie416/VTV-LLM","label":"Official implementation and current release instructions","fields":["release","links"],"note":"README and repository files inspected on 2026-10-17."}],"caveats":["“Visuo-tactile video” includes images produced by touch sensors; it is not evidence of a measured scene-depth input.","150K counts video frames, rather than demonstrations or video clips."],"relatedIds":["tvl","octopi-15"],"verified":"2026-10-17"},{"id":"osvi-wm","name":"OSVI-WM","title":"OSVI-WM: One-Shot Visual Imitation for Unseen Tasks using World-Model-Guided Trajectory Generation","date":"2025-05-26","year":2025,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A demonstration-conditioned latent dynamics model predicts a short trajectory that decodes into robot waypoints.","institution":"New York University / Meta FAIR","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2505.20425","code":"https://github.com/raktimgg/osvi-wm"},"policy":{"family":"Model-based planning","baseModel":"One-shot visual imitation with latent world model. Shared ResNet-18 encoder, learned latent action/world models and MLP waypoint decoder.","modelSize":"Not reported","inputs":"Ten RGB demonstration frames and current agent RGB image. Real pick-and-place additionally uses a wrist-camera grasp-correction routine.","outputs":"Five attributed end-effector waypoints, with motion planning/control for execution; optional replanning.","trainingData":"Meta-World and Sawyer-to-Franka pick-and-place, 100 demonstrations per training task and embodiment. Each of three physical setups uses 30 trajectories/task, three training tasks and one held-out task.","trainingTime":"10 epochs in simulation and 100 in physical settings; wall-clock duration not reported.","trainingHardware":"Appendix specifies 4 NVIDIA A100 40 GB GPUs for training. Evaluation uses RTX A4000 in simulation and RTX 2080 Ti on robots.","controlRate":"Not reported","trainingMethod":"End-to-end waypoint soft-DTW supervision plus stop-gradient latent prediction loss; ImageNet-initialized ResNet-18 according to appendix.","evaluation":"Two simulation benchmarks and three Franka-based real setups: robot-to-robot pick-and-place, human-to-Franka pick-and-place and pushing; 25 physical rollouts/setup."},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"No repository-wide license found in inspected tree","notes":"Author-linked repository and actual README/tree inspected; weights/data are tracked separately. No installation or reproduction attempted."},"evidence":[{"url":"https://arxiv.org/abs/2505.20425","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://github.com/raktimgg/osvi-wm","label":"Official code","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."}],"caveats":["The introduction describes in-domain learning without pretraining, but the appendix explicitly initializes ResNet-18 from ImageNet.","Waypoints are the learned policy output; the deployed system also needs calibrated execution and grasp correction."],"verified":"2026-10-17"},{"id":"vitapes","name":"ViTaPEs","title":"ViTaPEs: Visuotactile Position Encodings for Cross-Modal Alignment in Multimodal Transformers","date":"2025-05-26","year":2025,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Two-stage positional encodings improve visual/tactile feature fusion and transfer, including offline prediction of robot grasp outcomes.","institution":"Technical University of Leoben; Graz University of Technology","venue":"Transactions on Machine Learning Research 2026","modalities":["RGB","Tactile"],"environments":["Real robot"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2505.20032","project":"https://sites.google.com/view/vitapes","publication":"https://openreview.net/forum?id=mxzzO66Zbu"},"release":{"code":"Not released","weights":"Not released","data":"Not verified","license":"Not verified","notes":"Official project explicitly says code/models will be available soon; Code/Models buttons point to blank placeholders. Existing evaluation datasets are separate from a new model release."},"model":{"role":"Vision-tactile representation and outcome prediction","family":"Multimodal representation","baseModel":"ViT fusion with modality-specific local position encodings, a shared nonlinear projection and global position encodings; supervised and MAE-pretrained variants.","modelSize":"Supervised main model: 12.7M; SSL Balanced model: 30.6M. Scaling study spans 6.7M to 90.7M parameters.","inputs":"RGB and optical tactile images; paired modalities need not share a calibrated pixel coordinate frame.","outputs":"Fused descriptors, recognition labels or a predicted grasp-success score; no action commands.","trainingMethod":"Supervised recognition or 75%-masked MAE pretraining with independently augmented modalities; linear probing and zero-shot transfer evaluated separately.","trainingData":"Touch-and-Go pretraining; ObjectFolder Real, YCB-Slide and Grasp datasets support task-specific/transfer evaluation. Grasp prediction uses mid-grasp observations; split units are stated in the appendix.","trainingTime":"ViTaPEs pretraining budget: 62.5 GPU-hours under the reported comparison setting.","trainingHardware":"NVIDIA A100 40GB for training-cost comparison.","inference":"10 ms per fused visual–tactile pair on RTX 4090; model throughput, not a robot-control frequency.","evaluation":"Material/object recognition, cross-dataset/cross-sensor transfer and grasp-success prediction under SSL, linear-probe and zero-shot protocols."},"evidence":[{"url":"https://arxiv.org/abs/2505.20032","label":"Original manuscript and first public submission history","fields":["title","date","institution","model","policy","modalities","environments","caveats"],"note":"Full author method, evaluation and appendix reviewed; downstream controller probes are distinguished from the reusable model."},{"url":"https://sites.google.com/view/vitapes","label":"Official author project and current artifact destinations","fields":["release","venue","imageCandidates"],"note":"A project button or advertised artifact does not establish a downloadable release; paper-version differences are retained."},{"url":"https://arxiv.org/html/2505.20032v3#A4","label":"Original training/inference cost appendix","fields":["model.trainingTime","model.trainingHardware","model.inference","model.modelSize"],"note":"Cost is measured for the reported model/protocol; inference throughput is not an action-control rate."}],"caveats":["Grasp-success classification is an offline perception task; it is not closed-loop grasp control.","Performance and cost numbers belong to specified model scales/protocols. Local/global position embeddings do not establish calibrated scene depth."],"verified":"2026-10-17"},{"id":"canonical-policy","name":"Canonical Policy","title":"Canonical Policy: Learning Canonical 3D Representation for SE(3)-Equivariant Policy","date":"2025-05-24","year":2025,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Point clouds, proprioception and actions are canonicalized to learn an SE(3)-equivariant generative manipulation policy.","institution":"Purdue University","venue":"IEEE Transactions on Robotics 2026","modalities":["RGB","Depth","Point cloud","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2505.18474","project":"https://zhangzhiyuanzhang.github.io/cp-website/","code":"https://github.com/ZhangZhiyuanZhang/canonical_policy"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT for repository code; pretrained models, data and figure rights remain separate","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2505.18474","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://zhangzhiyuanzhang.github.io/cp-website/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/ZhangZhiyuanZhang/canonical_policy","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/ZhangZhiyuanZhang/canonical_policy/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/ZhangZhiyuanZhang/canonical_policy/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"152 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["Single-view occlusion changes can remove shared geometry; equivariance does not recover unobserved surfaces."],"verified":"2026-10-17","policy":{"family":"Diffusion","baseModel":"Vector-neuron canonical frame estimator, point-cloud aggregation encoder and diffusion policy.","modelSize":"Not reported in the inspected primary-source sections","inputs":"RGB-D-derived point clouds and robot proprioception.","outputs":"End-effector pose and gripper action trajectories transformed back from the canonical frame.","trainingMethod":"Behavior cloning with learned geometric canonicalization.","trainingData":"200 demonstrations per MimicGen/Push-T task; 50 per bimanual task in the reported simulation protocol.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Twelve simulation tasks and four real task types under object, layout and viewpoint changes.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"manifeel","name":"ManiFeel","title":"ManiFeel: Benchmarking and Understanding Visuotactile Manipulation Policy Learning","date":"2025-05-24","year":2025,"collections":["benchmark"],"categories":["tactile-benchmarks","precision-assembly"],"resourceType":"benchmark","summary":"Modular comparison of sensing channels, tactile encoders and supervised policy heads across insertion, screwing and visually constrained exploration.","institution":"Purdue University","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2505.18472","project":"https://zhengtongxu.github.io/manifeel-website/","code":"https://github.com/purdue-mars/manifeel"},"benchmark":{"simulator":"Isaac Gym with TacSL","simulatorVersion":"Modified IsaacGymEnvs README specifies Isaac Gym Preview 4 and TacSL package; original paper exact binary build Not reported.","taskCount":"9 simulation task setups and 4 real-world setups in Jan 2026 v2; normal-light ball sorting also appears as a simulation control in dataset Table V.","tasks":["Peg Insertion","USB Insertion","Power Plug Insertion","Gear Assembly","Nut–bolt Assembly","Bulb Installation","Peg Reorientation (Occluded)","Object Search (Occluded)","Ball Sorting (Dim Light)"],"taskListScope":"Complete nine-setup simulation benchmark taxonomy in v2; physical setups: Gear Assembly, Bulb Installation, Ball Sorting Normal Light and Dim Light. Table V additionally records the normal-light simulation control.","capabilities":["Insertion / assembly","Threaded assembly","Occluded exploration","Texture-based sorting"],"metrics":"Task success rate, mean and standard deviation over seeds; FID/KID of simulated vs real tactile observations are sensor-domain diagnostics, not control success.","robots":"Franka Panda with parallel gripper in physical evaluation; simulated tactile-enabled manipulator.","sensors":"Three recorded RGB views and two simulated GelSight R1.5 fingertips; TacRGB and TacFF (normal/shear field) evaluated separately. Real uses front RealSense RGB and one right-finger GelSight R1.5; TacFF estimated from photometric stereo.","dataCollection":"3Dconnexion SpaceMouse teleoperation for simulated and physical demonstrations, with tactile feedback available to the operator.","data":"SpaceMouse demonstrations at 10 Hz. Table V: 20–100 demonstrations per simulation setup; 50–80 per real setup. Gear/sorting comparative studies use 80 demos; bulb uses 20.","protocol":"Compare Vision, Vision+TacRGB and Vision+TacFF with shared data; three policy runs/seeds and 15 consecutive test rollouts per trained policy. Downstream sensing/fusion and encoder choices are controlled.","baselines":"Diffusion Policy, Equivariant Diffusion Policy, Flow Matching; scratch ResNet18, UniT, T3 and AnyTouch encoders."},"release":{"code":"Available","weights":"Partial","data":"Partial","license":"MIT code; Isaac Gym and representation checkpoints retain their own terms","notes":"Public benchmark/modified IsaacGymEnvs repositories; separate simulator access and pretrained representation downloads required."},"evidence":[{"url":"https://arxiv.org/abs/2505.18472","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://zhengtongxu.github.io/manifeel-website/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/purdue-mars/manifeel","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://github.com/purdue-mars/manifeel-isaacgymenvs/blob/main/README.md","label":"Official source","fields":["benchmark.simulatorVersion"],"note":"Setup explicitly requests Isaac Gym Preview 4."},{"url":"https://arxiv.org/abs/2505.18472","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["v1 and v2 scopes differ; this entry uses the Jan 2026 v2 task definitions while retaining v1 date.","A simulated RGB image or force-field match does not establish identical contact dynamics on hardware.","Primary comparisons add one tactile representation at a time rather than joint RGB+TacRGB+TacFF fusion."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Partial","data":"Partial","license":"MIT code; Isaac Gym and representation checkpoints retain their own terms","notes":"Public benchmark/modified IsaacGymEnvs repositories; separate simulator access and pretrained representation downloads required."}},"relatedIds":[]},{"id":"adaptac","name":"AdapTac","title":"Adaptive Visuo-Tactile Fusion with Predictive Force Attention for Dexterous Manipulation","date":"2025-05-20","year":2025,"collections":["policy"],"categories":["tactile-il","rgbd-3d"],"resourceType":"policy","summary":"Observed and predicted tactile net forces guide attention between measured 3D vision and distributed hand touch.","institution":"Peking University / PKU-Agibot Lab / Ant Group","venue":"IROS 2025","modalities":["Depth","Point cloud","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2505.13982","project":"https://adaptac-dex.github.io/","code":"https://github.com/kingchou007/adaptac-dex","data":"https://drive.google.com/file/d/1F6mV1sTDIK1wW3m17eX2PGON7JJenPq3/view"},"policy":{"family":"Diffusion","baseModel":"RISE sparse 3D visual encoder, pretrained 3DTacDex tactile encoder and diffusion policy with force-guided cross-attention.","modelSize":"Not reported.","inputs":"L515 measured-depth XYZ point cloud and 120 PaXini tri-axis taxels across eight fingertip/fingerpad sensors. Taxel positions, orientations and forces form the tactile representation; kinematics transform forces into camera coordinates.","outputs":"25-dimensional action chunks: arm translation (3), rotation representation (6) and LEAP hand joints (16); future net-force prediction also conditions attention.","trainingMethod":"Diffusion imitation loss plus self-supervised future force prediction; predicted and observed net forces query visual/tactile features without manual contact labels.","trainingData":"Approximately 30 expert demonstrations for each of Open Box, Cup Reorientation and Sponge Flipping.","trainingTime":"Not reported.","trainingHardware":"Not reported.","controlRate":"5 Hz for both demonstration collection and inference.","evaluation":"Flexiv Rizon 4 with customized 16-DoF LEAP Hand and eight 3×5 PaXini arrays; ten trials/task, maximum 300 steps. Separate unseen-object tests use five objects with four poses each."},"release":{"code":"Available","weights":"Not verified","data":"Partial","license":"CC BY-NC-SA 4.0 (actual root LICENSE), despite README MIT badge/text.","notes":"Training/deployment source and public example_data.zip link verified. Linked 3DTacDex checkpoint is a tactile encoder, not verified AdapTac policy weights; full demonstration-corpus coverage not established."},"evidence":[{"url":"https://arxiv.org/abs/2505.13982","label":"Original paper and reviewed full text","fields":["title","date","institution","summary","policy","caveats"],"note":"Full primary text, including available supplementary implementation details; first public date retained separately from reviewed revision."},{"url":"https://adaptac-dex.github.io/","label":"Official author project","fields":["links","release"],"note":"Current project links and release claims checked independently of paper prose."},{"url":"https://github.com/kingchou007/adaptac-dex","label":"Official implementation and repository files","fields":["release","links"],"note":"Training/deployment implementation, README and actual LICENSE inspected where present."},{"url":"https://drive.google.com/file/d/1F6mV1sTDIK1wW3m17eX2PGON7JJenPq3/view","label":"Official public data link and visible file inventory","fields":["links.data","release.data"],"note":"Accessible public sample/example assets verified; completeness against full paper corpus not established."}],"caveats":["Predicted net force guides deployment attention; it is not merely an auxiliary training target.","Measured tactile forces are summed after pose transformation; this is not a separate wrist six-axis F/T stream.","Paper interface specifies XYZ point clouds; a camera capable of RGB-D does not establish RGB-color policy input."],"verified":"2026-10-17"},{"id":"ra-touch","name":"RA-Touch","title":"RA-Touch: Retrieval-Augmented Touch Understanding with Enriched Visual Data","date":"2025-05-20","year":2025,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"A tactile-guided retriever and texture-aware integrator enrich tactile descriptions using an external visual corpus with synthetic tactile captions.","institution":"Sungkyunkwan University","venue":"ACM Multimedia 2025","modalities":["RGB","Tactile","Language"],"environments":["Real robot"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2505.14270","project":"https://aim-skku.github.io/RA-Touch/","code":"https://github.com/AIM-SKKU/RA-Touch","data":"https://huggingface.co/datasets/yoorhim/ImageNet-T"},"release":{"code":"Available","weights":"Not released","data":"Available","license":"Not verified","notes":"Actual repository has retriever/integrator training and evaluation modules; actual ImageNet-T inventory contains annotations and embedding files. Models section remains TBU; upstream TVL weights are not RA-Touch checkpoints. No repository-wide license verified."},"model":{"role":"Retrieval-augmented tactile-language understanding","family":"Tactile-language model","baseModel":"TVL/OpenCLIP vision/tactile encoders and LLaMA-2-7B; learned tactile-guided retrieval and texture-aware cross-attention integration.","modelSize":"7B language-model backbone with ViT-Tiny/Small/Base tactile variants; complete-system parameter count not reported.","inputs":"RGB/tactile image pairs and a language prompt; ImageNet-T visual/text embeddings supply external retrieval context.","outputs":"Open-vocabulary tactile-property descriptions, rather than robot actions.","trainingMethod":"Train retriever first, then freeze it while training the integrator and LoRA-adapted LLaMA layers.","trainingData":"TVL: 43,548 aligned visuo-tactile pairs; external ImageNet-T has up to 150K recaptioned images. ImageNet-T adds visual-language context, not new measured tactile samples.","trainingTime":"Retriever: 60 epochs; integrator/LLM adaptation: one epoch. Elapsed time not reported.","trainingHardware":"Four NVIDIA RTX A6000 GPUs.","inference":"Not reported in the inspected primary manuscript","evaluation":"TVL tactile-description benchmark: 402 test pairs across SSVTP/HCT; description similarity rated with the stated language-model evaluation protocol."},"evidence":[{"url":"https://arxiv.org/abs/2505.14270","label":"Original manuscript and first public submission history","fields":["title","date","institution","model","policy","modalities","environments","caveats"],"note":"Full author method, evaluation and appendix reviewed; downstream controller probes are distinguished from the reusable model."},{"url":"https://aim-skku.github.io/RA-Touch/","label":"Official author project and current artifact destinations","fields":["release","venue","imageCandidates"],"note":"A project button or advertised artifact does not establish a downloadable release; paper-version differences are retained."},{"url":"https://github.com/AIM-SKKU/RA-Touch/tree/main","label":"Actual public repository content inventory","fields":["release.code","release.weights","release.license"],"note":"57 tracked paths reviewed; representation code, action-policy code and README placeholders are distinguished."},{"url":"https://huggingface.co/datasets/yoorhim/ImageNet-T/tree/main","label":"Actual ImageNet-T file inventory","fields":["release.data","model.trainingData"],"note":"Public 10K/50K/100K/150K annotation and embedding files exist; these are visual/tactile-caption retrieval resources, not tactile sensor episodes."}],"caveats":["Retrieval augmentation supports tactile perception; no robot-control policy is introduced or evaluated.","Some external tactile descriptions are inferred from visual/context cues and can be inaccurate; they are not sensor measurements."],"verified":"2026-10-17"},{"id":"sparsh-skin","name":"Sparsh-skin","title":"Self-supervised perception for tactile skin covered dexterous hands","date":"2025-05-16","year":2025,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Pretrains contextual full-hand magnetic-skin representations from unlabeled play with fingertip, phalanx and palm coverage.","institution":"FAIR at Meta; Carnegie Mellon University; University of Washington","venue":"CoRL 2025","modalities":["Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Allegro hand"],"links":{"paper":"https://arxiv.org/abs/2505.11420","code":"https://github.com/facebookresearch/sparsh-multisensory-touch","data":"https://huggingface.co/datasets/facebook/sparsh-skin-dataset","weights":"https://huggingface.co/facebook/sparsh-skin"},"model":{"role":"Reusable representation or perception model; downstream controllers are separate systems","family":"Tactile encoder","baseModel":"Taxel-token transformer with kinematic 3D position and hand-region encodings; masked self-distillation","modelSize":"Not reported","inputs":"100 ms history from 368 three-axis Xela uSkin taxels, and their 3D positions computed from hand kinematics","outputs":"Per-taxel latent touch embeddings for force, pose and policy decoders","trainingMethod":"Block-masked student–teacher self-distillation; subsequent frozen or fine-tuned task decoders","trainingData":"Approximately four hours of unlabeled Allegro-hand play with 14 household objects; downstream labels are separate.","trainingTime":"500 pretraining epochs; wall-clock duration not reported","trainingHardware":"8 NVIDIA A100 80GB GPUs for pretraining; 1 A100 or RTX 4090 for downstream heads","inference":"Approximately 7 ms encoder inference reported; control frequency depends on downstream integration","evaluation":"Force estimation, in-hand pose, object identity, joystick control and plug-insertion policy evaluation."},"release":{"code":"Available","weights":"Available — official download link","data":"Available — official resource link","license":"CC BY-NC 4.0 repository LICENSE.md; official dataset and checkpoint terms apply separately.","notes":"Code, pretrained encoders and downstream task data are separate artifacts; no policy checkpoint is implied by an encoder release."},"evidence":[{"url":"https://arxiv.org/abs/2505.11420","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Method, experiments and implementation sections; specifications retain component and evaluation scope."},{"url":"https://github.com/facebookresearch/sparsh-multisensory-touch","label":"Official implementation and current release instructions","fields":["release","links"],"note":"README and repository files inspected on 2026-10-17."},{"url":"https://github.com/facebookresearch/sparsh-multisensory-touch/blob/main/LICENSE.md","label":"Repository license file","fields":["release.license"],"note":"Applies to repository scope; model/data/base-model terms can differ."}],"caveats":["Full-hand magnetic sensing is distinct from optical tactile images and from a wrist F/T measurement.","Hand kinematics determines taxel position; sensor layout/calibration remain part of the input assumptions."],"relatedIds":["sparsh","sparsh-x","tactile-jepa"],"verified":"2026-10-17"},{"id":"past-token-prediction","name":"Past-Token Prediction (PTP)","title":"Learning Long-Context Diffusion Policies via Past-Token Prediction","date":"2025-05-14","year":2025,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Past-action reconstruction regularizes long-context diffusion policies and reduces training cost through cached visual features.","institution":"Stanford University","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2505.09561","project":"https://long-context-dp.github.io","code":"https://github.com/long-context-dp/ldp"},"policy":{"family":"Diffusion","baseModel":"Transformer Diffusion Policy with past-token auxiliary prediction and a frozen short-context visual encoder.","modelSize":"Not reported","inputs":"Long histories of RGB observations, proprioception and previous actions.","outputs":"Future robot action chunks plus auxiliary past-action reconstruction during training.","trainingData":"Push-T, RoboMimic, added long-horizon simulated tasks and real ALOHA tasks; tape replacement has 150 demonstrations.","trainingTime":"Default policies: 500 epochs; long-horizon ALOHA: 1,500 epochs. Wall-clock duration varies by setting and is not summarized as one total.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Train a short-context visual encoder, cache features, then optimize long-context diffusion with past-token prediction.","evaluation":"Simulated memory-dependent tasks and real bimanual tape replacement / long-horizon manipulation."},"release":{"code":"Available","weights":"Partial","data":"Not verified","license":"Not verified","notes":"Official implementation includes cached-feature tooling and example observation-encoder checkpoints; those are not necessarily all final action policies."},"evidence":[{"url":"https://arxiv.org/abs/2505.09561","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://long-context-dp.github.io","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/long-context-dp/ldp","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."}],"caveats":["Past tokens are auxiliary training targets; future information is not provided at deployment."],"verified":"2026-10-17"},{"id":"vtla","name":"VTLA","title":"VTLA: Vision-Tactile-Language-Action Model with Preference Learning for Insertion Manipulation","date":"2025-05-14","year":2025,"collections":["policy"],"categories":["tactile-foundation","tactile-il"],"resourceType":"policy","summary":"Vision-guided tactile tokens and direct preference optimization adapt a language-action model for simulated and real insertion.","institution":"Institute of Automation, Chinese Academy of Sciences / Samsung R&D Institute China–Beijing / Beijing Academy of Artificial Intelligence","venue":"Preprint","modalities":["RGB","Tactile","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2505.09577","project":"https://sites.google.com/view/vtla"},"policy":{"family":"VLA","baseModel":"Qwen2-VL-7B; visual encoder and modality adapter frozen during language-model tuning.","modelSize":"7B base model; total trainable parameters Not reported.","inputs":"Wrist RGB, bilateral GelStereo 2.0 tactile image sequences and a language instruction; RealSense D405 is used for images, not a reported depth-policy stream.","outputs":"Numeric-text corrective Δx, Δy and yaw actions for iterative insertion.","trainingMethod":"Stage 1 next-token instruction tuning with vision-guided temporally enhanced tokens; Stage 2 DPO ranks sampled actions by distance to ground truth.","trainingData":"28,000 simulated assembly samples over five peg shapes and 0.6–2.0 mm clearances; evaluation includes 6k ID / 4k OOD samples. DPO variants use 1,000 or 2,400 preference pairs. Counts are not demonstration episodes.","trainingTime":"10 SFT epochs and 3 DPO epochs; wall-clock time Not reported.","trainingHardware":"Not reported.","controlRate":"Attempt-based insertion; physical policy Hz Not reported. Appendix records tactile acquisition at 20 fps, which is not the VLM control rate.","evaluation":"Isaac Gym with custom tactile rendering, version Not reported; UR3 / Robotiq 2F-85 with two GelStereo sensors. Fifty trials per simulated setting and 20 per real setting; shape, clearance and dim-light transfer."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No retrievable official policy code/checkpoint/data release was verified."},"evidence":[{"url":"https://arxiv.org/abs/2505.09577","label":"Original paper and reviewed full text","fields":["title","date","institution","summary","policy","caveats"],"note":"Full primary text, including available supplementary implementation details; first public date retained separately from reviewed revision."},{"url":"https://sites.google.com/view/vtla","label":"Official author project","fields":["links","release"],"note":"Current project links and release claims checked independently of paper prose."}],"caveats":["All policy training uses simulation; real experiments evaluate zero-shot sim-to-real transfer.","Fine insertion begins with a grasped peg and bounded initial misalignment; at most 15 attempts.","The project presents videos; public code, checkpoints and dataset artifacts were not verified."],"verified":"2026-10-17"},{"id":"cltp","name":"CLTP / Tac3D-LLM","title":"CLTP: Contrastive Language-Tactile Pre-training for 3D Contact Geometry Understanding","date":"2025-05-13","year":2025,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Contrastive alignment links 3D tactile contact geometry to language; a downstream tactile-language model also demonstrates primitive-based grasp refinement.","institution":"Institute of Automation, Chinese Academy of Sciences; Beihang University; Beijing University of Posts and Telecommunications; Samsung Research China","venue":"arXiv preprint 2025","modalities":["Tactile","Point cloud","Language"],"environments":["Simulation","Real robot"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2505.08194","project":"https://sites.google.com/view/cltp/"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Abstract advertises code/data release, but the inspected official Google Sites page exposes no verified code, checkpoint or dataset download destination."},"model":{"role":"Contact-state representation and tactile-language grounding","family":"Tactile-language model","baseModel":"ULIP-2-derived 3D tactile encoder aligned to frozen CLIP image/text encoders; Tac3D-LLM adds a projection into Qwen2.5-VL-3B.","modelSize":"Tac3D-LLM uses a 3B language-model backbone; tactile encoder and complete-system parameter counts are not reported.","inputs":"1,024-point tactile contact clouds; paired rendered contact images and contact-state language during pretraining. These are local deformation geometry, not external scene-depth observations.","outputs":"Contact-aligned embeddings, contact-state predictions and tactile-language responses; Appendix B converts responses to predefined robot adjustments.","trainingMethod":"Frozen vision-language towers supervise tactile contrastive alignment; caption training adapts an MLP for Tac3D-LLM.","trainingData":"TCL3D: 50,860 simulated contacts from 117 objects; 1,450 GelStereo and 115 GelSight real contacts support transfer evaluation. These are distinct subsets.","trainingTime":"Not reported in the inspected primary manuscript","trainingHardware":"Not reported in the inspected primary manuscript","inference":"Not reported in the inspected primary manuscript","evaluation":"Zero-shot/classified contact attributes and tactile question answering; Appendix B demonstrates closed-loop strawberry positioning and delicate grasp refinement using predefined motion/force primitives."},"evidence":[{"url":"https://arxiv.org/abs/2505.08194","label":"Original manuscript and first public submission history","fields":["title","date","institution","model","policy","modalities","environments","caveats"],"note":"Full author method, evaluation and appendix reviewed; downstream controller probes are distinguished from the reusable model."},{"url":"https://sites.google.com/view/cltp/","label":"Official author project and current artifact destinations","fields":["release","venue","imageCandidates"],"note":"A project button or advertised artifact does not establish a downloadable release; paper-version differences are retained."},{"url":"https://arxiv.org/html/2505.08194v1#A2","label":"Appendix B: actual Tac3D-LLM grasp-refinement loop","fields":["model.evaluation","model.outputs","caveats"],"note":"Algorithm 1 selects predefined position/force primitives from tactile-language feedback; the manuscript illustrates a strawberry grasp rather than reporting a broad policy evaluation."}],"caveats":["Primary role is reusable tactile pretraining; its qualitative Tac3D-LLM agent application does not establish a general continuous-action policy or a large manipulation benchmark.","Depth labels describe local tactile indentation, not measured scene depth; contact descriptions are qualitative rather than calibrated force estimates."],"verified":"2026-10-17"},{"id":"uniskill-human-video","name":"UniSkill (Human Video Imitation)","title":"UniSkill: Imitating Human Videos via Cross-Embodiment Skill Representations","date":"2025-05-13","year":2025,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Learns embodiment-agnostic skill representations through inverse and forward skill dynamics, then conditions robot diffusion policies on skills extracted from human videos.","institution":"Yonsei University","venue":"CoRL 2025","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2505.08787","project":"https://kimhanjung.github.io/UniSkill","code":"https://github.com/KimHanjung/UniSkill","data":"https://huggingface.co/datasets/HanjungKim/UniSkill","weights":"https://huggingface.co/HanjungKim/UniSkill"},"policy":{"family":"Diffusion","baseModel":"ResNet-18 inverse skill-dynamics encoder, forward skill-dynamics model and Diffusion Policy; DepthAnythingV2-small supplies monocular depth features.","modelSize":"Not reported","inputs":"Human / robot video prompt, current RGB views and robot state; learned skills provide cross-embodiment conditioning.","outputs":"Robot-action chunks from a skill-conditioned diffusion policy.","trainingData":"Robot Bridge / LIBERO / DROID and human Something-SomethingV2 / H2O videos. Benchmark-specific policy demonstrations include 100 per tabletop task and 50 per LIBERO task.","trainingTime":"Reported policy schedules: DROID 50,000, tabletop 25,000, Bridge 5,000 and LIBERO 200,000 steps; elapsed duration is not reported.","trainingHardware":"Not reported","controlRate":"Real RGB cameras record at 15 Hz; this is a sensor recording rate rather than a separate inference-throughput measurement.","trainingMethod":"Offline representation and policy learning; fixed learned skills condition unseen human-video prompts without target-prompt weight updates.","evaluation":"Tabletop, toy-kitchen and LIBERO tasks, cross-embodiment prompts and skill composition."},"release":{"code":"Available","weights":"Partial","data":"Partial","license":"MIT (code); model, dependency and dataset terms are separate.","notes":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact. Released artifacts include forward / inverse skill-dynamics weights and tabletop / kitchen data. This endpoint does not establish release of every trained downstream action policy or every upstream dataset. This model card declares mit. Released artifacts include forward / inverse skill-dynamics weights and tabletop / kitchen data. This endpoint does not establish release of every trained downstream action policy or every upstream dataset. This dataset card declares mit."},"evidence":[{"url":"https://arxiv.org/abs/2505.08787","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/KimHanjung/UniSkill/main/README.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."},{"url":"https://raw.githubusercontent.com/KimHanjung/UniSkill/main/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Code-license text checked directly; this license is not automatically assigned to model weights or third-party data."},{"url":"https://kimhanjung.github.io/UniSkill","label":"Official project links / artifacts","fields":["links","release"],"note":"Author-linked repository / model / dataset endpoints are audited individually; a link alone does not establish a permissive asset license."},{"url":"https://huggingface.co/HanjungKim/UniSkill","label":"Author-linked Hugging Face model files / card","fields":["links.weights","release.weights","release.notes"],"note":"Released artifacts include forward / inverse skill-dynamics weights and tabletop / kitchen data. This endpoint does not establish release of every trained downstream action policy or every upstream dataset. Card license: mit."},{"url":"https://huggingface.co/datasets/HanjungKim/UniSkill","label":"Author-linked Hugging Face dataset files / card","fields":["links.data","release.data","release.notes"],"note":"Released artifacts include forward / inverse skill-dynamics weights and tabletop / kitchen data. This endpoint does not establish release of every trained downstream action policy or every upstream dataset. Card license: mit."}],"caveats":["Distinct from Uni-Skill (arXiv:2603.02623), a self-evolving skill-repository system.","DepthAnythingV2 produces inferred monocular features; this does not establish measured depth as a deployment input."],"verified":"2026-10-17"},{"id":"gelfusion","name":"GelFusion","title":"GelFusion: Enhancing Robotic Manipulation under Visual Constraints via Visuotactile Fusion","date":"2025-05-12","year":2025,"collections":["policy"],"categories":["tactile-il","tactile-foundation"],"resourceType":"policy","summary":"Vision-dominated cross-attention fuses tactile geometry and interaction dynamics for manipulation under visual constraints.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2505.07455","project":"https://gelfusion.github.io/","code":"https://github.com/GelFusion/GelFusion","data":"https://huggingface.co/datasets/shu1ong/GelFusion"},"policy":{"family":"Diffusion","baseModel":"Dual-channel GelSight representation and visual–tactile cross-attention policy.","modelSize":"Not reported","inputs":"RGB, GelSight tactile images/dynamics and robot state.","outputs":"Robot manipulation action predictions.","trainingData":"50 demonstrations each for whiteboard wiping, peg insertion and fragile chip grasping.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Imitation learning with dual-channel tactile feature extraction and fusion.","evaluation":"Three real contact-rich tasks with visual ambiguity/occlusion."},"release":{"code":"Not released — implementation files absent from current repository","weights":"Not verified","data":"Available — three processed tactile dataset archives","license":"MIT","notes":"The current only branch and GitHub release inventory contain README, LICENSE, environment.yml and an illustration, with no actual implementation despite README references to DataCollection/train.py. The official linked HF dataset does contain ChipPick_replay_buffer.zip, Insertation_replay_buffer.zip and wiping_replay_buffer.zip; code and data availability are independent."},"evidence":[{"url":"https://arxiv.org/abs/2505.07455","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://gelfusion.github.io/","label":"Official author project","fields":["links"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://github.com/GelFusion/GelFusion","label":"Official released implementation and LICENSE","fields":["links"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://github.com/GelFusion/GelFusion","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/datasets/shu1ong/GelFusion","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["Tactile-image geometry is not an external measured depth stream."],"verified":"2026-10-19"},{"id":"3d-cavla","name":"3D-CAVLA","title":"3D CAVLA: Leveraging Depth and 3D Context to Generalize Vision Language Action Models for Unseen Tasks","date":"2025-05-09","year":2025,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Depth encoding, task-oriented ROI pooling and prompted decomposition add 3D context to an OpenVLA-derived policy.","institution":"New York University","venue":"3D LLM/VLA Workshop at CVPR 2025","modalities":["RGB","Depth","Point cloud","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2505.05800","project":"https://3d-cavla.github.io/","code":"https://github.com/vineet2104/3dcavla","data":"https://huggingface.co/datasets/bhatvineet/modified_libero_rlds_cotdep"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2505.05800","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://3d-cavla.github.io/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/vineet2104/3dcavla","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/vineet2104/3dcavla/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"1409 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."},{"url":"https://huggingface.co/datasets/bhatvineet/modified_libero_rlds_cotdep","label":"Official data release destination","fields":["release.data"],"note":"Destination linked by the official project/repository; component release scope is retained in release notes."}],"caveats":["Reasoning/ROI computation is precomputed once; it is not recurrent online replanning."],"verified":"2026-10-17","policy":{"family":"VLA","baseModel":"OpenVLA/OFT backbone, compact point-cloud encoder and offline reasoning/ROI modules.","modelSize":"~1M-parameter depth encoder; VLA backbone counted separately","inputs":"RGB, depth-derived points, language/CoT context and proprioception.","outputs":"Action chunks for tabletop manipulation.","trainingMethod":"LoRA imitation finetuning with precomputed reasoning and ROI masks.","trainingData":"LIBERO demonstrations with depth; physical Franka: ten tasks × 50 demonstrations.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Single NVIDIA A100, batch size eight","evaluation":"LIBERO seen/unseen task splits and physical unseen-task/compounded-instruction evaluation.","controlRate":"Approximately 4.3–4.4 Hz on A100; offline CoT/ROI preprocessing excluded."}},{"id":"univla","name":"UniVLA (OpenDriveLab)","title":"UniVLA: Learning to Act Anywhere with Task-centric Latent Actions","date":"2025-05-09","year":2025,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Learns task-centric latent actions from videos and decodes them into robot actions with a small embodiment-specific module.","institution":"University of Hong Kong / OpenDriveLab","venue":"RSS 2025","modalities":["RGB","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2505.06111","code":"https://github.com/OpenDriveLab/UniVLA","weights":"https://huggingface.co/qwbu/univla-7b"},"policy":{"family":"VLA","baseModel":"Prismatic-7B (DINOv2/SigLIP + Llama2) + task-centric latent-action model + lightweight action decoder","modelSize":"7B VLA backbone + 10.8M action decoder; latent-action model separate","inputs":"RGB observation and language instruction; target-embodiment action decoder","outputs":"Four latent tokens and decoded continuous chunks; real setting uses 12-step trajectories","trainingData":"OXE manipulation, navigation data and Ego4D human video; downstream robot action labels for adaptation","trainingTime":"960 A100 GPU-hours pretraining; not elapsed wall-clock time","trainingHardware":"NVIDIA A100 pretraining; RTX 4090 real inference","controlRate":"Reported real closed-loop 10 Hz inference on RTX 4090 with 12-step action chunks","trainingMethod":"Unsupervised task-centric action learning, latent-token VLA pretraining, target action-decoder training","evaluation":"LIBERO, CALVIN, SimplerEnv and real robot tasks; also navigation evaluated separately"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 (code); Prismatic/Llama-derived weights and underlying datasets have separate terms","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2505.06111","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/OpenDriveLab/UniVLA","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."}],"caveats":["This is OpenDriveLab UniVLA (arXiv2505.06111), distinct from BAAI UniVLA (2506.19850).","Action-free video pretraining still requires an embodiment-specific supervised action decoder for physical deployment."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 (code); Prismatic/Llama-derived weights and underlying datasets have separate terms","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"stola","name":"SToLa","title":"SToLa: Self-Adaptive Touch-Language Framework with Tactile Commonsense Reasoning in Open-Ended Scenarios","date":"2025-05-07","year":2025,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Touch-language reasoning uses modality-adaptive mixture-of-experts processing, with instruction tuning and open-ended tactile question answering.","institution":"Beijing Jiaotong University; Beijing University of Posts and Telecommunications","venue":"AAAI 2026","modalities":["Tactile","Language"],"environments":["Real robot"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2505.04201","project":"https://cocacola-lab.github.io/SToLa-Page/","code":"https://github.com/cocacola-lab/STOLA","publication":"https://ojs.aaai.org/index.php/AAAI/article/view/38882"},"release":{"code":"Not released","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Actual official repository contains only README.md and says updates are coming; no implementation, model checkpoint or dataset release was verified."},"model":{"role":"Tactile commonsense reasoning","family":"Tactile-language model","baseModel":"TLV-Link touch encoder, linear adapter and Vicuna-7B v1.5; LoRA attention tuning and replicated FFN mixture-of-experts layers.","modelSize":"Nominal 7B Vicuna backbone; total and active parameter counts after expert replication are not reported.","inputs":"Tactile images or sampled video frames interleaved with textual instructions.","outputs":"Free-form tactile descriptions and reasoning answers; no robot actions.","trainingMethod":"Stage I adapts touch tokens with frozen LLM; Stage II tunes adapter/LLM with LoRA and MoE while freezing touch encoder and word embeddings.","trainingData":"Touch100k language-pair adaptation; PhysiCLeAR video instructions and 5K constructed touch-language instructions. Synthetic descriptions receive stated manual review.","trainingTime":"One epoch per training stage; wall-clock not reported.","trainingHardware":"One NVIDIA A100 80GB; batch size 16.","inference":"Not reported in the inspected primary manuscript","evaluation":"PhysiCLeAR and TactileBench tactile question answering, property comparison and commonsense reasoning; uses text metrics and language-model scoring."},"evidence":[{"url":"https://arxiv.org/abs/2505.04201","label":"Original manuscript and first public submission history","fields":["title","date","institution","model","policy","modalities","environments","caveats"],"note":"Full author method, evaluation and appendix reviewed; downstream controller probes are distinguished from the reusable model."},{"url":"https://cocacola-lab.github.io/SToLa-Page/","label":"Official author project and current artifact destinations","fields":["release","venue","imageCandidates"],"note":"A project button or advertised artifact does not establish a downloadable release; paper-version differences are retained."},{"url":"https://github.com/cocacola-lab/STOLA/tree/main","label":"Actual public repository content inventory","fields":["release.code","release.weights","release.license"],"note":"1 tracked paths reviewed; representation code, action-policy code and README placeholders are distinguished."},{"url":"https://ojs.aaai.org/index.php/AAAI/article/view/38882","label":"Official AAAI 2026 publication","fields":["venue","links.code"],"note":"Official proceedings link the same model and its README-only repository."}],"caveats":["The model reasons about touch; it does not contribute an executable robot agent.","Self-adaptive refers to trained MoE routing, not online weight updates or recursive self-improvement; ICL is used for synthetic-data generation."],"verified":"2026-10-17"},{"id":"kinedex","name":"KineDex","title":"KineDex: Learning Tactile-Informed Visuomotor Policies via Kinesthetic Teaching for Dexterous Manipulation","date":"2025-05-04","year":2025,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"Hand-over-hand demonstrations train a tactile diffusion policy that predicts joint positions and normal fingertip forces for dexterous control.","institution":"Tongji University / Tsinghua University / Shanghai Jiao Tong University / University of Hong Kong / Shanghai Qi Zhi Institute / Shanghai AI Lab","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2505.01974","project":"https://dinomini00.github.io/KineDex/","code":"https://github.com/DinoMini00/KineDex_code"},"policy":{"family":"Diffusion","baseModel":"U-Net Diffusion Policy with multi-view image encoding, shared per-finger 1D convolutional tactile encoder and force-informed controller.","modelSize":"Not reported.","inputs":"Front and wrist RGB, robot state and 5×120×3 tactile force readings from XHand1. Human-body masks and inpainting modify training images, not runtime sensing.","outputs":"16-step chunks of 23-dimensional actions: six end-effector pose dimensions, twelve hand joint angles and five target normal fingertip forces.","trainingMethod":"Task-specific diffusion imitation from kinesthetic demonstrations; Grounded-SAM and ProPainter remove demonstrator occlusion before training. Force targets modify finger position setpoints during deployment.","trainingData":"Nine real tasks. Appendix B reports approximately 100 demonstrations/task for simpler picking, plugging and insertion, and around 150 for more challenging tasks such as toothpaste squeezing and syringe pressing; exact total Not reported.","trainingTime":"500 epochs per task; wall-clock time Not reported.","trainingHardware":"Not reported.","controlRate":"Interpolation controller: arm 100 Hz and hand 50 Hz. Learned policy refresh frequency Not reported; 16-step chunks and eight-step temporal ensembling.","evaluation":"Franka Panda / 12-DoF XHand1: Bottle Picking, Cup Picking, Egg Picking, Cap Twisting, Nut Tightening, Peg Insertion, Charger Plugging, Toothpaste Squeezing and Syringe Pressing; 20 trials/task. Inspire Hand is used separately for collection-efficiency comparisons."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT (actual repository LICENSE); third-party components retain their own licenses.","notes":"Collection, task-specific policy training and evaluation source verified. README explicitly excludes inpainting implementation and refers users to external tools. Public checkpoints/dataset downloads not verified."},"evidence":[{"url":"https://arxiv.org/abs/2505.01974","label":"Original paper, §§3–4 and Appendices A–D","fields":["title","date","summary","institution","modalities","policy","caveats"],"note":"Original XHand1 policy: 120 three-axis taxels/finger. Benchmark-specific resampling and adapter dimensions do not redefine this interface."},{"url":"https://dinomini00.github.io/KineDex/","label":"Official author project","fields":["links","release"],"note":"Author-linked implementation identity and artifact links reviewed."},{"url":"https://github.com/DinoMini00/KineDex_code","label":"Official implementation, README and LICENSE","fields":["links","release"],"note":"Collection/training/evaluation code and MIT LICENSE inspected; inpainting step explicitly not included."}],"caveats":["Predicts five normal-force targets although tactile observations contain three-axis forces; no wrist six-axis F/T stream is claimed.","The force-informed controller adjusts finger targets using fixed stiffness coefficients; it is distinct from direct torque-action learning.","Hardware for the nine-task policy evaluation is XHand1; collection-efficiency/user studies use an Inspire Hand setup.","Arm/hand interpolation rates are not the learned-policy inference frequency."],"verified":"2026-10-17"},{"id":"tesseract-world","name":"TesserAct","title":"TesserAct: Learning 4D Embodied World Models","date":"2025-04-29","year":2025,"collections":["policy"],"categories":["rgbd-3d","geometry-models"],"resourceType":"policy","summary":"Joint RGB/depth/normal video generation provides 4D forecasts that can be paired with task-trained inverse dynamics.","institution":"UMass Amherst; HKUST; Harvard University","venue":"ICCV 2025","modalities":["RGB","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2504.20995","project":"https://tesseractworld.github.io/","code":"https://github.com/UMass-Embodied-AGI/TesserAct","weights":"https://huggingface.co/anyeZHY/tesseract"},"release":{"code":"Partial — world-model training/inference; task inverse dynamics unverified","weights":"Partial — released RGB/RGB-D-normal world-model checkpoints","data":"Not verified","license":"MIT for repository code; pretrained models, data and figure rights remain separate","notes":"The official HF inventory contains an RGB LoRA and three RGB-D-normal SFT safetensors shards. These establish a world-model checkpoint release; the separate task-specific inverse-dynamics policy/checkpoint remains unverified. Code, world-model weights and executable task policies are separate artifacts."},"evidence":[{"url":"https://arxiv.org/abs/2504.20995","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://tesseractworld.github.io/","label":"Official author project","fields":["institution","imageCandidates"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://github.com/UMass-Embodied-AGI/TesserAct","label":"Official implementation and release scope","fields":["links"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://github.com/UMass-Embodied-AGI/TesserAct/blob/main/LICENSE","label":"Actual license file in released source","fields":["links"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://github.com/UMass-Embodied-AGI/TesserAct/tree/main","label":"Actual repository content inventory","fields":["links"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://huggingface.co/anyeZHY/tesseract","label":"Official weights release destination","fields":["links"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://github.com/UMass-Embodied-AGI/TesserAct","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/anyeZHY/tesseract","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["Generated future geometry is not measured sensory feedback.","Public world-model code/checkpoints and the task-specific inverse-dynamics release are separate; the latter is not verified."],"verified":"2026-10-19","policy":{"family":"World-action model","baseModel":"CogVideoX-based multistream video diffusion, geometric consistency and inverse-dynamics action extraction.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Initial image and language; estimated geometry prepares depth/normal conditions where required.","outputs":"RGB/depth/normal video and actions from a separate inverse-dynamics module.","trainingMethod":"4D world-model training; task-specific keyframe adaptation and inverse-dynamics learning.","trainingData":"Robot videos augmented with estimated depth/normals; 500 samples per task for inverse dynamics.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Embodied video prediction and RLBench policy execution.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"polytouch","name":"PolyTouch policy","title":"PolyTouch: A Robust Multi-Modal Tactile Sensor for Contact-rich Manipulation Using Tactile-Diffusion Policies","date":"2025-04-27","year":2025,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"Uses peripheral vision, camera-based touch and acoustic signals in a tactile diffusion policy.","institution":"MIT CSAIL / Toyota Research Institute","venue":"Preprint","modalities":["RGB","Tactile","Audio","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2504.19341","project":"https://polytouch.alanz.info/"},"policy":{"family":"Diffusion","baseModel":"Multimodal tactile-diffusion policy with attention-based fusion","modelSize":"Not reported","inputs":"RGB, camera-based tactile observations, acoustics and proprioception","outputs":"Robot manipulation action sequences","trainingData":"Human demonstrations on contact-aware tasks; count not reported","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Multimodal diffusion imitation learning on PolyTouch finger hardware","evaluation":"Contact-rich real manipulation with ablations of tactile modalities"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."},"evidence":[{"url":"https://arxiv.org/abs/2504.19341","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://polytouch.alanz.info/","label":"Author project page","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2504.19341","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Sensor paper includes a policy evaluation; its multimodal sensor differs from conventional GelSight-only setups."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."}},"relatedIds":[]},{"id":"pp-tac","name":"PP-Tac","title":"PP-Tac: Paper Picking Using Tactile Feedback in Dexterous Robotic Hands","date":"2025-04-23","year":2025,"collections":["policy"],"categories":["tactile-il","tactile-foundation"],"resourceType":"policy","summary":"Tactile surface/slip feedback and learned finger–wrist coordination pick thin paper-like objects from varied terrain.","institution":"BIGAI / ShanghaiTech University / Bei hang University","venue":"RSS 2025","modalities":["Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2504.16649","project":"https://peilin-666.github.io/projects/PP-Tac","code":"https://github.com/bigai-ai/PP-Tac"},"policy":{"family":"Diffusion","baseModel":"Omni directional tactile fingertips, slip CNN/MLP and transformer diffusion motion generator.","modelSize":"Not reported","inputs":"Tactile contact deformation/slip and hand/arm state.","outputs":"Coordinated finger joints, wrist pose and contact-adjusted sliding motions.","trainingData":"~20 min tactile slip data with manual labels; synthetic trajectory sequences for motion training.","trainingTime":"~600,000 motion model iterations; wall-clock Not reported.","trainingHardware":"Single NVIDIA RTX4090.","controlRate":"Motion generation 11 mson RTX4090; not a reported overall control frequency.","trainingMethod":"Slip class i fier learning and~600 k iteration diffusion motion training.","evaluation":"Real paper/fabric picking across flat/complex supporting terrains."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Not verified; no unambiguous repository license identified.","notes":"Author-linked repository and actual LICENSE inspected; code, checkpoints anddatasets have independent availability."},"evidence":[{"url":"https://arxiv.org/abs/2504.16649","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://peilin-666.github.io/projects/PP-Tac","label":"Official author project","fields":["links","release"],"note":"Live artifact status reviewed separately from paper claims."},{"url":"https://github.com/bigai-ai/PP-Tac","label":"Official released implementation and LICENSE","fields":["links","release"],"note":"Repository metadata,README and actualNone inspected; availability scoped to the listed component."}],"caveats":["Tactile-depth reconstruction is local sensor surface deformation, not external scene depth."],"verified":"2026-10-17"},{"id":"pi05","name":"π0.5","title":"π0.5: a Vision-Language-Action Model with Open-World Generalization","date":"2025-04-22","year":2025,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Extends the π family using heterogeneous robot, web and semantic data for open-world mobile manipulation.","institution":"Physical Intelligence","venue":"CoRL 2025","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Bimanual","Mobile manipulator","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2504.16054","project":"https://www.pi.website/blog/pi05","code":"https://github.com/Physical-Intelligence/openpi","weights":"https://github.com/Physical-Intelligence/openpi#base-models"},"policy":{"family":"VLA","baseModel":"PaliGemma-based VLA with FAST discrete training outputs and a flow-matching action expert","modelSize":"3.3B-class released π0.5 model; exact total varies with implementation","inputs":"Multi-view RGB, language prompt/subtask and robot state","outputs":"Continuous robot action chunks plus semantic subtask predictions","trainingData":"~400 h mobile manipulation in ~100 homes plus larger non-mobile, lab, web and semantic-prediction mixtures","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"50 Hz target joint/gripper/torso/base commands; inference is chunked","trainingMethod":"Heterogeneous pretraining followed by combined discrete/continuous post-training","evaluation":"Novel homes: kitchen/bedroom cleanup and long sequences involving mobile bimanual manipulation"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 (openpi code); model/data terms require per-checkpoint verification","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2504.16054","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/Physical-Intelligence/openpi","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://www.pi.website/blog/pi05","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."}],"caveats":["400 h refers specifically to mobile-manipulator data, not total training data.","Hierarchical subtask prompting is part of the reported long-horizon system."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 (openpi code); model/data terms require per-checkpoint verification","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"forceful-foundation-survey","name":"Forceful Robotic Foundation Models — Survey","title":"Towards Forceful Robotic Foundation Models: a Literature Survey","date":"2025-04-16","year":2025,"collections":["policy"],"categories":["tactile-foundation","tactile-representation"],"resourceType":"survey","summary":"Reviews how force is sensed, collected, represented and controlled in manipulation-policy learning, distinguishing skin touch from wrist and joint sensing.","institution":"University of Colorado Boulder","venue":"Preprint · Survey","modalities":[],"environments":["Literature review"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2504.11827"},"survey":{"scope":"Force-aware manipulation learning, spanning tactile sensing, wrist F/T, joint torques, action spaces, data collection and multimodal representation.","taxonomy":["Force/touch sensing and robot proprioception","Data collection: teleoperation, kinesthetic and augmented teaching","Action spaces and explicit force/compliance control","Behavior cloning and visuotactile/force representations","Scaling multimodal reasoning toward foundation models"],"adaptationRegimes":["Behavior cloning","Reinforcement learning","Representation pretraining and transfer","Explicit low-level force/compliance control"],"contextSources":["Fingertip tactile/pressure/audio signals","Wrist force/torque","Whole-arm joint sensing","Vision, language and demonstrations"],"evaluationAxes":["Required interaction forces and task duration","Sensor modality and placement","Data collection and amount","Action/control interface","Policy architecture and force dependence"],"corpus":"20-page review; Section 3 analyzes 25 selected works. The complete bibliography also contains background literature.","reviewMethod":"Comparative narrative analysis of selected force-aware manipulation papers, with distributions of sensing, data collection, action spaces and model architectures.","coverageLimits":"The 25 selected works are not an exhaustive modern tactile-model inventory. The discussion questions whether existing tasks fully isolate force dependence; it does not show that touch is unnecessary for precision manipulation."},"release":{"code":"Not applicable — survey; no new robot implementation","weights":"Not applicable — survey","data":"Not verified — no separate coded corpus linked","license":"Paper and third-party figures retain their stated original rights","notes":"Literature inventories and review tables are not robot demonstration data or deployable model weights."},"evidence":[{"url":"https://arxiv.org/abs/2504.11827","label":"Original survey and first submission","fields":["title","date","summary","institution","survey"],"note":"Manuscript structure, tables and bibliography inspected; underlying method specifications require their original paper."},{"url":"https://arxiv.org/html/2504.11827","label":"Primary full-text survey and references","fields":["survey"],"note":"Citation extraction preserves original bibliography identities, including background and adjacent resources."}],"caveats":["Survey, not an executable policy or model; deployment modalities, training costs and control rates do not apply."],"relatedIds":["sparsh","unitouch","t3","tvl","anytouch","anytouch2","sparsh-x","sparsh-skin"],"verified":"2026-10-17","surveyAuditId":"forceful-foundation-survey-references"},{"id":"vitamin","name":"ViTaMIn","title":"ViTaMIn: Learning Contact-Rich Tasks Through Robot-Free Visuo-Tactile Manipulation Interface","date":"2025-04-08","year":2025,"collections":["policy"],"categories":["tactile-il","tactile-foundation"],"resourceType":"policy","summary":"A compliant handheld visual-tactile interface and multimodal pretraining support contact-rich robot imitation.","institution":"Tsinghua University / University of California, Berkeley","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2504.06156","project":"https://chuanyune.github.io/ViTaMIn_page/"},"policy":{"family":"Diffusion","baseModel":"UMI-style diffusion policy with masked/contrastive tactile pretraining.","modelSize":"Not reported","inputs":"RGB, tactile images and gripper/pose observations.","outputs":"End-effector/gripper action sequences.","trainingData":"Raw demonstration counts 87–172 per single-arm task, with approximately 80%SLAM tracking success; retained training demonstrations differ from raw collection.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"10 Hz robot control.","trainingMethod":"Action-free multimodal tactile pretraining then demonstration-based policy learning.","evaluation":"Main latest text evaluates five real contact-rich tasks including test-tube reorientation and dual-arm knife pulling."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2504.06156","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://chuanyune.github.io/ViTaMIn_page/","label":"Official author project","fields":["links","release"],"note":"Live artifact status reviewed separately from paper claims."}],"caveats":["Discovery-list/abstract seven-task claim differs from latest main-text five-task evaluation; do not silently aggregate them.","SLAM success filters demonstrations; action-free sensor pretraining can use trajectories rejected for pose learning."],"verified":"2026-10-17"},{"id":"uwm","name":"Unified World Models (UWM)","title":"Unified World Models: Coupling Video and Action Diffusion for Pretraining on Large Robotic Datasets","date":"2025-04-03","year":2025,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Unifies policy, forward dynamics, inverse dynamics and video prediction by independently controlling action and video diffusion times.","institution":"University of Washington / Toyota Research Institute","venue":"RSS 2025","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2504.02792","project":"https://weirdlabuw.github.io/uwm/","code":"https://github.com/WEIRDLabUW/unified-world-model"},"policy":{"family":"World-action model","baseModel":"Multimodal diffusion Transformer with observation/action streams and independent modality timesteps.","modelSize":"Complete policy count not reported.","inputs":"RGB cameras; proprioception where configured, with language conditioning for LIBERO.","outputs":"Actions or visual futures depending on query mode; physical seven-dimensional end-effector delta/gripper actions.","trainingData":"DROID robot trajectories, physical target-task data, LIBERO/robomimic and action-free video co-training.","trainingTime":"DROID pretraining: 100k steps in approximately 24 hours. Target-task fine-tuning is separate.","trainingHardware":"Four A100 GPUs for DROID pretraining.","controlRate":"Physical Franka control at 10 Hz; not a measured common model-query throughput.","trainingMethod":"Coupled video/action diffusion with independent noise levels; missing action labels masked on action-free videos.","evaluation":"Physical Franka/DROID tasks, LIBERO and robomimic; ID/OOD distractors and learning-from-scratch comparisons."},"release":{"code":"Available","weights":"Not verified","data":"Partial","license":"Not verified; pretrained backbones and data retain separate terms","notes":"Availability concerns author-linked artifacts; no training or installation reproduced."},"evidence":[{"url":"https://arxiv.org/abs/2504.02792","label":"Author manuscript and submission history","fields":["title","date","institution","policy","modalities","environments"],"note":"Read full manuscript; first arXiv submission retained separately from current revision. Numeric costs and observations have setting-specific scope."},{"url":"https://github.com/WEIRDLabUW/unified-world-model","label":"Official implementation","fields":["release"],"note":"Author-linked code and artifact instructions; current release may differ from the original manuscript."},{"url":"https://weirdlabuw.github.io/uwm/","label":"Official project","fields":["venue","imageCandidates","release"],"note":"Author project and links; scientific figure keeps original rights."}],"caveats":["The model exposes an executable policy mode, in addition to video simulation.","Observation/action context varies across benchmarks."],"verified":"2026-10-17"},{"id":"gift-functional-transfer","name":"GIFT","title":"GIFT: Geometry-Induced Functional Transfer for Category-level Object Manipulation","date":"2025-03-19","year":2025,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Functional surface maps transfer robot-object/environment constraints and reconstruct task-preserving screw trajectories.","institution":"University of Birmingham / Technical University of Munich / University of Nottingham / Sorbonne Université","venue":"Preprint","modalities":["Depth","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2503.15371"},"policy":{"family":"Geometric transfer","baseModel":"Geometry-induced functional skill transfer. Laplace-Beltrami functional-map correspondence, robot/environment interaction functions and dual-quaternion ScLERP.","modelSize":"Not reported","inputs":"Wrist Ensenso N35 3D scans stitched into segmented meshes, a kinesthetic demonstrated motion and robot/environment interaction functions.","outputs":"Transferred grasp/contact functions and robot trajectory preserving geometric relations.","trainingData":"A single demonstrated skill plus source/target object geometry; no large action pretraining dataset.","trainingTime":"No task-specific neural policy training.","trainingHardware":"Experiments on Intel i7 four-core CPU with 32 GB RAM; no GPU requirement reported.","controlRate":"Not reported","trainingMethod":"Infer functional correspondences, transport interaction functions and generate constant-screw motion.","evaluation":"KUKA iiwa / Schunk PG70 physical wiping, drawing, bottle stirring and button-press skill transfer; four-trial quantitative settings."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No author-linked implementation was verified in the inspected primary manuscript/project. This is an evidence gap, not proof that no implementation exists."},"evidence":[{"url":"https://arxiv.org/abs/2503.15371","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."}],"caveats":["Distinct from older tool-keypoint learning papers also called GIFT.","Geometry and constraint transfer is explicit; no direct raw-image action inference is established."],"verified":"2026-10-17"},{"id":"grootn1","name":"GR00T N1","title":"GR00T N1: An Open Foundation Model for Generalist Humanoid Robots","date":"2025-03-18","year":2025,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Open humanoid foundation model combines a VLM and a flow-matching action system trained on real, simulated and video-derived data.","institution":"NVIDIA","venue":"Technical report","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Dexterous hand","Humanoid upper body"],"links":{"paper":"https://arxiv.org/abs/2503.14734","project":"https://research.nvidia.com/labs/gear/gr00t-n1/","code":"https://github.com/NVIDIA/Isaac-GR00T/tree/n1-release","weights":"https://huggingface.co/nvidia/GR00T-N1-2B"},"policy":{"family":"VLA","baseModel":"NVIDIA Eagle-2 VLM + embodiment encoders + DiT flow-matching action head","modelSize":"2.2B total in released GR00T-N1-2B; 1.34B VLM","inputs":"RGB camera views, language, proprioception and embodiment identity","outputs":"16-step continuous motor-action chunks with embodiment-specific decoding","trainingData":"Human video, public robot data, 88 h in-house real teleop; 827 h synthetic video and 780k simulated trajectories reported","trainingTime":"Roughly 50,000 H100 GPU-hours pretraining; not 50,000 wall-clock hours","trainingHardware":"Up to 1,024 H100 GPUs; compute-constrained post-training also tested","controlRate":"16-action chunk 63.9 ms on L40 BF16; dual-system paper describes ~10 Hz VLM / up to 120 Hz motor actions","trainingMethod":"Heterogeneous pretraining with flow matching; embodiment post-training; latent-action/video augmentation","evaluation":"Real/sim single-arm and humanoid manipulation, including dexterous bimanual setups"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 (current Isaac-GR00T code); original N1 model uses NVIDIA OneWay Noncommercial terms; datasets separate","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2503.14734","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/NVIDIA/Isaac-GR00T/tree/n1-release","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://research.nvidia.com/labs/gear/gr00t-n1/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/NVIDIA/Isaac-GR00T/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["Synthetic hours, video-only hours and action-labelled real demonstrations are different data units.","Current repository defaults to N1.7; use the N1 tag for this paper."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 (current Isaac-GR00T code); original N1 model uses NVIDIA OneWay Noncommercial terms; datasets separate","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"elastic-motion-policy","name":"Elastic Motion Policy","title":"Elastic Motion Policy: An Adaptive Dynamical System for Robust and Efficient One-Shot Imitation Learning","date":"2025-03-11","year":2025,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"A stable full-pose dynamical system learned from one demonstration is morphed online as geometric task constraints change.","institution":"University of Pennsylvania","venue":"Preprint","modalities":["RGB","Depth","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2503.08029","project":"https://elastic-motion-policy.github.io/EMP/","code":"https://github.com/penn-figueroa-lab/emp"},"policy":{"family":"Geometric transfer","baseModel":"Stable adaptive dynamical-system motion policy. SE(3) LPV dynamical systems, Gaussian-mixture geometry, Laplacian editing and convex Lyapunov fitting; experimental frontend uses GPT-4o semantic label, Grounded SAM and FoundationPose.","modelSize":"Not reported","inputs":"Core EMP accepts end-effector poses, demonstration poses and tracked geometric constraints. The complete experimental pipeline uses RGB-D FoundationPose tracking and a user-selected key pose to provide these quantities.","outputs":"Continuous translational/orientational velocity field with attractor convergence and optional obstacle modulation.","trainingData":"One target motion demonstration; separate LASA benchmark has thirty 2D handwriting motions with seven trajectories each.","trainingTime":"LASA single-trajectory convex Lyapunov fitting approximately 0.038 seconds, or 0.007 seconds with GMM approximation; these are component timings.","trainingHardware":"Not reported","controlRate":"Approximately 30 Hz for online dynamical-system adaptation; the paper does not equate this component rate with complete perception-to-actuation latency.","trainingMethod":"Fit stable dynamics from demonstration, then optimize/morph model geometry and Lyapunov parameters online for new scene constraints.","evaluation":"LASA component tests and physical full-pose tasks including book placement, changing constraints and obstacle avoidance."},"release":{"code":"Partial","weights":"Not verified","data":"Partial","license":"Penn Software license: non-profit research only","notes":"Repository currently contains the core EMP module and example data; it explicitly restricts use to non-commercial/non-profit research."},"evidence":[{"url":"https://arxiv.org/abs/2503.08029","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://elastic-motion-policy.github.io/EMP/","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://github.com/penn-figueroa-lab/emp","label":"Official code","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://raw.githubusercontent.com/penn-figueroa-lab/emp/main/LICENSE.md","label":"Actual source license file","fields":["release.license"],"note":"Scope limited to the file/repository subtree carrying these terms, not paper figures or external models/data."}],"caveats":["The full system uses an RGB-D pose-estimation frontend; the core stable controller receives estimated poses/constraints rather than learning directly from images.","Component fitting timings are not end-to-end robot policy training or control rates.","The experiment assumes available 3D meshes for object tracking; this is an explicit geometry prior."],"verified":"2026-10-17"},{"id":"tla","name":"TLA","title":"TLA: Tactile-Language-Action Model for Contact-Rich Manipulation","date":"2025-03-11","year":2025,"collections":["policy"],"categories":["tactile-foundation","tactile-il"],"resourceType":"policy","summary":"Tactile image histories and language condition corrective insertion actions through a Qwen2-VL language model.","institution":"Institute of Automation, Chinese Academy of Sciences / Samsung Research China–Beijing / Beijing Academy of Artificial Intelligence (arXiv); Xiaomi EV replaces BAAI affiliation in journal version.","venue":"Robot Learning 2026","modalities":["Tactile","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2503.08548","project":"https://sites.google.com/view/tactile-language-action/","journal":"https://doi.org/10.55092/rl20260001"},"policy":{"family":"VLA","baseModel":"Qwen2-VL / Qwen2-7B, frozen ViT tactile encoder and LoRA language-model adaptation.","modelSize":"7B backbone; adapter and total trainable parameter counts Not reported.","inputs":"Four frames from each of two GelStereo 2.0 fingertips, arranged into one image, plus a text instruction; no external scene RGB or depth.","outputs":"Corrective horizontal translation and yaw: Δx, Δy, Δrz, decoded from numeric text.","trainingMethod":"Next-token supervised fine-tuning with frozen tactile encoder and LoRA language-model updates; action labels derived from simulated peg-hole pose errors.","trainingData":"24k simulated tactile-action instruction samples at 2.0 mm clearance. Single-peg study uses 6k train / 2k test; multi-peg uses 16k train and a separate 8k test set. These are sample counts, not robot episodes.","trainingTime":"Journal §5.2: single-peg 10 epochs / 1.6 h; multi-peg 10 epochs / 4.4 h. ArXiv v1 instead gives 20 epochs for single-peg.","trainingHardware":"8 NVIDIA A6000 GPUs.","controlRate":"Attempt-based corrective control; physical policy Hz Not reported; maximum 15 insertion attempts per episode.","evaluation":"Isaac Gym / Flex-FEM insertion across shapes and clearances; version Not reported. Journal §5.4 adds UR3 with Robotiq-85 and two GelStereo 2.0 sensors, 20 trials per setting; real square-peg success falls from 80% at 2.0 mm to 55% at 1.0 mm."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No retrievable official policy code/checkpoint/data release was verified."},"evidence":[{"url":"https://arxiv.org/abs/2503.08548","label":"Original paper and reviewed full text","fields":["title","date","institution","summary","policy","caveats"],"note":"Full primary text, including available supplementary implementation details; first public date retained separately from reviewed revision."},{"url":"https://sites.google.com/view/tactile-language-action/","label":"Official author project","fields":["links","release"],"note":"Current project links and release claims checked independently of paper prose."},{"url":"https://pdf.elspublishing.com/paper/journal/open/RL/2026/rl20260001.pdf","label":"Published journal extension, §§5.2–5.4 and data availability","fields":["venue","institution","environments","policy.trainingTime","policy.evaluation","release.data","caveats"],"note":"Published 2026-01-07; reviewed training resources and new real-world experiments separately from arXiv v1."}],"caveats":["ArXiv v1 reports simulation and leaves real deployment to future work; the 2026 journal version adds real experiments and revises single-peg training details.","The journal data availability statement offers data on reasonable request; a downloadable public release was not verified despite the original abstract release claim.","The gripper already holds the peg near its target; evaluation concerns tactile correction, not autonomous perception and grasp acquisition."],"verified":"2026-10-17"},{"id":"odil","name":"ODIL","title":"One-Shot Dual-Arm Imitation Learning","date":"2025-03-10","year":2025,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Global and wrist RGB-D alignment stages transfer one coordinated bimanual demonstration to changed object poses.","institution":"Imperial College London","venue":"Preprint","modalities":["RGB","Depth","Proprioception"],"environments":["Real world"],"embodiments":["Bimanual"],"links":{"paper":"https://arxiv.org/abs/2503.06831","project":"https://www.robot-learning.uk/one-shot-dual-arm","code":"https://github.com/kelthuzadyl/ODIL"},"policy":{"family":"Geometric transfer","baseModel":"Three-stage dual-arm visual servoing and replay. FPFH/FGR/ICP, GroundedSAM, SIFTGPU/LightGlue, weighted Kabsch and final homography servoing.","modelSize":"Not reported","inputs":"One dual-arm demonstration, global RealSense D415 and wrist D405 RGB-D observations, robot kinematics and segmentation masks.","outputs":"Coordinated end-effector alignment and replay through TRAC-IK/MoveIt.","trainingData":"One demonstration per new task; pretrained matcher/segmentation components. Separate correspondence study collects 15,600 RGB-D images.","trainingTime":"No task-specific network fitting.","trainingHardware":"Not reported","controlRate":"ABB YuMi position controller at 10 Hz.","trainingMethod":"Global coarse registration, wrist 3D correspondence refinement and image-plane fine alignment before demonstrated interaction.","evaluation":"Six precise coordinated dual-arm tasks, three conditions (4-DoF, 4-DoF with distractors/occlusion, 6-DoF with distractors/occlusion), ten trials each."},"release":{"code":"Partial","weights":"Not verified","data":"Not verified","license":"No repository-wide license found in inspected tree","notes":"Author release describes lightweight wrist-camera visual-servoing controllers, not the entire coordinated dual-arm system."},"evidence":[{"url":"https://arxiv.org/abs/2503.06831","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://www.robot-learning.uk/one-shot-dual-arm","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://github.com/kelthuzadyl/ODIL","label":"Official code","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."}],"caveats":["Correspondence-analysis images are evaluation data, not a 15,600-image action-policy training corpus.","System depends on calibrated cameras, segmentation and explicit kinematic planning."],"verified":"2026-10-17"},{"id":"pointvla","name":"PointVLA","title":"PointVLA: Injecting the 3D World into Vision-Language-Action Models","date":"2025-03-10","year":2025,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Injects depth-derived point features into a pretrained VLA action expert while freezing the original expert.","institution":"Shanghai University / Midea Group","venue":"IEEE RA-L 2025","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2503.07511","project":"https://pointvla.github.io/"},"policy":{"family":"VLA","baseModel":"DexVLA: Qwen2-VL-2B + ScaleDP-1B; iDP3-style point encoder and injection blocks","modelSize":"2B VLM + 1B action expert inherited from DexVLA; added module size not reported","inputs":"Multi-camera RGB, measured point cloud, language and robot state","outputs":"14-dimensional bimanual arm/gripper action chunks","trainingData":"Four-task few-shot setting uses 20 demos/task; RoboTwin 20/50-demo experiments and long-horizon real data","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Data recorded at 15 Hz UR5e / 30 Hz AgileX; inference loop rate not reported","trainingMethod":"Freeze vanilla action expert; train lightweight point-feature injection","evaluation":"RoboTwin; dual UR5e and AgileX: height changes, photo-vs-object, packing moving objects"},"release":{"code":"Not released","weights":"Not released","data":"Not verified","license":"Not verified (code); model and data terms not verified","notes":"Author project page exposes paper and experiments; no official code/weights link verified."},"evidence":[{"url":"https://arxiv.org/abs/2503.07511","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://pointvla.github.io/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."}],"caveats":["Freezing the action expert does not mean the added 3D blocks are untrained.","Dataset collection frequencies are not policy inference frequencies."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not released","weights":"Not released","data":"Not verified","license":"Not verified (code); model and data terms not verified","notes":"Author project page exposes paper and experiments; no official code/weights link verified."}},"relatedIds":[]},{"id":"go1","name":"GO-1 / AgiBot World","title":"AgiBot World Colosseo: A Large-scale Manipulation Platform for Scalable and Intelligent Embodied Systems","date":"2025-03-09","year":2025,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Pairs an InternVL backbone and latent-action planner with a diffusion action expert for generalist bimanual manipulation.","institution":"HKU / AgiBot / SII / Shanghai AI Laboratory","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2503.06669","project":"https://agibot-world.com/","code":"https://github.com/OpenDriveLab/AgiBot-World","data":"https://huggingface.co/datasets/agibot-world/AgiBotWorld-Beta","weights":"https://huggingface.co/agibot-world/GO-1"},"policy":{"family":"VLA","baseModel":"InternVL2.5-2B; learned latent-action planner and diffusion action expert","modelSize":"2B VLM backbone; full aggregate not reported","inputs":"Head/left/right RGB views, robot proprioception and language","outputs":"30-step low-level action chunks conditioned on predicted latent action tokens","trainingData":"AgiBot World corpus: over 1M trajectories, 217 tasks, 5 deployment scenarios; scaling experiments use subsets","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Dataset/control streams recorded at 30 Hz; policy replanning latency not reported","trainingMethod":"Latent-action pretraining and joint generalist action learning; scaling with robot data","evaluation":"Real bimanual, dexterous and long-horizon tasks; in-domain/OOD data-scaling studies"},"release":{"code":"Available","weights":"Available","data":"Gated — automatic Hugging Face access terms","license":"Not reported","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested. The current official artifact inventory is gated (automatic access terms); files are listed but anonymous unrestricted download is not claimed."},"evidence":[{"url":"https://arxiv.org/abs/2503.06669","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/OpenDriveLab/AgiBot-World","label":"Official policy implementation","fields":["links"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://github.com/OpenDriveLab/AgiBot-World","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/datasets/agibot-world/AgiBotWorld-Beta","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://huggingface.co/agibot-world/GO-1","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["AgiBot World supports visuo-tactile collection, but GO-1 architecture conditions on RGB, proprioception and language; dataset sensor availability is not policy usage."],"verified":"2026-10-19"},{"id":"rise2","name":"RISE-2","title":"AirExo-2: Scaling up Generalizable Robotic Imitation Learning with Low-Cost Exoskeletons","date":"2025-03-05","year":2025,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Fuses sparse 3D spatial features and 2D semantic features to learn manipulation from adapted in-the-wild exoskeleton demonstrations.","institution":"Shanghai Jiao Tong University","venue":"CoRL 2025","modalities":["RGB","Depth","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2503.03081","project":"https://airexo.tech/airexo2/#rise2","code":"https://github.com/rise-policy/RISE-2","data":"https://github.com/rise-policy/RISE-2#-rise-2-a-generalizable-policy-for-learning-from-in-the-wild-demonstrations"},"policy":{"family":"Diffusion","baseModel":"Sparse 3D geometry encoder + frozen DINOv2 semantic encoder (DINOv3 now supported) + spatial aligner + diffusion head","modelSize":"Not verified; parameter counts are shown in a raster evaluation figure rather than extracted text","inputs":"RGB-D observation and robot state; RGB feeds dense semantics, depth feeds the color-free sparse geometry stream","outputs":"Continuous camera-frame action chunks with temporal ensembling","trainingData":"Four-task teleoperated and AirExo-2 in-the-wild demonstrations; count varies with comparison","trainingTime":"60k training steps; elapsed time not reported","trainingHardware":"4 A100 GPUs, batch 240","controlRate":"5 Hz policy inference on RTX 2060 SUPER; downstream control separate","trainingMethod":"Aligned 2D/3D feature fusion and diffusion behavior cloning","evaluation":"Four real tasks, in-domain and object/scene generalization; Flexiv bimanual manipulation"},"release":{"code":"Available","weights":"Not verified","data":"Partial","license":"CC-BY-NC-SA-4.0 (RISE-2 data/code); dependencies retain their own licenses","notes":"Policy training/deployment and sample data released; complete experiment data/checkpoints not verified."},"evidence":[{"url":"https://arxiv.org/abs/2503.03081","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/rise-policy/RISE-2","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://airexo.tech/airexo2/#rise2","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."}],"caveats":["AirExo-2 is the data/hardware system; RISE-2 is the policy described in the same paper.","August 2025 spatial-aligner bug fix and November DINOv3 support change the released implementation from paper settings."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Partial","license":"CC-BY-NC-SA-4.0 (RISE-2 data/code); dependencies retain their own licenses","notes":"Policy training/deployment and sample data released; complete experiment data/checkpoints not verified."}},"relatedIds":[]},{"id":"reactive-diffusion-policy","name":"Reactive Diffusion Policy","title":"Reactive Diffusion Policy: Slow-Fast Visual-Tactile Policy Learning for Contact-Rich Manipulation","date":"2025-03-04","year":2025,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"A slow latent diffusion planner and fast asymmetric tokenizer enable tactile/force corrections during action chunk execution.","institution":"Shanghai Jiao Tong University","venue":"RSS 2025","modalities":["RGB","Tactile","Force/Torque","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2503.02881","project":"https://reactive-diffusion-policy.github.io/","code":"https://github.com/xiaoxiaoxh/reactive_diffusion_policy","data":"https://huggingface.co/datasets/WendiChen/reactive_diffusion_policy_dataset","weights":"https://huggingface.co/WendiChen/reactive_diffusion_policy_model"},"policy":{"family":"Diffusion","baseModel":"Latent Diffusion Policy + asymmetric action tokenizer","modelSize":"Not reported","inputs":"RGB, robot state and tactile/force feedback according to sensor configuration","outputs":"High-level latent chunks and fast low-level actions","trainingData":"Not reported","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Slow planner 1–2 Hz; fast tactile policy >20 Hz; rates depend on sensor configuration","trainingMethod":"Hierarchical slow–fast imitation learning from TactAR demonstrations","evaluation":"Three real contact-rich tasks with multiple tactile/force sensor setups"},"release":{"code":"Available","weights":"Available","data":"Available","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Author repository links dataset/checkpoints, TactAR and training/deployment."},"evidence":[{"url":"https://arxiv.org/abs/2503.02881","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/xiaoxiaoxh/reactive_diffusion_policy","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2503.02881","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."},{"url":"https://github.com/xiaoxiaoxh/reactive_diffusion_policy/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["Optical tactile, force/torque and gripper torque configurations provide different information."],"verified":"2026-10-19","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Available","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Author repository links dataset/checkpoints, TactAR and training/deployment."}},"relatedIds":[]},{"id":"lipvq-vae","name":"LipVQ-VAE / ICRT","title":"Action Tokenizer Matters in In-Context Imitation Learning","date":"2025-03-03","year":2025,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A Lipschitz-constrained action tokenizer improves the smoothness and execution reliability of an in-context robot transformer.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2503.01206","project":"https://action-tokenizer-matters.github.io/","code":"https://github.com/andvg3/LipVQ-VAE"},"policy":{"family":"Autoregressive policy","baseModel":"ICRT policy with a weight-normalized vector-quantized action autoencoder (LipVQ-VAE).","modelSize":"Not reported","inputs":"Current RGB/state observations and demonstration observation/action sequences.","outputs":"Discrete learned action tokens decoded to smooth continuous robot controls.","trainingData":"RoboCasa and ManiSkill demonstration sets; real Kinova drawer experiments include ten human demonstrations augmented using MimicGen.","trainingTime":"RoboCasa policy setting uses 500k iterations; tokenizer/policy stage scopes are separate.","trainingHardware":"NVIDIA RTX 4090 for the reported experiments.","controlRate":"Simulation and real Kinova controller at 50 Hz; this is controller rate, not autoregressive model throughput.","trainingMethod":"Train a Lipschitz-regularized action VQ-VAE and use it in demonstration-conditioned next-token policy learning.","evaluation":"RoboCasa, ManiSkill Pick Cube / Push Cube / Stack Cube, and real drawer opening/closing."},"release":{"code":"Available","weights":"Available","data":"Partial","license":"MIT for repository code; external model, dataset and dependency terms remain separate.","notes":"Official implementation links tokenizer/policy weights; standard benchmark datasets are referenced separately."},"evidence":[{"url":"https://arxiv.org/abs/2503.01206","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://action-tokenizer-matters.github.io/","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/andvg3/LipVQ-VAE","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."},{"url":"https://github.com/andvg3/LipVQ-VAE/blob/HEAD/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Actual license file inspected; code license does not automatically license data or weights."}],"caveats":["Included as an evaluated executable ICRT policy variant, not only an action compression component."],"verified":"2026-10-17"},{"id":"oft","name":"OpenVLA-OFT / OFT+","title":"Fine-Tuning Vision-Language-Action Models: Optimizing Speed and Success","date":"2025-02-27","year":2025,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Optimized fine-tuning replaces slow autoregressive action generation with parallel continuous chunks and enables bimanual control.","institution":"Stanford University","venue":"RSS 2025","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2502.19645","project":"https://openvla-oft.github.io/","code":"https://github.com/moojink/openvla-oft","weights":"https://huggingface.co/moojink"},"policy":{"family":"VLA","baseModel":"OpenVLA-7B + parallel continuous action head; OFT+ adds FiLM language conditioning","modelSize":"~7.5B including visual components; LoRA updates a subset","inputs":"RGB camera views, natural language and proprioception; multiple images supported","outputs":"Continuous action chunks; original LIBERO eight steps and real bimanual joint actions","trainingData":"LIBERO 40 tasks; bimanual ALOHA target-task demonstrations; base inherits OXE pretraining","trainingTime":"Up to 150k steps per LIBERO experiment; full wall-clock varies","trainingHardware":"8 A100/H100 in paper fine-tuning; release allows 1–8 GPUs with 27–80 GB VRAM","controlRate":"Real ALOHA controller 25 Hz; action-generation throughput is higher and is not feedback rate","trainingMethod":"LoRA optimized fine-tuning with L1 regression, parallel decoding and action chunking","evaluation":"Four LIBERO suites and real ALOHA tasks requiring fine-grained bimanual manipulation"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"MIT (code); OpenVLA/Llama-derived model terms also apply; per-checkpoint terms should be checked","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2502.19645","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/moojink/openvla-oft","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://openvla-oft.github.io/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/moojink/openvla-oft/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["OFT and OFT+ are fine-tuning recipes, not a single unchanged generalist checkpoint.","OFT action throughput measures generated actions/s, not camera-conditioned decisions/s."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Not verified","license":"MIT (code); OpenVLA/Llama-derived model terms also apply; per-checkpoint terms should be checked","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"point-policy","name":"Point Policy","title":"Point Policy: Unifying Observations and Actions with Key Points for Robot Manipulation","date":"2025-02-27","year":2025,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"A point-track policy unifies observations and action targets to learn robot manipulation directly from human demonstrations.","institution":"New York University","venue":"Preprint","modalities":["RGB","Depth","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2502.20391","project":"https://point-policy.github.io/","code":"https://github.com/siddhanthaldar/Point-Policy","data":"https://huggingface.co/datasets/siddhanthaldar/Point-Policy"},"policy":{"family":"Transformer policy","baseModel":"DIFT correspondences, modified CoTracker, point-track encoder and transformer policy trunk/action head.","modelSize":"Not reported","inputs":"Ten-step history of object/robot 3D point tracks, obtained using calibrated multi-view triangulation.","outputs":"Predicted robot point tracks converted to end-effector pose and gripper commands.","trainingData":"Human demonstrations for eight real tasks, with task-specific counts such as 20 drawer-opening and 30 bread-placement demonstrations.","trainingTime":"Not reported","trainingHardware":"One NVIDIA RTX A4000 for the reported model training experiments.","controlRate":"End-effector position control commands executed at 6 Hz.","trainingMethod":"Retarget human hand points to robot points and train supervised point-track action prediction.","evaluation":"Eight Franka tasks, with spatial, object-instance and background-clutter generalization."},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"MIT for repository code; external model, dataset and dependency terms remain separate.","notes":"Official code and human demonstration data are public; 3D positions use triangulation rather than monocular inferred depth."},"evidence":[{"url":"https://arxiv.org/abs/2502.20391","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://point-policy.github.io/","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/siddhanthaldar/Point-Policy","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."},{"url":"https://github.com/siddhanthaldar/Point-Policy/blob/HEAD/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Actual license file inspected; code license does not automatically license data or weights."}],"caveats":["Depth/3D geometry comes from multi-view triangulation; it is not a tactile policy."],"verified":"2026-10-17"},{"id":"sitr","name":"SITR","title":"Sensor-Invariant Tactile Representation","date":"2025-02-27","year":2025,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Uses sensor calibration images and simulated optical designs to learn sensor-invariant contact geometry representations.","institution":"University of Illinois Urbana-Champaign","venue":"ICLR 2025","modalities":["Tactile"],"environments":["Simulation","Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2502.19638","project":"https://hgupt3.github.io/sitr/","code":"https://github.com/hgupt3/gsrl","data":"https://huggingface.co/datasets/hgupt3/sitr_dataset","weights":"https://huggingface.co/datasets/hgupt3/sitr_dataset"},"model":{"role":"Reusable representation or perception model; downstream controllers are separate systems","family":"Tactile encoder","baseModel":"Calibration-conditioned Vision Transformer with a normal-map decoder and contrastive embedding head","modelSize":"Not reported","inputs":"Optical tactile image plus 18 calibration images: a 4 mm ball and a cube corner, pressed at nine locations each","outputs":"Sensor-invariant embedding and contact normal map; optional downstream classification or pose heads","trainingMethod":"Joint normal-map reconstruction and supervised cross-sensor contrastive learning in simulated sensors","trainingData":"Physics-based rendered optical tactile sensors with varied geometry, material and illumination; held-out real sensors evaluated after calibration.","trainingTime":"Not reported","trainingHardware":"Not reported","inference":"Not reported","evaluation":"Real-sensor normal/height reconstruction, object classification, pose estimation and sensor-transfer tests."},"release":{"code":"Available","weights":"Available — pretrained checkpoints in the official dataset repository","data":"Available — simulation and real-sensor data","license":"Official code README states MIT; no standalone LICENSE file was found in the inspected code snapshot. The Hugging Face artifact card states MIT.","notes":"The official model/data repository includes simulation/real-sensor data and pretrained perception weights; no robot policy checkpoint is implied."},"evidence":[{"url":"https://arxiv.org/abs/2502.19638","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Method, experiments and implementation sections; specifications retain component and evaluation scope."},{"url":"https://github.com/hgupt3/gsrl","label":"Official SITR implementation","fields":["links","release"],"note":"README and source files inspected."},{"url":"https://huggingface.co/datasets/hgupt3/sitr_dataset","label":"Official SITR dataset and pretrained models","fields":["release"],"note":"Published artifact card and file inventory inspected; card states MIT. Actual code LICENSE was not found despite README claim."}],"caveats":["Normal and reconstructed height maps describe tactile contact geometry; they are not measured scene-depth observations.","Zero-shot transfer still assumes sensor calibration images; it does not mean no target-sensor information."],"relatedIds":["bideta","anytouch","htt"],"verified":"2026-10-17"},{"id":"factr","name":"FACTR","title":"FACTR: Force-Attending Curriculum Training for Contact-Rich Policy Learning","date":"2025-02-24","year":2025,"collections":["policy"],"categories":["tactile-il","tactile-foundation"],"resourceType":"policy","summary":"A curriculum gradually restores visual detail so force-aware imitation policies learn to use joint-torque feedback.","institution":"Carnegie Mellon University","venue":"Preprint","modalities":["RGB","Force/Torque","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2502.17432","project":"https://jasonjzliu.com/factr/","code":"https://github.com/RaindragonD/factr"},"policy":{"family":"ACT","baseModel":"ACT with pretrained visual transformer and MLP torque token.","modelSize":"Not reported","inputs":"RGB and robot joint-torque/proprioceptive feedback.","outputs":"Transformer action chunks for contact-rich arm manipulation.","trainingData":"50 demonstration collection setting; four tasks:box lifting, non-prehensile pivoting, fruit pick-and-place, rolling dough.","trainingTime":"20 k–50 k gradient steps, 2–6 h (appendix typical-policy setting).","trainingHardware":"Single NVIDIA RTX4090.","controlRate":"Not reported","trainingMethod":"Behavior cloning with decreasing visual corruption in pixel/latent space.","evaluation":"Real contact-rich manipulation and unseen object appearance/geometry."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Apache-2.0","notes":"Author-linked repository and actual LICENSE inspected; code, checkpoints anddatasets have independent availability."},"evidence":[{"url":"https://arxiv.org/abs/2502.17432","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://jasonjzliu.com/factr/","label":"Official author project","fields":["links","release"],"note":"Live artifact status reviewed separately from paper claims."},{"url":"https://github.com/RaindragonD/factr","label":"Official released implementation and LICENSE","fields":["links","release"],"note":"Repository metadata,README and actualLICENSE inspected; availability scoped to the listed component."}],"caveats":["Force token is robot joint torque, not a skin tactile array."],"verified":"2026-10-17"},{"id":"functo","name":"FUNCTO","title":"FUNCTO: Function-Centric One-Shot Imitation Learning for Tool Manipulation","date":"2025-02-17","year":2025,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Function, grasp and center keypoints transfer a human-demonstrated tool-use skill across tools with different geometry.","institution":"Southern University of Science and Technology / National University of Singapore","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2502.11744","project":"https://sites.google.com/view/functo"},"policy":{"family":"Geometric transfer","baseModel":"Function-centric keypoint correspondence and planning. Grounding-SAM, CoTracker, functional VLM reasoning, GraspGPT grasp sampling and constrained trajectory optimization; exact VLM backbone is not identified in the inspected manuscript.","modelSize":"Not reported","inputs":"One human tool-use video and RGB-D scene/tool geometry; functional task context, tracked tool keypoints and robot state.","outputs":"Functional keypoint trajectory, feasible end-effector poses and joint-velocity commands.","trainingData":"One human demonstration per skill; no target-task behavioral cloning dataset.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Commands at 10 Hz; interpolated joint-velocity controller runs at 200 Hz.","trainingMethod":"Extract functional keypoints, establish function-centric correspondences and optimize robot motion preserving geometric constraints.","evaluation":"Five real tool functions (pour, cut, scoop, brush, pound), five tool/target tasks per function spanning spatial/instance/category transfer, ten trials/task (250 total per method)."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No author-linked implementation was verified in the inspected primary manuscript/project. This is an evidence gap, not proof that no implementation exists."},"evidence":[{"url":"https://arxiv.org/abs/2502.11744","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://sites.google.com/view/functo","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."}],"caveats":["High-rate inner control is distinct from functional correspondence/planning throughput.","This is a modular geometric/foundation-model system, not an end-to-end video-to-action network."],"verified":"2026-10-17"},{"id":"anytouch","name":"AnyTouch","title":"AnyTouch: Learning Unified Static-Dynamic Representation across Multiple Visuo-tactile Sensors","date":"2025-02-15","year":2025,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Combines static optical tactile images and temporal tactile clips with cross-sensor matching and touch–vision–language alignment.","institution":"Renmin University of China; Wuhan University of Science and Technology; Beijing University of Posts and Telecommunications","venue":"ICLR 2025","modalities":["Tactile"],"environments":["Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2502.12191","project":"https://gewu-lab.github.io/AnyTouch/","code":"https://github.com/GeWu-Lab/AnyTouch","data":"https://huggingface.co/datasets/xxuan01/TacQuad","weights":"https://drive.google.com/file/d/1L4jGUjIHNBMzOiD33Rv0jxWYKHBORD1R/view?usp=sharing"},"model":{"role":"Reusable representation or perception model; downstream controllers are separate systems","family":"Multimodal representation","baseModel":"CLIP ViT-L/14 initialization; multi-sensor tactile transformer with sensor-specific and universal tokens","modelSize":"Not reported","inputs":"Static optical tactile images or 3-frame tactile clips; sensor identity; paired vision/text during training","outputs":"Unified tactile representation for material, hardness, grasp prediction and dynamic perception","trainingMethod":"Masked tactile modeling plus modality-missing-aware alignment and cross-sensor matching","trainingData":"Nine-source mixture including TAG, VisGel, Cloth, TVL, SSVTP, YCB-Slide, ObjectFolder Real, Octopi and TacQuad; TacQuad has 72,606 contact frames across 4 sensors","trainingTime":"20 epochs stage 1 + 12 epochs stage 2; wall-clock not reported","trainingHardware":"4 NVIDIA A800 GPUs","inference":"Not reported","evaluation":"Static property and grasp prediction, unseen-sensor transfer, cross-sensor generation and real-world pouring perception; downstream results are task-specific."},"release":{"code":"Available","weights":"Available — official download link","data":"Available — official resource link","license":"MIT code LICENSE; TacQuad and upstream datasets/checkpoints retain their own terms","notes":"Code, pretrained encoders and downstream task data are separate artifacts; no policy checkpoint is implied by an encoder release."},"evidence":[{"url":"https://arxiv.org/abs/2502.12191","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Method, experiments and implementation sections; specifications retain component and evaluation scope."},{"url":"https://gewu-lab.github.io/AnyTouch/","label":"Official project","fields":["links","release"],"note":"Author project; author-reported release scope is distinguished from an executed reproduction."},{"url":"https://github.com/GeWu-Lab/AnyTouch","label":"Official implementation and current release instructions","fields":["release","links"],"note":"README and repository files inspected on 2026-10-17."},{"url":"https://github.com/GeWu-Lab/AnyTouch/blob/main/LICENSE","label":"Repository license file","fields":["release.license"],"note":"Applies to repository scope; model/data/base-model terms can differ."}],"caveats":["The model produces representations or perceptual predictions, rather than executable robot actions."],"relatedIds":["anytouch2","unitouch","t3"],"verified":"2026-10-17"},{"id":"3d-foresight","name":"3D Foresight","title":"3D Dynamics-Aware Manipulation: Endowing Manipulation Policies with 3D Foresight","date":"2025-02-14","year":2025,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Auxiliary current depth, future RGB-D and 3D-flow learning improves an RGB policy while avoiding sensor depth at rollout.","institution":"Hong Kong University of Science and Technology (Guangzhou); JAKA Robotics","venue":"ICRA 2026","modalities":["RGB","Language","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2502.10028","code":"https://github.com/Stardust-hyx/3D-Foresight"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT for repository code; pretrained models, data and figure rights remain separate","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2502.10028","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://github.com/Stardust-hyx/3D-Foresight","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/Stardust-hyx/3D-Foresight/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/Stardust-hyx/3D-Foresight/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"133 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["Depth labels are measured in CALVIN and estimated in LIBERO/real data; the policy does not require a measured depth stream."],"verified":"2026-10-17","policy":{"family":"World-action model","baseModel":"GR-MG-initialized causal transformer with geometric auxiliary queries and action chunks.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Main/wrist RGB, language and proprioception.","outputs":"Action chunks; depth/future-RGB-D/flow are auxiliary outputs during learning.","trainingMethod":"Cross-embodiment pretraining then task finetuning; auxiliary decoding heads removed or offloaded at inference.","trainingData":"44K trajectories from RH20T, Bridge, Berkeley UR5, Mutex, LIBERO; 60 real demonstrations per task.","trainingTime":"Pretraining: 35 epochs, approximately three days","trainingHardware":"4 NVIDIA RTX 4090 GPUs for pretraining","evaluation":"CALVIN, LIBERO, real JAKA K-1 cup stacking and drawer/tape manipulation.","controlRate":"112 ms policy inference in reported comparison; not a measured closed-loop robot rate."}},{"id":"dexvla","name":"DexVLA","title":"DexVLA: Vision-Language Model with Plug-In Diffusion Expert for General Robot Control","date":"2025-02-09","year":2025,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Combines a pretrained VLM with a large plug-in diffusion expert using an embodiment curriculum.","institution":"Midea Group / Shanghai University","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Dexterous hand","Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2502.05855","project":"https://dex-vla.github.io/","code":"https://github.com/juruobenruo/DexVLA","data":"https://huggingface.co/datasets/lesjie/dexvla_example_data","weights":"https://huggingface.co/lesjie/scale_dp_h"},"policy":{"family":"VLA","baseModel":"Qwen2-VL-2B + ScaleDP diffusion expert up to 1B","modelSize":"2B VLM + up to 1B action expert; exact total depends on expert variant","inputs":"Multi-view RGB, language and robot state","outputs":"Continuous action chunks across single-arm, bimanual and dexterous embodiments","trainingData":"100 h cross-embodiment demonstration pretraining; <100 demos in some novel-embodiment adaptations","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Authors report 60 Hz on one A6000; query-throughput vs servo-frequency scope not fully resolved","trainingMethod":"Separate action-expert pretraining → embodiment alignment → optional task post-training","evaluation":"Real gripper/dexterous/bimanual tasks, drink pouring, packing and long-horizon laundry folding"},"release":{"code":"Available","weights":"Partial","data":"Partial","license":"Official repository LICENSE specifies attribution and noncommercial terms; no versioned standard identifier stated; base models/dependencies have separate terms","notes":"Training/deployment, example data and Stage1 ScaleDP expert weights released; complete final-task checkpoints not verified."},"evidence":[{"url":"https://arxiv.org/abs/2502.05855","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/juruobenruo/DexVLA","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://dex-vla.github.io/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."}],"caveats":["The linked Stage1 expert is not a complete plug-and-play final DexVLA robot policy.","100 h data is demonstration duration, not compute time."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Partial","data":"Partial","license":"Official repository LICENSE specifies attribution and noncommercial terms; no versioned standard identifier stated; base models/dependencies have separate terms","notes":"Training/deployment, example data and Stage1 ScaleDP expert weights released; complete final-task checkpoints not verified."}},"relatedIds":[]},{"id":"reassemble","name":"REASSEMBLE","title":"REASSEMBLE: A Multimodal Dataset for Contact-rich Robotic Assembly and Disassembly","date":"2025-02-07","year":2025,"collections":["benchmark"],"categories":["supporting-resources","precision-assembly"],"resourceType":"dataset","summary":"Physical NIST-board assembly/disassembly data including successful and failed demonstrations with visual, event, force/torque and audio streams.","institution":"TU Wien","venue":"RSS 2025","modalities":["RGB","Force/Torque","Proprioception","Audio","Event camera"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2502.05086","project":"https://tuwien-asl.github.io/REASSEMBLE_page/","code":"https://github.com/TUWIEN-ASL/REASSEMBLE","data":"https://researchdata.tuwien.ac.at/records/0ewrv-8cb44"},"benchmark":{"simulator":"Not applicable — physical dataset","simulatorVersion":"Not applicable","taskCount":"4 action types applied to 17 board objects","tasks":["Pick","Insert","Remove","Place"],"taskListScope":"Complete four action labels; object-conditioned action combinations are additional instances.","capabilities":["Assembly/disassembly","Failure/anomaly analysis","Temporal segmentation","Task inversion"],"metrics":"Action segmentation / anomaly detection / manipulation-policy evaluation depending downstream task.","robots":"Franka Panda / FR3 teleoperation setup around NIST Assembly Task Board 1.","sensors":"Multi-view RGB, event camera, six-axis force/torque, external/wrist microphones, proprioception. No tactile image or metric depth stream claimed.","dataCollection":"Bidirectional haptic teleoperation: Force Dimension omega.6 master device controls the Franka FR3 and returns force feedback.","data":"4,551 demonstrations, including 4,035 successful, over 781 minutes and 17 objects.","protocol":"Synchronized multimodal demonstrations with action and outcome annotations; includes success and execution errors rather than success-only filtering.","baselines":"Policy and temporal-action recognition baselines provided by paper/code; benchmark task-specific."},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"Dataset archive terms and code license require separate inspection","notes":"Official project moved from author page to TU Wien ASL; data archive and code public."},"evidence":[{"url":"https://arxiv.org/abs/2502.05086","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://tuwien-asl.github.io/REASSEMBLE_page/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/TUWIEN-ASL/REASSEMBLE","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://arxiv.org/abs/2502.05086","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Force/torque and event-camera signals are distinct from tactile images and depth; do not label as RGBDT."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not verified","data":"Available","license":"Dataset archive terms and code license require separate inspection","notes":"Official project moved from author page to TU Wien ASL; data archive and code public."}},"relatedIds":[]},{"id":"spatialvla","name":"SpatialVLA","title":"SpatialVLA: Exploring Spatial Representations for Visual-Language-Action Model","date":"2025-01-27","year":2025,"collections":["policy"],"categories":["rgbd-3d","generalist-foundation"],"resourceType":"policy","summary":"Uses inferred 3 D position encoding and adaptive spatial action tokens to add geometric structure to a generalist VLA.","institution":"Shanghai AI Laboratory","venue":"RSS 2025","modalities":["RGB","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2501.15830","project":"https://spatialvla.github.io/","code":"https://github.com/SpatialVLA/SpatialVLA","weights":"https://huggingface.co/IPEC-COMMUNITY/spatialvla-4b-224-pt"},"policy":{"family":"VLA","baseModel":"PaliGemma2-3B + frozen ZoeDepth + Ego3D position encoding","modelSize":"3.5B total reported; includes frozen depth component","inputs":"RGB image and language; depth is inferred by ZoeDepth, not measured by a depth camera","outputs":"Autoregressive spatial translation, rotation and gripper tokens","trainingData":"1.1M robot episodes from OXE and RH20T; downstream real demonstrations","trainingTime":"10 days pretraining","trainingHardware":"64 A100 GPUs; fine-tuning settings differ","controlRate":"Paper reports ~20 Hz on one RTX 4090; action-token throughput should be distinguished from physical execution","trainingMethod":"Autoregressive behavior cloning with adaptive 3D action-grid fine-tuning","evaluation":"SimplerEnv, LIBERO and 16 real tasks across several robot setups"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Not verified (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2501.15830","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/SpatialVLA/SpatialVLA","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://spatialvla.github.io/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."}],"caveats":["RGB-only deployment; its geometric features do not demonstrate a measured-depth ablation.","Reported 1.1M episodes are dataset mixture scale, not precision-contact demonstration count."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Not verified","license":"Not verified (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"pifast","name":"π0-FAST / FAST","title":"FAST: Efficient Action Tokenization for Vision-Language-Action Models","date":"2025-01-16","year":2025,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Uses frequency-space action compression to train an autoregressive VLA on high-frequency continuous robot trajectories.","institution":"Physical Intelligence / UC Berkeley / Stanford University","venue":"RSS 2025","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2501.09747","project":"https://www.pi.website/research/fast","code":"https://github.com/Physical-Intelligence/openpi","weights":"https://github.com/Physical-Intelligence/openpi#base-models"},"policy":{"family":"VLA","baseModel":"PaliGemma3B autoregressive VLA + DCT/BPE FAST action tokenizer","modelSize":"3B-class PaliGemma policy; FAST tokenizer is a separate compact processor","inputs":"RGB views, language and robot state as configured per embodiment","outputs":"Discrete tokens decoded to 1-second continuous action chunks; DROID 15-step chunks with 8 or 15 executed","trainingData":"Policy scale 10k h robot data; universal FAST+ tokenizer separately trained on 1M action sequences","trainingTime":"DROID experiment 240k steps / 3 epochs / ~4 days; full 10k h pretraining cost not reported","trainingHardware":"DROID experiment 8 H100 GPUs","controlRate":"Task actions 20/50 Hz; tokenizer packs 1 s; does not establish 20/50 fresh model queries/s","trainingMethod":"Autoregressive behavior cloning with DCT coefficient quantization/BPE rather than per-step scalar bins","evaluation":"Six real robot environments and LIBERO; DROID zero-shot, table bussing, folding and bagging"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 (openpi code); model/tokenizer and dataset terms separately apply","notes":"π0-FAST checkpoints and FAST+ tokenizer released; full proprietary policy training data not released."},"evidence":[{"url":"https://arxiv.org/abs/2501.09747","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/Physical-Intelligence/openpi","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://www.pi.website/research/fast","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."}],"caveats":["FAST+ is an action tokenizer, not itself a robot policy; this record includes the associated π0-FAST policy.","Up to 5 × faster training is a reported relative result, not an inference-frequency claim."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 (openpi code); model/tokenizer and dataset terms separately apply","notes":"π0-FAST checkpoints and FAST+ tokenizer released; full proprietary policy training data not released."}},"relatedIds":[]},{"id":"fuse","name":"FuSe","title":"Beyond Sight: Finetuning Generalist Robot Policies with Heterogeneous Sensors via Language Grounding","date":"2025-01-08","year":2025,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Grounds heterogeneous sensor modalities in language to adapt pretrained generalist policies using scarce multimodal robot data.","institution":"UC Berkeley","venue":"ICRA 2025","modalities":["RGB","Tactile","Audio","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2501.04693","project":"https://fuse-model.github.io/","code":"https://github.com/fuse-model/FuSe","data":"https://huggingface.co/datasets/oier-mees/FuSe"},"policy":{"family":"VLA","baseModel":"Octo diffusion generalist for main experiments; separate PaliGemma3B VLA variant","modelSize":"3B VLA variant; Octo configuration size not verified","inputs":"RGB, DIGIT tactile images, audio, language and proprioception","outputs":"Delta end-effector robot actions and optional language descriptions","trainingData":"27K multimodal robot trajectories across three real-world tasks; Octo initialized from OXE; separate VLA initialization uses an OXE checkpoint after50k pretraining steps","trainingTime":"50,000 multimodal training steps; separate VLA OXE initialization checkpoint at 50,000 pretraining steps; wall-clock not reported","trainingHardware":"v5e-128 TPU pod; batch 1024 (paper implementation details)","controlRate":"Robot action commands5 Hz","trainingMethod":"Action learning plus multimodal contrastive and language-generation objectives","evaluation":"Object grasping, shopping-bag retrieval and button/sound cross-modal tasks"},"release":{"code":"Available","weights":"Available","data":"Available","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Official project labels linked Hub resources as dataset and models."},"evidence":[{"url":"https://arxiv.org/abs/2501.04693","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/fuse-model/FuSe","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2501.04693","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."},{"url":"https://arxiv.org/html/2501.04693v3","label":"Independent review: dataset overview, SectionIII implementation details and SectionIV-G VLA variant","fields":["policy.baseModel","policy.trainingData","policy.trainingTime","policy.trainingHardware"],"note":"Main Octo and additional PaliGemma variants are distinct backbones; the multimodal finetuning schedule is separate from the VLA OXE initialization stage."},{"url":"https://github.com/fuse-model/FuSe/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["Includes both diffusion generalist and VLA instantiations; their scale and training differ."],"verified":"2026-10-19","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Available","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Official project labels linked Hub resources as dataset and models."}},"relatedIds":[]},{"id":"mt3","name":"MT3","title":"Learning a Thousand Tasks in a Day","date":"2025","year":2025,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Multi-Task Trajectory Transfer combines language/geometry retrieval with separate alignment and interaction phases for few-demonstration learning.","institution":"Imperial College London","venue":"Science Robotics 2025; earlier ICLR 2025 workshop version","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2511.10110","project":"https://www.robot-learning.uk/learning-1000-tasks","code":"https://github.com/kamil-dreczkowski/learning_thousand_tasks","data":"https://zenodo.org/records/17334511"},"policy":{"family":"Geometric transfer","baseModel":"Multi-task retrieval and trajectory transfer. Language embeddings, object point-cloud geometry retrieval and pose-based alignment; MT-ACT+ is a separately studied BC comparison.","modelSize":"Not reported","inputs":"Language task description, segmented calibrated RGB-D object point cloud and stored end-effector/gripper trajectories.","outputs":"Alignment commands and transferred interaction trajectories for the selected task.","trainingData":"One or few demonstrations per task; large-scale experiment teaches 1,000 everyday tasks. Dataset-diversity studies vary objects and demonstrations.","trainingTime":"Under 24 hours refers to human demonstration collection for 1,000 tasks, not GPU training wall time.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Decompose trajectories into alignment/interaction and retrieve by language then geometry; no per-task policy-gradient update is needed.","evaluation":"3,450 real rollouts studying design choices, plus 2,200 large-scale rollouts for task retention, novel objects and skill chaining."},"release":{"code":"Available","weights":"Not verified","data":"Partial","license":"MIT code; dependency/model/data terms separate","notes":"Author-linked repository and actual README/tree inspected; weights/data are tracked separately. No installation or reproduction attempted."},"evidence":[{"url":"https://arxiv.org/abs/2511.10110","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://www.robot-learning.uk/learning-1000-tasks","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://github.com/kamil-dreczkowski/learning_thousand_tasks","label":"Official code","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://raw.githubusercontent.com/kamil-dreczkowski/learning_thousand_tasks/main/LICENSE","label":"Actual source license file","fields":["release.license"],"note":"Scope limited to the file/repository subtree carrying these terms, not paper figures or external models/data."},{"url":"https://openreview.net/pdf?id=5e48yYecV5","label":"Earlier original ICLR 2025 workshop manuscript","fields":["date","venue"],"note":"Same MT3 title, authors, method and 3,450-rollout study are present in the ICLR 2025 workshop manuscript. Exact first-public day could not be verified. Date uses year-only precision; later Science Robotics date is November 12, 2025 and arXiv v1 November 13."}],"caveats":["Task count concerns the paper’s taught everyday task definitions; it should not be equated with 1,000 independent benchmark categories.","BC model parameter sweeps belong to comparison architectures, not necessarily to retrieval-based MT3.","The arXiv author-version notice allows personal use and does not grant blanket redistribution; AAAS/paper figure rights remain separate from the MIT code license.","An ICLR 2025 workshop manuscript predates the journal/arXiv version; exact first-public day is unverified, so the displayed date has year-only precision. Journal publication: November 12, 2025; arXiv v1: November 13, 2025."],"verified":"2026-10-17"},{"id":"sgpt","name":"ST-GPT / SGPT","title":"Enabling Long(er) Horizon Imitation for Manipulation Tasks by Modeling Subgoal Transitions","date":"2025","year":2025,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Learns explicit or continuous subgoal transitions so a long-horizon imitation policy advances through a demonstrated task without hand-tuned switch thresholds.","institution":"Indian Institute of Technology Delhi","venue":"CoRL 2025","modalities":["Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://proceedings.mlr.press/v305/jain25b.html","project":"https://shivam89jain.github.io/lhi/","code":"https://github.com/shivam89jain/SGPT-long-horizon-imitation","data":"https://github.com/shivam89jain/Franka-LHT-Simulator","weights":"https://huggingface.co/shivam89jain/Trained-Checkpoints-SGPT"},"policy":{"family":"Hierarchical imitation","baseModel":"ST-GPT causal transformer with action / transition heads; SGPT encoder–decoder transformer with cross-attention over all subgoals.","modelSize":"Approximately 25M parameters for the transformer policies; the 3M MLP is a baseline.","inputs":"Low-dimensional current state and a subgoal-state sequence; ST-GPT additionally uses a bounded state history and current subgoal.","outputs":"Joint-space action predictions; ST-GPT predicts a binary transition signal while SGPT transitions implicitly through subgoal cross-attention.","trainingData":"Franka Kitchen: 25 tasks with ten demonstrations each. FrankaLHT: 180 trajectories across eighteen tasks. Real setup: fourteen demonstrations per task across six tasks; training / evaluation are split within each task.","trainingTime":"SGPT: 20.1 hours on Franka Kitchen, 115.8 hours on FrankaLHT and 23.4 hours on the real-world dataset; transformer runs use 1,000 epochs.","trainingHardware":"One NVIDIA A40 GPU for all reported policy training.","controlRate":"FrankaLHT collection / execution uses 50 Hz.","trainingMethod":"Offline supervised action learning with transition labels for ST-GPT or implicit smooth subgoal conditioning for SGPT; no language-model prompting or deployment weight updates are required.","evaluation":"25 Franka Kitchen tasks, eighteen FrankaLHT tasks and six real Panda tasks, with in-distribution and out-of-distribution evaluation."},"release":{"code":"Available","weights":"Partial","data":"Partial","license":"Not verified","notes":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact. The Hub contains separate Franka Kitchen and Franka PyBullet .pth checkpoints; physical robot checkpoints are not established by this endpoint. This model card declares mit. The author simulator repository documents 180 recorded FrankaLHT trajectories; physical demonstrations are not claimed released."},"evidence":[{"url":"https://proceedings.mlr.press/v305/jain25b.html","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/shivam89jain/SGPT-long-horizon-imitation/main/Readme.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."},{"url":"https://shivam89jain.github.io/lhi/","label":"Official project links / artifacts","fields":["links","release"],"note":"Author-linked repository / model / dataset endpoints are audited individually; a link alone does not establish a permissive asset license."},{"url":"https://huggingface.co/shivam89jain/Trained-Checkpoints-SGPT","label":"Author-linked Hugging Face model files / card","fields":["links.weights","release.weights","release.notes"],"note":"The Hub contains separate Franka Kitchen and Franka PyBullet .pth checkpoints; physical robot checkpoints are not established by this endpoint. Card license: mit."},{"url":"https://github.com/shivam89jain/SGPT-long-horizon-imitation","label":"SGPT official README artifact scope","fields":["release","links.data"],"note":"Readme.md links the FrankaLHT simulator / recorded trajectories and separate trained-checkpoint Hub."}],"caveats":["This is a state-conditioned hierarchical imitation policy; the paper does not establish raw RGB-D or tactile input.","Training hours are dataset / architecture specific, not one combined universal-policy cost."],"verified":"2026-10-17"},{"id":"vtdexmanip","name":"VTDexManip","title":"VTDexManip: A Dataset and Benchmark for Visual-tactile Pretraining and Dexterous Manipulation with Reinforcement Learning","date":"2025","year":2025,"collections":["benchmark","policy"],"categories":["tactile-benchmarks","dexterous-benchmarks","tactile-il"],"resourceType":"policy","summary":"Combines human visual-tactile representation pretraining with a six-task dexterous RL benchmark and sim-to-real control.","institution":"Zhejiang University","venue":"ICLR 2025","modalities":["RGB","Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand","Bimanual"],"links":{"paper":"https://proceedings.iclr.cc/paper_files/paper/2025/file/e19b6f65791e350347bcff8a3955cb5b-Paper-Conference.pdf","project":"https://lqts.github.io/VTDexManip/","code":"https://github.com/LQTS/VTDexManip","data":"https://1drv.ms/f/c/9054151f0ba654c9/EslUpgsfFVQggJB7AQAAAAABSp31p8Fft1mtHyOwJNqmoA"},"policy":{"family":"RL","baseModel":"Joint masked visual–tactile Transformer encoder plus PPO MLP [1024,1024,512]","modelSize":"Not reported","inputs":"Egocentric RGB, 20 binary tactile contacts and hand joint positions/velocities","outputs":"Shadow Hand joint actions; handover directly outputs both hands","trainingData":"2,032 human sequences, 565k paired frames, 182 objects, 10 collection tasks; downstream simulation RL","trainingTime":"Representation250 epochs≈25 h; RL2k/3k updates≈14/21 h for pretrained vision+touch","trainingHardware":"Representation: 2 NVIDIA3090 GPUs; policy RL: 1 NVIDIA3090","controlRate":"Not reported","trainingMethod":"Masked visual/tactile pretraining; frozen encoder PPO; domain randomization/DAgger distillation for real deployment","evaluation":"Six simulation tasks; five single-hand real tasks shown"},"release":{"code":"Available","weights":"Available","data":"Available","license":"MIT for benchmark repository; dataset/model terms separate","notes":"Benchmark, pretraining code and pretrained representation models linked; dataset download password documented as vtdexmanip."},"evidence":[{"url":"https://proceedings.iclr.cc/paper_files/paper/2025/file/e19b6f65791e350347bcff8a3955cb5b-Paper-Conference.pdf","label":"Official ICLR2025 full paper","fields":["benchmark","policy","date"],"note":"§4/5 andAppendixB/C; year is official conference year, no arXiv v1 was identified."},{"url":"https://github.com/LQTS/VTDexManip","label":"Official implementation","fields":["release","benchmark.tasks","benchmark.simulatorVersion"],"note":"Training/evaluation, pretraining model anddataset links; MIT license inspected."},{"url":"https://proceedings.iclr.cc/paper_files/paper/2025/file/e19b6f65791e350347bcff8a3955cb5b-Paper-Conference.pdf","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Tactile is sparse binary contact from20 regions, not optical tactile imagery.","Human collection ten task families and robot benchmark six tasks are separate counts."],"verified":"2026-10-17","benchmark":{"simulator":"NVIDIA Isaac Gym","simulatorVersion":"Exact preview build not reported;official code uses Python3.8,PyTorch2.0.1,CUDA11.4.","taskCount":"Six robot tasks;ten human dataset task families are separate.","tasks":["BottleCap Turning","Faucet Screwing","Lever Sliding","Table Reorientation","In-hand Reorientation","Bimanual Hand-over"],"taskListScope":"Complete six-task simulation benchmark.","capabilities":["Dexterous articulation","Object rotation","In-hand reorientation","Bimanual throwing/catching","Visual–tactile pretraining transfer"],"metrics":"Task success on seen/unseen objects;viewpoint,tactile-noise andthreshold robustness.","robots":"24-DoF Shadow Hand;two hands forhandover;real Shadow Hand plusAzure Kinect.","sensors":"EgoRGB;20 regional binary contacts from simulated force sensors or realpiezoresistive sensors;proprioception;not optical tactile images.","dataCollection":"Human manipulation videos synchronized with a pressure-sensor glove provide pretraining data; downstream robot policies collect RL interaction in simulation. Human capture is not robot teleoperation.","data":"Human dataset2,032 sequences/565k frames/182 objects/10 tasks;simulation200 parallel environments,task-specificseen/unseen object splits.","protocol":"18 pretrained/non-pretrained representations under matchedPPO;frozen pretrained encoders;simulation seen/unseen andreal qualitative transfer.","baselines":"VT-JointPretrain,V/T separatepretrain;CLIP,R3M,MVP,Voltron,ResNet18with/without tactile;untrainedV/T/V+T/proprioception."},"releaseByCollection":{"benchmark":{"code":"Available","weights":"Available","data":"Available","license":"MIT for benchmark repository; dataset/model terms separate","notes":"Benchmark, pretraining code and pretrained representation models linked; dataset download password documented as vtdexmanip."},"policy":{"code":"Available","weights":"Available","data":"Available","license":"MIT for benchmark repository; dataset/model terms separate","notes":"Benchmark, pretraining code and pretrained representation models linked; dataset download password documented as vtdexmanip."}},"relatedIds":[],"benchmarkSummary":"Six dexterous robot tasks evaluate visual-tactile representations under PPO, with seen/unseen objects and real-world transfer examples.","policySummary":"Combines human visual-tactile representation pretraining with a six-task dexterous RL benchmark and sim-to-real control."},{"id":"regent","name":"REGENT","title":"REGENT: A Retrieval-Augmented Generalist Agent That Can Act In-Context in New Environments","date":"2024-12-06","year":2024,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A retrieval-augmented transformer acts in new environments by combining demonstration neighbors with learned action prediction.","institution":"University of Pennsylvania","venue":"ICLR 2025","modalities":["Proprioception"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2412.04759","project":"https://kaustubhsridhar.github.io/regent-research","code":"https://github.com/regent-research/regent","data":"https://huggingface.co/datasets/regent-research/regent-subset-of-jat-dataset-tokenized","weights":"https://huggingface.co/regent-research/regent-medium-embeddings"},"policy":{"family":"Retrieval policy","baseModel":"Transformer policy combined with retrieval-and-play action interpolation.","modelSize":"138.6M parameters for REGENT.","inputs":"Current observations, prior reward and retrieved state/action examples; simulated arm tasks use vector observations.","outputs":"Continuous arm actions in manipulation domains; the general agent also supports discrete game actions.","trainingData":"14.5M transitions: 100k each from 45 Meta-World, nine MuJoCo, 52 Atari and 39 BabyAI environments.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Pretrain on query/retrieved-neighbor sequences; adapt to new environments by adding demonstrations to retrieval context without gradient updates.","evaluation":"Held-out Meta-World manipulation environments and MuJoCo, alongside game/navigation environments."},"release":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 for repository code; external model, dataset and dependency terms remain separate.","notes":"Official REGENT checkpoint and pretraining subset are public. Game-domain image inputs are not evidence of visual arm-policy evaluation."},"evidence":[{"url":"https://arxiv.org/abs/2412.04759","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://kaustubhsridhar.github.io/regent-research","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/regent-research/regent","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."},{"url":"https://github.com/regent-research/regent/blob/HEAD/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Actual license file inspected; code license does not automatically license data or weights."}],"caveats":["Manipulation experiments are simulated state-based control.","The corpus includes games and locomotion; total transition count is not a manipulation-only data scale."],"verified":"2026-10-17"},{"id":"g3flow","name":"G3Flow","title":"G3Flow: Generative 3D Semantic Flow for Pose-aware and Generalizable Object Manipulation","date":"2024-11-27","year":2024,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Generated object twins and pose tracking provide a persistent 3D semantic flow to a diffusion controller.","institution":"HKU; Tencent Robotics X; Shenzhen University; collaborating institutions","venue":"arXiv preprint","modalities":["RGB","Depth","Point cloud","Proprioception"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2411.18369","project":"https://tianxingchen.github.io/G3Flow/","code":"https://github.com/TianxingChen/G3Flow"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT for repository code; pretrained models, data and figure rights remain separate","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2411.18369","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://tianxingchen.github.io/G3Flow/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/TianxingChen/G3Flow","label":"Official current implementation linked by author project","fields":["release"],"note":"Actual runnable release and model artifacts checked independently."},{"url":"https://github.com/TianxingChen/G3Flow/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/TianxingChen/G3Flow/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"917 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["Digital-twin creation and tracking prerequisites can fail; do not treat a foundation-model twin as ground-truth object geometry."],"verified":"2026-10-17","policy":{"family":"Diffusion","baseModel":"3D generative digital twins, visual semantic features, object pose tracking and diffusion policy.","modelSize":"Not reported in the inspected primary-source sections","inputs":"RGB-D observations, object segmentation/poses and proprioception.","outputs":"Object-semantic-flow-conditioned robot actions.","trainingMethod":"Foundation-model twin initialization and task demonstration diffusion learning.","trainingData":"Five simulated manipulation tasks with fixed-pose and cross-object settings.","trainingTime":"3,000 epochs per task; wall-clock not reported","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Pose-aware terminal constraints and unseen-object manipulation, with object occlusion analysis.","controlRate":"34.04 Hz model decision-frequency profiling on RTX 4090; excludes initial digital-twin generation."}},{"id":"malmm","name":"MALMM","title":"MALMM: Multi-Agent Large Language Models for Zero-Shot Robotics Manipulation","date":"2024-11-26","year":2024,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Coordinates separate planner, coder and supervisor agents to generate manipulation programs and recover from failures using observations after each step.","institution":"Mohamed bin Zayed University of Artificial Intelligence","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2411.17636"},"policy":{"family":"Robot agent","baseModel":"GPT-4-turbo-2024-04-09 for the three agents; Llama-3.3-70B is a separate open-model experiment.","modelSize":"70B for the Llama variant; hosted GPT-4-Turbo size is not disclosed.","inputs":"Task, current scene / robot observations and generated program outcomes. RGB-D, LangSAM and M2T2 produce 3D grasp / placement geometry in the visual setting.","outputs":"High-level plans, executable low-level code and 3D waypoints executed through a local trajectory planner.","trainingData":"No agent fine-tuning or task-demonstration ICL data. Prompts initially developed on Stack Blocks are reused on other tasks.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen LLMs; role-separated multi-agent planning / coding / supervision and observation-based replanning.","evaluation":"Nine RLBench tasks, task-horizon variations and physical Franka Panda tasks using a RealSense D435i."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The original paper is public; code, model and dataset release scopes are verified separately where established. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms."},"evidence":[{"url":"https://arxiv.org/abs/2411.17636","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."}],"caveats":["Privileged simulator observations and vision-grounded observations are distinct evaluation settings.","No in-context task demonstrations does not mean no manually developed system prompt."],"verified":"2026-10-17"},{"id":"foar","name":"FoAR","title":"FoAR: Force-Aware Reactive Policy for Contact-Rich Robotic Manipulation","date":"2024-11-24","year":2024,"collections":["policy"],"categories":["tactile-il","rgbd-3d"],"resourceType":"policy","summary":"A contact predictor controls RGB-D/force fusion and reactive position correction in a diffusion manipulation policy.","institution":"Shanghai Jiao Tong University","venue":"IEEE RA-L 2025","modalities":["RGB","Depth","Point cloud","Force/Torque","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2411.15753","project":"https://tonyfang.net/FoAR/","code":"https://github.com/Alan-Heoooh/FoAR","data":"https://drive.google.com/drive/folders/1Tq7wR9MdLLFr5AZTqXvypF2_2CJ-ghd5"},"policy":{"family":"Diffusion","baseModel":"RISE sparse point-cloud encoder and CNN diffusion action head, with force-history transformer and contact predictor.","modelSize":"Not reported.","inputs":"RealSense D435 RGB-D colored point cloud, RGB for the contact predictor, 200 past wrist six-axis F/T samples at 100 Hz and current TCP state for reactive correction.","outputs":"20-step end-effector action chunks with gripper commands; predicted contact probability gates fusion and position adjustment.","trainingMethod":"Diffusion imitation loss plus contact classification labels derived from thresholded demonstration forces within a surrounding temporal window; deployment uses reactive position control.","trainingData":"50 expert demonstrations for wiping and peeling, 40 for chopping. Wiping (General) is an orientation variant; do not multiply the main task counts by four.","trainingTime":"Not reported.","trainingHardware":"2 NVIDIA A100 GPUs; batch size 240. RTX 3090 workstation is for collection/evaluation, not the reported training setup.","controlRate":"F/T history sampled at 100 Hz; a distinct achieved action/inference frequency is Not reported.","evaluation":"Flexiv Rizon / Dahuan AG-95 with flange-mounted OptoForce F/T and D435; wiping, arbitrary-orientation wiping, peeling and chopping. Twenty wiping/peeling trials and ten chopping trials; ASR and quality metrics are distinct."},"release":{"code":"Available","weights":"Not verified","data":"Partial","license":"Not reported in inspected repository; no root LICENSE file.","notes":"Training/deployment code verified. Public Drive folder lists peel and wipe sample data; no chopping/full-corpus release or pretrained weights verified."},"evidence":[{"url":"https://arxiv.org/abs/2411.15753","label":"Original paper and reviewed full text","fields":["title","date","institution","summary","policy","caveats"],"note":"Full primary text, including available supplementary implementation details; first public date retained separately from reviewed revision."},{"url":"https://tonyfang.net/FoAR/","label":"Official author project","fields":["links","release"],"note":"Current project links and release claims checked independently of paper prose."},{"url":"https://github.com/Alan-Heoooh/FoAR","label":"Official implementation and repository files","fields":["release","links"],"note":"Training/deployment implementation, README and actual LICENSE inspected where present."},{"url":"https://drive.google.com/drive/folders/1Tq7wR9MdLLFr5AZTqXvypF2_2CJ-ghd5","label":"Official public data link and visible file inventory","fields":["links.data","release.data"],"note":"Accessible public sample/example assets verified; completeness against full paper corpus not established."}],"caveats":["Wrist F/T is distinct from fingertip tactile skin.","The future-contact predictor uses future logged force only to form training labels; runtime sees current/history observations.","Wiping quality and normalized peeled length are not binary full-task success rates."],"verified":"2026-10-17"},{"id":"speed-adjustable-tactile","name":"Speed-Adjustable Tactile Manipulation","title":"Learning Time-Optimal and Speed-Adjustable Tactile In-Hand Manipulation","date":"2024-11-20","year":2024,"collections":["policy"],"categories":["tactile-foundation","visual-precision-rl"],"resourceType":"policy","summary":"Speed-conditioned estimator-coupled RL accelerates tactile goal reorientation while preserving force closure.","institution":"German Aerospace Center (DLR)","venue":"Preprint","modalities":["Force/Torque","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2411.13148"},"policy":{"family":"RL","baseModel":"PPO policy and learned object pose estimator; speed-conditioned training objective.","modelSize":"Not reported","inputs":"Joint position/control error history, estimated object pose, goal and speed target.","outputs":"Hand joint targets filtered for impedance execution.","trainingData":"GPU-parallel Isaac simulation with domain randomization.","trainingTime":"Under 24 h reported for configurable-speed cuboid agent.","trainingHardware":"Single NVIDIA T4.","controlRate":"20 Hz policy/estimator;1000 Hz impedance controller.","trainingMethod":"Estimator-coupled RL and time-optimal/speed-adjustable objectives.","evaluation":"DLR Hand II cuboid reorientation at configurable speeds."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2411.13148","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."}],"caveats":["Torque-derived contact feedback isnot a fingertip cutaneous sensor."],"verified":"2026-10-17"},{"id":"instant-policy","name":"Instant Policy","title":"Instant Policy: In-Context Imitation Learning via Graph Diffusion","date":"2024-11-19","year":2024,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"A graph-based diffusion model adapts manipulation behavior from one or two demonstration trajectories using segmented point-cloud geometry.","institution":"Not reported","venue":"ICLR 2025","modalities":["Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2411.12633","project":"https://www.robot-learning.uk/instant-policy","code":"https://github.com/vv19/instant_policy"},"policy":{"family":"Diffusion","baseModel":"Graph diffusion network with a pretrained local-geometry encoder; no language backbone.","modelSize":"Scaling study: 69M, 117M and 178M trainable parameters, with the geometry encoder treated separately.","inputs":"One or two demonstrations, current end-effector / gripper state and segmented object point clouds downsampled to 2,048 points in the end-effector frame.","outputs":"Denoised SE(3) end-effector actions and binary gripper commands.","trainingData":"Continuously generated simulated pseudo-demonstrations, including geometric motion and common trajectory templates; no target-task demonstration training is required.","trainingTime":"2.5 million optimization steps, approximately five days, followed by a 50,000-step learning-rate cooldown.","trainingHardware":"One NVIDIA GeForce RTX 3080 Ti for the reported training run.","controlRate":"Demonstrations are recorded at 25 Hz in simulation and 10 Hz in the real world; these are recording rates rather than an asserted neural inference rate.","trainingMethod":"Offline graph-diffusion training on synthetic pseudo-demonstrations; target demonstrations condition inference without weight updates.","evaluation":"24 unseen RLBench tasks and 16 real-world manipulation tasks."},"release":{"code":"Partial — deployment code and compiled model","weights":"Not verified","data":"Not verified","license":"PolyForm Noncommercial 1.0.0","notes":"Repository provides deployment utilities and instant_policy.so/pyi; this is not a complete source/training release and has noncommercial terms."},"evidence":[{"url":"https://arxiv.org/abs/2411.12633","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/vv19/instant_policy","label":"Official implementation, README and file/license inspection on 19 October 2026","fields":["links","release"],"note":"Repository provides deployment utilities and instant_policy.so/pyi; this is not a complete source/training release and has noncommercial terms. PolyForm Noncommercial 1.0.0."}],"caveats":["Point clouds contain segmented objects of interest; robots, tables and distractors are excluded by the observation pipeline.","Real-world segmentation failures are a documented limitation.","RLBench simulation uses ground-truth object segmentation; real-world segmentation is a separate perception procedure."],"verified":"2026-10-19"},{"id":"maniskill-vitac-2025","name":"ManiSkill-ViTac 2025","title":"ManiSkill-ViTac 2025: Challenge on Manipulation Skill Learning With Vision and Tactile Sensing","date":"2024-11-19","year":2024,"collections":["benchmark"],"categories":["tactile-benchmarks","precision-assembly"],"resourceType":"benchmark","summary":"Challenge combining tactile-only transfer, tactile-plus-depth fusion and tactile-sensor structure design with physical finalist evaluation.","institution":"Tsinghua University","venue":"ICRA 2025 workshop challenge","modalities":["Depth","Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2411.12503","project":"https://ai-workshops.github.io/maniskill-vitac-challenge-2025/","code":"https://github.com/cyliizyz/ManiSkill-ViTac2025"},"benchmark":{"simulator":"SAPIEN / IPC-based high-fidelity tactile simulation (challenge-specific platform)","simulatorVersion":"Official README: install SAPIEN 3.0.0b1 before SapienIPC.","taskCount":"3 tracks; tactile-only track has 2 tasks; depth-fusion track has 1 peg-selection/insertion task; sensor-design track evaluates a modified sensor.","tasks":["Peg insertion","Lock opening","Tactile-vision-fusion peg insertion"],"taskListScope":"Complete manipulation task families; tracks and object-shape variants are not separate tasks.","capabilities":["Unseen peg/key shape generalization","Tactile insertion","Depth-based hole selection and approach","Sensor morphology design"],"metrics":"Track 1: 50×SR(peg)+50×SR(lock); Track 2: 100×SR; Track 3: 50×SR+50×subjective score. Physical stage determines final ranking for manipulation tracks.","robots":"Matched simulated and physical parallel-gripper manipulation platform.","sensors":"GelSight Mini marker-based tactile sensing; Track 2 adds RealSense D415 depth with semantic segmentation.","dataCollection":"Task-specific simulator interaction supports skill learning; a uniform demonstration collector or teleoperation interface is Not reported in the challenge overview.","data":"Simulation training environment and baseline resources; standardized full demonstration corpus count Not reported.","protocol":"Sim evaluation followed by physical checkpoint submission for advancing teams. Held-out peg/key shapes; Track 2 increases reach distance and requires selecting matching hole.","baselines":"Provided TD3 tactile policy training / pretrained challenge baseline checkpoints."},"release":{"code":"Available","weights":"Available","data":"Not reported","license":"Apache-2.0 for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Official repository and cyliizyz/ManiSkill-ViTac2025_ckpt checkpoint link verified."},"evidence":[{"url":"https://arxiv.org/abs/2411.12503","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://ai-workshops.github.io/maniskill-vitac-challenge-2025/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/cyliizyz/ManiSkill-ViTac2025","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://github.com/cyliizyz/ManiSkill-ViTac2025","label":"Official source","fields":["release","benchmark.simulatorVersion"],"note":"Official challenge page linked cyliizyz repository, not the earlier provisional guessed owner."},{"url":"https://arxiv.org/abs/2411.12503","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."},{"url":"https://github.com/chuanyune/ManiSkill-ViTac2025/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["Input permissions differ by track; depth is permitted in Track 2 but not tactile-only Track 1.","The 2026 edition changes to real-world bimanual tasks and should not be merged into the same task count."],"verified":"2026-10-19","releaseByCollection":{"benchmark":{"code":"Available","weights":"Available","data":"Not reported","license":"Apache-2.0 for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Official repository and cyliizyz/ManiSkill-ViTac2025_ckpt checkpoint link verified."}},"relatedIds":[]},{"id":"magic-contact","name":"MAGIC","title":"One-Shot Manipulation Strategy Learning by Making Contact Analogies","date":"2024-11-14","year":2024,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Global visual shape cues and local contact geometry transfer manipulation strategies by analogy from one demonstration.","institution":"Massachusetts Institute of Technology and collaborators","venue":"ICRA 2025","modalities":["RGB","Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2411.09627","project":"https://magic-2024.github.io/","code":"https://github.com/nature21/magic"},"policy":{"family":"Geometric transfer","baseModel":"Contact-analogy one-shot strategy transfer. DINOv2 global shape features, local curvature/contact-pair matching and simulation/planning.","modelSize":"Not reported","inputs":"Reference demonstration/contact points, source/target tool and target geometry; real scenes captured with two calibrated RealSense D435 RGB-D cameras.","outputs":"Transferred contact pairs and feasible tool/object manipulation trajectory.","trainingData":"One reference demonstration per strategy with pretrained visual features; no tool-specific action-policy training corpus.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Rank contact analogies using global/local geometry and evaluate strategy feasibility.","evaluation":"Simulated hooking/scooping/hanging and tool-use comparisons; real Franka Panda Hanging and Hooking."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT code; dependency/model/data terms separate","notes":"Author-linked repository and actual README/tree inspected; weights/data are tracked separately. No installation or reproduction attempted."},"evidence":[{"url":"https://arxiv.org/abs/2411.09627","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://magic-2024.github.io/","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://github.com/nature21/magic","label":"Official code","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://raw.githubusercontent.com/nature21/magic/main/LICENSE.txt","label":"Actual source license file","fields":["release.license"],"note":"Scope limited to the file/repository subtree carrying these terms, not paper figures or external models/data."}],"caveats":["MAGIC here means Manipulation Analogies for Generalizable Intelligent Contacts; do not merge with unrelated MAGIC acronyms.","Contact points are geometric strategy representations, not tactile-sensor observations."],"verified":"2026-10-17"},{"id":"compliant-hand-visuotactile-insertion","name":"Compliant-Hand Visuotactile Insertion","title":"Visuotactile-Based Learning for Insertion with Compliant Hands","date":"2024-11-10","year":2024,"collections":["policy"],"categories":["tactile-foundation","visual-precision-rl","rgbd-3d"],"resourceType":"policy","summary":"A privileged simulation teacher transfers multimodal insertion to an underactuated compliant hand using touch and depth point clouds.","institution":"Tel Aviv University / Rutgers University","venue":"Preprint","modalities":["Depth","Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Underactuated hand"],"links":{"paper":"https://arxiv.org/abs/2411.06408","project":"https://osheraz.github.io/visuotactile/","code":"https://github.com/osheraz/IsaacGymInsertion"},"policy":{"family":"RL","baseModel":"RL teacher and visuotactile student with AllSight tactile encoders and scene-pointcloud perception.","modelSize":"Not reported","inputs":"Runtime AllSight tactile images, depth-backprojected object point clouds, end-effector pose and last action.","outputs":"Robot end-effector motions for compliant grasped insertion.","trainingData":"4096 teacher/32 rendered student parallel training environments;11 real test objects, 6 novel.","trainingTime":"Teacher~24 h, student~48 h;~1e8 and 0.7e7 states respectively.","trainingHardware":"Single RTX4090training; RTX3060local deployment.","controlRate":"Tactile/image 30 Hz; Tracking SAM segmentation 10 Hz; kinematics 100 Hz.","trainingMethod":"Privileged teacher RL, visuotactile student distillation and zero-shot sim-to-real transfer.","evaluation":"Kinova Gen3/OpenHand Model O with 3 AllSight fingertips;20 trials/object/policy."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"BSD-3-Clause (NVIDIA-inherited repository LICENSE; third-party terms may differ)","notes":"Author-linked repository and actual LICENSE inspected; code, checkpoints anddatasets have independent availability."},"evidence":[{"url":"https://arxiv.org/abs/2411.06408","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://osheraz.github.io/visuotactile/","label":"Official author project","fields":["links","release"],"note":"Live project artifact links inspected on2026-10-17."},{"url":"https://github.com/osheraz/IsaacGymInsertion","label":"Official released implementation and LICENSE","fields":["links","release"],"note":"Repository metadata,README and actualLICENSE.txt inspected; availability scoped to the listed component."}],"caveats":["Scene RGB used forone-frame interactive object mask annotation/tracking; policy geometric observations are depth point clouds.","Teacher privileged object state is not available to deployed student."],"verified":"2026-10-17"},{"id":"tacex","name":"TacEx","title":"TacEx: GelSight Tactile Simulation in Isaac Sim — Combining Soft-Body and Visuotactile Simulators","date":"2024-11-07","year":2024,"collections":["benchmark"],"categories":["supporting-resources"],"resourceType":"simulator","summary":"Modular Isaac Sim/Lab tactile extension combining optical rendering, marker motion and IPC/FEM soft-body mechanics.","institution":"Technical University of Darmstadt","venue":"Preprint","modalities":["Tactile","Proprioception"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper","Robot arm tactile fingertip"],"links":{"paper":"https://arxiv.org/abs/2411.04776","project":"https://github.com/DH-Ng/TacEx","code":"https://github.com/DH-Ng/TacEx"},"benchmark":{"simulator":"Isaac Sim / Isaac Lab plus UIPC","simulatorVersion":"Current public README: Isaac Sim 4.5 / Isaac Lab 2.1.1; installation tested on Ubuntu 22.04, RTX 4090, CUDA 12.4.","taskCount":"Sensor simulation and example interactions, not a fixed policy benchmark task count","tasks":["Ball rolling (simulation performance example)"],"taskListScope":"Example only; not a standardized task suite.","capabilities":["Tactile image rendering","Marker motion","FEM/IPC contact","Modular sensor simulation"],"metrics":"Sensor simulation fidelity and performance; example-specific policy results.","robots":"Sensor-mounted robots / fingertips in examples.","sensors":"Current main release supports GelSight Mini; Taxim optical rendering, FOTS marker motion and UIPC-based FEM marker simulation.","dataCollection":"Task-dependent simulation examples and sensor/calibration workflows; no common benchmark-wide demonstration collection method is prescribed.","data":"Sensor assets and calibration/examples; no standardized manipulation demonstration dataset.","protocol":"Select interchangeable optical/marker/soft-body components; measure fidelity/performance under example interactions.","baselines":"Taxim/FOTS/UIPC components rather than a common learned-policy leaderboard."},"release":{"code":"Available","weights":"Not applicable","data":"Partial","license":"Not verified","notes":"Beta/preview framework; sensor support and modified forks used by UniVTAC/OpenViTac differ from upstream."},"evidence":[{"url":"https://arxiv.org/abs/2411.04776","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://github.com/DH-Ng/TacEx","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/DH-Ng/TacEx","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://arxiv.org/abs/2411.04776","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Upstream supports GelSight Mini; additional sensors in downstream projects should not be attributed to unmodified TacEx."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not applicable","data":"Partial","license":"Not verified","notes":"Beta/preview framework; sensor support and modified forks used by UniVTAC/OpenViTac differ from upstream."}},"relatedIds":[]},{"id":"touch-to-touch-translation","name":"Touch-to-Touch Translation / touch2touch","title":"Touch-to-Touch Translation -- Learning the Mapping Between Heterogeneous Tactile Sensing Technologies","date":"2024-11-04","year":2024,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Maps opticalDIGIT observations to capacitiveCySkin responses using paired contacts across sensing technologies.","institution":"University of Genoa; Oxford Robotics Institute, University of Oxford","venue":"Preprint","modalities":["Tactile"],"environments":["Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2411.02187"},"model":{"role":"Reusable representation or perception model; downstream controllers are separate systems","family":"Generative tactile model","baseModel":"Adapted pix2pix image-to-image model compared with ResNet-18 regression","modelSize":"Not reported","inputs":"DIGIT optical tactile image","outputs":"Predicted CySkin capacitive taxel-response vector or map","trainingMethod":"Supervised paired-sensor translation with adversarial/reconstruction losses, or direct regression","trainingData":"Matched physical stimuli across DIGIT and CySkin; trained on local tactile features and tested on previously unseen larger objects.","trainingTime":"Not reported","trainingHardware":"Not reported","inference":"Not reported","evaluation":"Cross-technology contact-shape and signal-magnitude translation; no autonomous manipulation-policy evaluation."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Paper retains its stated arXiv rights; official code, model and dataset release terms not verified.","notes":"Code, pretrained encoders and downstream task data are separate artifacts; no policy checkpoint is implied by an encoder release."},"evidence":[{"url":"https://arxiv.org/abs/2411.02187","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Method, experiments and implementation sections; specifications retain component and evaluation scope."}],"caveats":["Distinct from Touch2Touch using GelSlim and Soft Bubble; similar names do not establish the same method.","The manuscript says initially submitted to IROS 2023. That statement does not establish conference acceptance or an earlier publicly accessible paper."],"relatedIds":["cttp","unitac-nv"],"verified":"2026-10-17"},{"id":"3d-vitac","name":"3D-ViTac","title":"3D-ViTac: Learning Fine-Grained Manipulation with Visuo-Tactile Sensing","date":"2024-10-31","year":2024,"collections":["policy"],"categories":["tactile-il","rgbd-3d"],"resourceType":"policy","summary":"Unifies depth points and spatially located pressure taxels in 3D for fine-grained bimanual diffusion imitation learning.","institution":"UIUC","venue":"CoRL 2024","modalities":["Depth","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2410.24091","project":"https://binghao-huang.github.io/3D-ViTac/","code":"https://github.com/binghao-huang/3d-vitac-training-code"},"policy":{"family":"Diffusion","baseModel":"3D visuo-tactile point encoder + Diffusion Policy","modelSize":"Not reported","inputs":"Depth XYZ point clouds, pressure values/positions and joint state","outputs":"Bimanual action trajectories","trainingData":"Task-specific teleoperated bimanual demonstrations; total not reported","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Synchronized demonstrations 10 Hz; tactile acquisition node 30 Hz","trainingMethod":"Unified spatial representation and denoising behavior cloning","evaluation":"Fragile-object manipulation, in-hand adjustment and contact-rich bimanual tasks"},"release":{"code":"Partial","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Dedicated repository currently contains a redirect readme to VT-Refine pretraining; cleaned real-robot implementation is described as forthcoming."},"evidence":[{"url":"https://arxiv.org/abs/2410.24091","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/binghao-huang/3d-vitac-training-code","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2410.24091","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Primary policy uses geometric XYZ and pressure; recording RGBD does not imply RGB colors are policy inputs.","256 taxels per finger are pressure arrays, not GelSight imagery."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Partial","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Dedicated repository currently contains a redirect readme to VT-Refine pretraining; cleaned real-robot implementation is described as forthcoming."}},"relatedIds":[]},{"id":"sparsh","name":"Sparsh / TacBench","title":"Sparsh: Self-supervised touch representations for vision-based tactile sensing","date":"2024-10-31","year":2024,"collections":["benchmark","policy"],"categories":["tactile-benchmarks","supporting-resources","tactile-representation"],"resourceType":"model","summary":"Self-supervised optical-touch encoders plus TacBench, a six-task tactile evaluation suite with one closed-loop bead-maze control task.","institution":"FAIR at Meta","venue":"CoRL 2024","modalities":["Tactile"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Robot arm tactile fingertip"],"links":{"paper":"https://arxiv.org/abs/2410.24090","project":"https://sparsh-ssl.github.io/","code":"https://github.com/facebookresearch/sparsh","data":"https://huggingface.co/collections/facebook/sparsh-67167ce57566196a4526c328","weights":"https://huggingface.co/collections/facebook/sparsh-67167ce57566196a4526c328"},"benchmark":{"simulator":"Not applicable — recorded/physical touch evaluation","simulatorVersion":"Not applicable","taskCount":"6 touch-centric tasks; only bead-maze is a closed-loop manipulation-policy task","tasks":["Force estimation","Slip detection","Relative pose estimation","Grasp stability prediction","Textile classification","Bead maze manipulation"],"taskListScope":"Complete six-task TacBench suite.","capabilities":["Force perception","Incipient slip","Local pose","Grasp stability","Material recognition","Contact-guided control"],"metrics":"Task-specific regression, classification and policy-success metrics; results under limited labeled-data budgets.","robots":"Task-specific physical sensor/robot setups rather than one common manipulation embodiment.","sensors":"Vision-based tactile sensors including DIGIT and GelSight variants, with task-specific datasets.","dataCollection":"TacBench bead maze: a mixture of VR and manual kinesthetic teleoperation. Offline tactile probes use separate instrumented-contact/pose-label capture setups.","data":"TacBench labeled datasets released separately from the 460K+ image self-supervised tactile corpus used by Sparsh.","protocol":"Frozen representation + lightweight downstream heads for sensing probes; label-budget comparisons; bead-maze policy evaluation separated from offline predictions.","baselines":"Sparsh MAE, DINO, I-JEPA, V-JEPA variants; task/sensor-specific end-to-end baselines."},"release":{"code":"Available","weights":"Available","data":"Available","license":"CC BY-NC 4.0 repository LICENSE.md; model/dataset cards and upstream corpora have separate terms.","notes":"Public repository and Hugging Face collection include representation models and benchmark data."},"evidence":[{"url":"https://arxiv.org/abs/2410.24090","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://sparsh-ssl.github.io/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/facebookresearch/sparsh","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://arxiv.org/html/2410.24090#S3","label":"Original Sparsh representation and training sections","fields":["model"],"note":"460K+ original pretraining corpus; Appendix C specifies eight A100 80GB GPUs. Separate from TacBench labels."},{"url":"https://github.com/facebookresearch/sparsh/blob/main/LICENSE.md","label":"Repository license","fields":["release.license"],"note":"CC BY-NC 4.0 license file verified."},{"url":"https://arxiv.org/abs/2410.24090","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Do not describe six tasks as six manipulation environments.","TacBench is distinct from the 2026 Tac-Bench 14-task simulation suite."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Available","data":"Available","license":"Code/model/data terms differ; inspect official repository and dataset cards","notes":"Public repository and Hugging Face collection include representation models and benchmark data."},"policy":{"code":"Available","weights":"Available","data":"Available","license":"CC BY-NC 4.0 repository LICENSE.md; model/dataset cards and upstream corpora have separate terms.","notes":"Public repository and Hugging Face collection include representation models and benchmark data."}},"relatedIds":["sparsh-x","sparsh-skin","t3","anytouch2"],"policyName":"Sparsh","benchmarkName":"TacBench","model":{"role":"Reusable optical tactile encoder; the TacBench benchmark role is retained separately","family":"Tactile encoder","baseModel":"ViT backbones trained with MAE, DINO, DINOv2, I-JEPA or V-JEPA adapted to tactile images","modelSize":"ViT-Base is the primary configuration (width 768); ViT-Small variants also evaluated. Total parameter count not reported.","inputs":"Optical tactile images or short temporal context, depending on the SSL variant, from DIGIT/GelSight-family sensors","outputs":"Latent touch features for lightweight perception heads or a separately trained manipulation policy","trainingMethod":"Self-supervised masked reconstruction, self-distillation or latent prediction; frozen encoder with attentive downstream probing","trainingData":"460K+ tactile images in the original paper: DIGIT Touch-Slide/YCB-Slide and GelSight Touch and Go/ObjectFolder Real. Current release preprocessing can differ.","trainingTime":"Not reported — objective-specific schedules appear in Appendix C","trainingHardware":"8 NVIDIA A100 80GB GPUs in paper Appendix C","inference":"Not reported — depends on encoder variant and downstream head","evaluation":"TacBench force, slip, relative pose, grasp stability, textile classification and bead-maze control; six tasks are not six closed-loop robot environments."}},{"id":"pi0","name":"π0","title":"π0: A Vision-Language-Action Flow Model for General Robot Control","date":"2024-10-31","year":2024,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Generalist VLA couples a pretrained VLM to a flow-matching action expert for high-frequency dexterous manipulation.","institution":"Physical Intelligence","venue":"RSS 2025","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2410.24164","project":"https://www.pi.website/blog/pi0","code":"https://github.com/Physical-Intelligence/openpi","weights":"https://github.com/Physical-Intelligence/openpi#base-models"},"policy":{"family":"VLA","baseModel":"PaliGemma 3B + 300M flow-matching action expert","modelSize":"3.3B total: 3B PaliGemma + 300M action expert","inputs":"Multiple RGB views, language instruction and robot proprioception","outputs":"50-step continuous action chunks; joint/gripper and mobile actions padded across embodiments","trainingData":"10k+ hours robot data, seven robot configurations / 68 tasks, plus OXE subset","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Actions executed up to 50 Hz; chunk inference is a separate rate","trainingMethod":"Cross-embodiment flow-matching pretraining and task fine-tuning","evaluation":"Real folding, table clearing, stacking and other single/bimanual/mobile manipulation tasks"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 (openpi code); per-checkpoint model/data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2410.24164","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/Physical-Intelligence/openpi","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://www.pi.website/blog/pi0","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/Physical-Intelligence/openpi/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["Open checkpoints and full proprietary pretraining data have different availability.","50 Hz action execution does not imply 50 new image-conditioned inference calls per second."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Not verified","license":"Apache-2.0 (openpi code); per-checkpoint model/data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"kalm","name":"KALM","title":"Keypoint Abstraction using Large Models for Object-Relative Imitation Learning","date":"2024-10-30","year":2024,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Foundation-model-assisted keypoint discovery grounds a diffusion policy in object-relative coordinates from a small demonstration set.","institution":"MIT / National Taiwan University","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2410.23254","project":"https://kalm-il.github.io/","code":"https://github.com/FANG-Xiaolin/KALM"},"policy":{"family":"Diffusion","baseModel":"GPT-4o/SAM keypoint proposal, DINO/FeatUp and FPFH correspondence, followed by keypoint-conditioned diffusion actions.","modelSize":"Not reported","inputs":"Task description, demonstration-derived keypoints, current RGB/3D scene and robot state.","outputs":"Robot trajectory represented relative to discovered object keypoints.","trainingData":"Typically 5–10 robot demonstrations per task; no manual keypoint labels required.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Distill task-relevant consistent keypoints, then train an object-relative imitation policy.","evaluation":"Real manipulation across object pose, camera pose and object-instance changes."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT for repository code; external model, dataset and dependency terms remain separate.","notes":"Author implementation is public; hosted foundation models and downstream policy checkpoints have separate release scopes."},"evidence":[{"url":"https://arxiv.org/abs/2410.23254","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://kalm-il.github.io/","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/FANG-Xiaolin/KALM","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."},{"url":"https://github.com/FANG-Xiaolin/KALM/blob/HEAD/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Actual license file inspected; code license does not automatically license data or weights."}],"caveats":["Large models discover / track task keypoints; they are not the low-level diffusion action model."],"verified":"2026-10-17"},{"id":"hilserl","name":"HIL-SERL","title":"Precise and Dexterous Robotic Manipulation via Human-in-the-Loop Reinforcement Learning","date":"2024-10-29","year":2024,"collections":["policy"],"categories":["visual-precision-rl"],"resourceType":"policy","summary":"Combines real-world off-policy RL with human interventions for precise assembly, dynamic manipulation and dual-arm skills.","institution":"UC Berkeley","venue":"Science Robotics 2025","modalities":["RGB","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2410.21845","project":"https://hil-serl.github.io/","code":"https://github.com/rail-berkeley/hil-serl"},"policy":{"family":"RL","baseModel":"ResNet-10 visual encoder + sample-efficient off-policy actor-critic; task-dependent gripper/controller design","modelSize":"Not reported","inputs":"RGB views and proprioception; no tactile sensor required in original main settings","outputs":"Continuous Cartesian setpoints/wrenches and discrete gripper decisions, task-dependent","trainingData":"20 initial demos per task plus online interventions and autonomous rollouts","trainingTime":"1–2.5 h real-world training per task","trainingHardware":"Single RTX 4090 workstation","controlRate":"10 Hz policy setpoints; 1,000 Hz low-level impedance controller","trainingMethod":"RLPD-style learning with human corrections and learned binary reward classifiers","evaluation":"Real precision assembly, dashboard assembly, insertion, dynamic and bimanual tasks; typically 100 evaluation trials/task"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Apache-2.0 (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2410.21845","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/rail-berkeley/hil-serl","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://hil-serl.github.io/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/rail-berkeley/hil-serl/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["Human interventions are additional training data and cannot be equated to the initial 20 demonstrations alone.","Precision depends on both the learned policy and the impedance/controller design."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Apache-2.0 (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"gendp","name":"GenDP","title":"GenDP: 3D Semantic Fields for Category-Level Generalizable Diffusion Policy","date":"2024-10-23","year":2024,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Builds 3D semantic fields from measured RGB-D and foundation-vision descriptors for category-level manipulation transfer.","institution":"Columbia University","venue":"CoRL 2024","modalities":["RGB","Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2410.17488","project":"https://robopil.github.io/GenDP/","code":"https://github.com/WangYixuan12/gendp","data":"https://github.com/WangYixuan12/gendp#-download-dataset","weights":"https://github.com/WangYixuan12/gendp#-infer-in-simulation"},"policy":{"family":"Diffusion","baseModel":"D3Fields/DINOv2 descriptor field + PointNet++ + diffusion policy","modelSize":"Not reported","inputs":"Calibrated multi-view RGB-D and robot/gripper geometry; selected reference semantic descriptors","outputs":"10D single-arm or 20D bimanual pose/gripper action chunks: XYZ, 6D rotation and gripper","trainingData":"Task-specific multi-object demonstrations; released simulation/real data includes mug hanging and pencil insertion","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Diffusion behavior cloning on explicit3D semantic fields","evaluation":"Simulation and real category-transfer tasks including mug hanging, pencil insertion, shoe alignment and tool picking"},"release":{"code":"Available","weights":"Available","data":"Available","license":"MIT (official repository); pretrained vision/backbone and datasets have separate terms","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2410.17488","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/WangYixuan12/gendp","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://robopil.github.io/GenDP/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."}],"caveats":["Reference descriptors are manually selected; foundation features alone are not a task instruction interface.","Real deployment requires camera/world/robot calibration; main paper does not disclose a unified training compute budget."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Available","license":"MIT (official repository); pretrained vision/backbone and datasets have separate terms","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"visk","name":"ViSk","title":"Learning Precise, Contact-Rich Manipulation through Uncalibrated Tactile Skins","date":"2024-10-22","year":2024,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"Adds inexpensive uncalibrated magnetic skin tokens to a visual Transformer policy for precision contact manipulation.","institution":"New York University","venue":"ICRA 2025","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2410.17246","project":"https://visuoskin.github.io/","code":"https://github.com/raunaqbhirangi/visuoskin"},"policy":{"family":"Other","baseModel":"BAKU transformer behavior cloning with AnySkin tactile token","modelSize":"Not reported","inputs":"Multi-view RGB, AnySkin magnetometer measurements and robot state","outputs":"Robot manipulation actions","trainingData":"<200 demonstrations per task; four real tasks","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"RGB30 Hz; AnySkin100 Hz recording; deployment feedback rate not separately reported","trainingMethod":"End-to-end visuotactile behavior cloning without tactile-specific calibration","evaluation":"Credit card swiping, plug insertion, USB insertion, bookshelf retrieval"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Project links policy code; data/weights scope not independently verified."},"evidence":[{"url":"https://arxiv.org/abs/2410.17246","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/raunaqbhirangi/visuoskin","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2410.17246","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."},{"url":"https://github.com/raunaqbhirangi/visuoskin/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["Low-dimensional magnetic skin and optical tactile image baselines differ in sensor properties, not only network design."],"verified":"2026-10-19","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Project links policy code; data/weights scope not independently verified."}},"relatedIds":[]},{"id":"roboprompt","name":"RoboPrompt","title":"In-Context Learning Enables Robot Action Prediction in LLMs","date":"2024-10-16","year":2024,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Uses object-pose descriptions and demonstrated end-effector keyframes to predict robot action sequences with a frozen LLM.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2410.12782","project":"https://davidyyd.github.io/roboprompt","code":"https://github.com/davidyyd/roboprompt"},"policy":{"family":"Robot agent","baseModel":"GPT-4 Turbo.","modelSize":"Not reported","inputs":"Task, estimated object poses from an initial RGB-D observation and ten in-context demonstration examples represented as textual poses / actions.","outputs":"End-effector keyframe poses and gripper actions decoded for robot execution.","trainingData":"In-context demonstrations; no LLM parameter fine-tuning for the target tasks.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen language model; object-centric pose encoding, action discretization and demonstration prompting.","evaluation":"RLBench simulation and physical manipulation experiments."},"release":{"code":"Available","weights":"Not applicable","data":"Not verified","license":"MIT original code, verified from LICENSE; vendored PyRep/RLBench/PerAct and external models retain separate terms.","notes":"Actual in-context demonstration construction, LLM action prediction, RLBench evaluation and installation source are public. Demonstration/train/test episodes are generated separately with RLBench; no new model weights are needed for the frozen-LLM method and a complete pregenerated episode corpus was not verified."},"evidence":[{"url":"https://arxiv.org/abs/2410.12782","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/davidyyd/roboprompt","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["Simulation uses ground-truth object-center information; real deployment estimates object poses.","The model-facing pose description is not a raw RGB-only observation interface."],"verified":"2026-10-19"},{"id":"cttp","name":"CTTP / Contrastive Touch-to-Touch Pretraining","title":"Contrastive Touch-to-Touch Pretraining","date":"2024-10-15","year":2024,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Aligns tactile signals from GelSlim and SoftBubble in a shared space, transferring pose/classification heads between sensors.","institution":"University of Michigan","venue":"Preprint","modalities":["Tactile"],"environments":["Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2410.11834","project":"https://www.mmintlab.com/research/cttp/"},"model":{"role":"Reusable representation or perception model; downstream controllers are separate systems","family":"Tactile encoder","baseModel":"ResNet-50 encoder with a two-layer MLP into a 64-dimensional contrastive space","modelSize":"64-dimensional contrastive projection; total network parameters not reported","inputs":"Paired GelSlim and SoftBubble tactile signals during pretraining; one sensor at inference","outputs":"Shared tactile embeddings; optional tool identity or in-hand pose estimates","trainingMethod":"Cross-sensor contrastive positives from matched object/grasp configurations; supervised downstream decoders","trainingData":"Matched tactile observations of tools grasped by two heterogeneous sensor systems; seen/unseen grasp and tool splits","trainingTime":"100 epochs; wall-clock duration not reported","trainingHardware":"Not reported","inference":"Not reported","evaluation":"Tool classification and in-hand pose estimation; transferred features also evaluated in an insertion experiment."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Paper copyright applies; code/model/data release license not verified","notes":"Code, pretrained encoders and downstream task data are separate artifacts; no policy checkpoint is implied by an encoder release."},"evidence":[{"url":"https://arxiv.org/abs/2410.11834","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Method, experiments and implementation sections; specifications retain component and evaluation scope."},{"url":"https://www.mmintlab.com/research/cttp/","label":"Official project","fields":["links","release"],"note":"Author project; author-reported release scope is distinguished from an executed reproduction."}],"caveats":["The project URL is identified by the original paper, but the site returned an access error during this audit; no unverified code/weights link was invented.","Closed-loop insertion uses an additional downstream system; the reusable contrastive encoder is the recorded model."],"relatedIds":["tactile-functasets","t3"],"verified":"2026-10-17"},{"id":"idp3","name":"Improved 3D Diffusion Policy (iDP3)","title":"Generalizable Humanoid Manipulation with 3D Diffusion Policies","date":"2024-10-14","year":2024,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Uses egocentric point-cloud representations to deploy humanoid manipulation across previously unseen scenes without external camera calibration.","institution":"Stanford University","venue":"IROS 2025","modalities":["Depth","Proprioception"],"environments":["Real world"],"embodiments":["Humanoid upper body","Bimanual","Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2410.10803","project":"https://humanoid-manipulation.github.io/","code":"https://github.com/YanjieZe/Improved-3D-Diffusion-Policy","data":"https://github.com/YanjieZe/Improved-3D-Diffusion-Policy#news","weights":"https://github.com/YanjieZe/Improved-3D-Diffusion-Policy#news"},"policy":{"family":"Diffusion","baseModel":"Pyramid 3D convolutional encoder + diffusion U-Net","modelSize":"Not reported","inputs":"Egocentric measured-depth point clouds and robot state; camera-frame coordinates","outputs":"Humanoid upper-body arm, hand, head and waist action sequences","trainingData":"Three real tasks; paper compares one/two demonstrations, each containing 20 successful rounds","trainingTime":"Not reported","trainingHardware":"Single NVIDIA RTX 4090 24 GB; deployment on Fourier GR1 onboard CPU (official README)","controlRate":"Approximately 15 Hz onboard policy inference; low-level servo rate not reported","trainingMethod":"Behavior cloning; 300 epochs, 50 training/10 DDIM inference steps","evaluation":"Fourier GR1 tasks in varied unseen offices, meeting rooms and kitchens; 2,253 reported real evaluation episodes"},"release":{"code":"Available","weights":"Available","data":"Available","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2410.10803","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/YanjieZe/Improved-3D-Diffusion-Policy","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://humanoid-manipulation.github.io/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/YanjieZe/Improved-3D-Diffusion-Policy/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["Task demonstrations are repeated rounds, not equivalent to one independent short trajectory.","Released deployment is a robot-specific example; camera-frame geometry removes external calibration but does not remove sensor calibration."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Available","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"forcemimic","name":"ForceMimic / HybridIL","title":"ForceMimic: Force-Centric Imitation Learning with Force-Motion Capture System for Contact-Rich Manipulation","date":"2024-10-10","year":2024,"collections":["policy"],"categories":["tactile-il","rgbd-3d"],"resourceType":"policy","summary":"Robot-free wrench-motion demonstrations train a point-cloud diffusion policy that outputs force-position trajectories for hybrid control.","institution":"Shanghai Jiao Tong University","venue":"ICRA 2025","modalities":["RGB","Depth","Point cloud","Proprioception","Force/Torque"],"environments":["Real world"],"embodiments":["Robot arm with task-specific tool","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2410.07554","project":"https://forcemimic.github.io/","code":"https://github.com/ForceMimic/hybridil","data":"https://drive.google.com/drive/folders/1sKqOj9tO-luddM8XIaJvTFbRSEZet7C3","hardware":"https://github.com/ForceMimic/forcecapture"},"policy":{"family":"Diffusion","baseModel":"Modified Diffusion Policy with MLP point-cloud encoder and hybrid force-position controller.","modelSize":"Not reported.","inputs":"Learned policy: L515 RGB-D-derived colored point clouds and TCP pose history; official eval.py assembles XYZRGB points. Measured wrench is not selected in the provided HybridIL model config; the hybrid controller uses separate F/T feedback.","outputs":"20-step TCP pose and wrench targets; predicted force selects IK position or orthogonal hybrid force-position primitives.","trainingMethod":"Imitate robot-free ForceCapture data after gravity compensation and pose/frame conversion; diffusion learns pose-wrench targets.","trainingData":"15 zucchinis, 438 peeling segments and 30,199 action sequences; these are distinct levels of the same collection. Sensor recordings aligned to 30 Hz RGB-D observations.","trainingTime":"500 epochs; wall-clock time Not reported.","trainingHardware":"Not reported.","controlRate":"Collection aligned to 30 Hz; deployment policy/controller rates Not reported.","evaluation":"Flexiv Rizon 4; right fixed peeler controlled by HybridIL, left arm stabilizes zucchini using rules. One learned peeling skill; HybridIL/vision baseline each 20 actions, force-input baselines each 10. Motion correctness and peel length >10 cm are separate criteria."},"release":{"code":"Available","weights":"Not verified","data":"Partial","license":"MIT (actual HybridIL and ForceCapture LICENSE files).","notes":"Policy and ForceCapture code verified. Public example-data folder contains zucchini_shave.hdf5, conversion script and README; full-corpus coverage and pretrained policy weights not established."},"evidence":[{"url":"https://arxiv.org/abs/2410.07554","label":"Original paper and reviewed full text","fields":["title","date","institution","summary","policy","caveats"],"note":"Full primary text, including available supplementary implementation details; first public date retained separately from reviewed revision."},{"url":"https://forcemimic.github.io/","label":"Official author project","fields":["links","release"],"note":"Current project links and release claims checked independently of paper prose."},{"url":"https://github.com/ForceMimic/hybridil","label":"Official implementation and repository files","fields":["release","links"],"note":"Training/deployment implementation, README and actual LICENSE inspected where present."},{"url":"https://drive.google.com/drive/folders/1sKqOj9tO-luddM8XIaJvTFbRSEZet7C3","label":"Official public data link and visible file inventory","fields":["links.data","release.data"],"note":"Accessible public sample/example assets verified; completeness against full paper corpus not established."},{"url":"https://github.com/ForceMimic/hybridil/blob/main/eval.py","label":"Released observation assembly and training config","fields":["modalities","policy.inputs","caveats"],"note":"eval.py builds XYZRGB points and transmits wrench; train_dp_ftout.json selects only pointcloud and TCP pose keys, not wrench. Released config gives 1000 epochs versus paper 500."}],"caveats":["Force is a prediction target and low-level feedback signal, not a deployed learned-policy observation; force-input baselines perform worse in this paper.","Bimanual hardware does not imply a learned two-arm policy; the left arm stabilizes by rules.","The reported evidence covers one peeling skill and a controlled start configuration.","Paper reports 500 training epochs; the current released train_dp_ftout config instead specifies 1000 epochs. This is an artifact-versus-paper difference, not a revised paper measurement."],"verified":"2026-10-17"},{"id":"rdt","name":"RDT-1B","title":"RDT-1B: a Diffusion Foundation Model for Bimanual Manipulation","date":"2024-10-10","year":2024,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Large diffusion Transformer unifies heterogeneous action spaces for language-conditioned bimanual manipulation.","institution":"Tsinghua University","venue":"ICLR 2025","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2410.07864","project":"https://rdt-robotics.github.io/rdt-robotics/","code":"https://github.com/thu-ml/RoboticsDiffusionTransformer","data":"https://huggingface.co/datasets/robotics-diffusion-transformer/rdt-ft-data","weights":"https://huggingface.co/robotics-diffusion-transformer/rdt-1b"},"policy":{"family":"Diffusion","baseModel":"T5 language + SigLIP vision + 1.2B Robotics Diffusion Transformer","modelSize":"1.2B diffusion policy; external encoders have additional parameters","inputs":"Multi-view RGB, language and proprioception; unified action/state padding","outputs":"64-step continuous bimanual action chunks","trainingData":"46 public robot datasets plus >6k self-collected bimanual trajectories","trainingTime":"~1 month pretraining, 1M steps; ~3 days fine-tuning, 130k steps","trainingHardware":"48 H100 80 GB GPUs for pretraining/fine-tuning","controlRate":"6 action chunks/s or 381 generated actions/s on RTX 4090; physical action/control frequency separate","trainingMethod":"Multirobot diffusion pretraining followed by bimanual fine-tuning; DPM-Solver inference","evaluation":"Real bimanual manipulation with novel objects/scenes; downstream fine-tuning experiments"},"release":{"code":"Available","weights":"Available","data":"Available","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2410.07864","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/thu-ml/RoboticsDiffusionTransformer","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://rdt-robotics.github.io/rdt-robotics/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/thu-ml/RoboticsDiffusionTransformer/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["Despite RGB-D dataset provenance, released main visual policy uses RGB image encoders.","381 actions/s is chunk-generation throughput, not closed-loop sensing frequency."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Available","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"gembench-3d-lotus","name":"GemBench / 3D-LOTUS++","title":"Towards Generalizable Vision-Language Robotic Manipulation: A Benchmark and LLM-guided 3D Policy","date":"2024-10-02","year":2024,"collections":["benchmark","policy"],"categories":["rgbd-3d","general-manipulation","robustness-evaluation"],"resourceType":"policy","summary":"A calibrated 3D point-transformer policy is combined with language planning and open-vocabulary object grounding for task generalization.","institution":"Inria; École normale supérieure; CNRS; PSL Research University","venue":"ICRA 2025","modalities":["RGB","Depth","Point cloud","Proprioception"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2410.01345","project":"https://www.di.ens.fr/willow/research/gembench/","code":"https://github.com/vlc-robot/robot-3dlotus","data":"https://huggingface.co/datasets/rjgpinel/GEMBench","weights":"https://huggingface.co/rjgpinel/3dlotus"},"release":{"code":"Available","weights":"Available","data":"Available","license":"Not verified","notes":"Official source code, GemBench dataset, 3D-LOTUS and 3D-LOTUS++ checkpoints are public. Actual Hugging Face inventories contain model checkpoints and training/test dataset files. Repository-wide license not verified; upstream components retain their terms."},"evidence":[{"url":"https://arxiv.org/abs/2410.01345","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://www.di.ens.fr/willow/research/gembench/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://arxiv.org/html/2410.01345","label":"Original GemBench protocol, SectionIII / V-A and AppendixVI-A TableVIII","fields":["benchmark","collections","benchmarkName"],"note":"Author-reported totals retained; per-level task counts overlap. No new independent task count inferred from names."},{"url":"https://github.com/vlc-robot/robot-3dlotus","label":"Official current implementation linked by author project","fields":["release"],"note":"Actual runnable release and model artifacts checked independently."},{"url":"https://github.com/vlc-robot/robot-3dlotus/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"139 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."},{"url":"https://huggingface.co/datasets/rjgpinel/GEMBench","label":"Official data release destination","fields":["release.data"],"note":"Destination linked by the official project/repository; component release scope is retained in release notes."},{"url":"https://github.com/vlc-robot/robot-3dlotus/blob/main/INSTALL.md","label":"Released simulator installation recipe","fields":["benchmark.simulatorVersion"],"note":"CoppeliaSim 4.1.0 is a current release installation example, not a version claim from the manuscript."},{"url":"https://github.com/vlc-robot/robot-3dlotus/blob/main/assets/taskvars_train.json","label":"Official train task-variation registry","fields":["benchmark.tasks","benchmark.taskCount","benchmark.taskListScope"],"note":"The four registries contain 92 variations over 44 unique task function IDs; level task counts overlap."},{"url":"https://github.com/vlc-robot/robot-3dlotus/blob/main/assets/taskvars_test_l2.json","label":"Official test_l2 task-variation registry","fields":["benchmark.tasks","benchmark.taskCount","benchmark.taskListScope"],"note":"The four registries contain 92 variations over 44 unique task function IDs; level task counts overlap."},{"url":"https://github.com/vlc-robot/robot-3dlotus/blob/main/assets/taskvars_test_l3.json","label":"Official test_l3 task-variation registry","fields":["benchmark.tasks","benchmark.taskCount","benchmark.taskListScope"],"note":"The four registries contain 92 variations over 44 unique task function IDs; level task counts overlap."},{"url":"https://github.com/vlc-robot/robot-3dlotus/blob/main/assets/taskvars_test_l4.json","label":"Official test_l4 task-variation registry","fields":["benchmark.tasks","benchmark.taskCount","benchmark.taskListScope"],"note":"The four registries contain 92 variations over 44 unique task function IDs; level task counts overlap."},{"url":"https://huggingface.co/rjgpinel/3dlotus/tree/main","label":"Actual 3D-LOTUS checkpoint inventory","fields":["release.weights"],"note":"Contains GemBench model_step_150000.pt and RLBench model_step_300000.pt checkpoint files."},{"url":"https://huggingface.co/rjgpinel/3dlotusplus/tree/main","label":"Actual 3D-LOTUS++ checkpoint inventory","fields":["release.weights"],"note":"Contains GemBench model_step_140000.pt and RealRobot model_step_50000.pt checkpoint files."},{"url":"https://huggingface.co/datasets/rjgpinel/GEMBench/tree/main","label":"Actual GemBench dataset inventory","fields":["release.data"],"note":"Contains released LMDB training episodes and the test microsteps archive."},{"url":"https://arxiv.org/abs/2410.01345","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["LLM/VLM grounding modules and motion-policy training have separate costs.","Author-reported task counts and per-level counts overlap; task variations are the evaluation units."],"verified":"2026-10-17","policy":{"family":"Planning / hybrid","baseModel":"3D-LOTUS: language-conditioned Point Transformer v3; ++ adds LLM planning, OWLv2/SAM object grounding and trajectory prediction.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Calibrated four-view RGB-D, language and robot proprioception.","outputs":"Gripper pose/rotation/open-state keyframes; ++ composes primitive trajectories.","trainingMethod":"Behavior cloning; frozen foundation models provide decomposition and grounding.","trainingData":"GemBench: 3,100 training demonstrations across 31 task variations; separate validation episodes.","trainingTime":"Paper: 150K iterations, about 11 hours on one A100. Current release README estimates approximately 14 hours for policy training; these are different source scopes.","trainingHardware":"Single NVIDIA A100 for GemBench training","evaluation":"RLBench 18-task and four GemBench generalization levels.","controlRate":"Not reported in the inspected primary-source sections"},"benchmarkName":"GemBench","policyName":"3D-LOTUS / 3D-LOTUS++","benchmarkSummary":"Generalization suite built on RLBench, with novel placements, rigid objects, articulated objects and long-horizon compositions.","benchmark":{"simulator":"RLBench on CoppeliaSim","simulatorVersion":"Manuscript does not specify an engine version; current INSTALL.md uses CoppeliaSim Edu 4.1.0 for Ubuntu 20.04 with modified RLBench/PyRep forks.","taskCount":"44 unique released task IDs and 92 test variations; training uses 16 task IDs / 31 variations. Test levels contain 16/15/18/6 task IDs with cross-level overlap.","tasks":["push_button","close_fridge","close_laptop_lid","close_microwave","open_door","open_box","open_drawer","pick_and_lift","pick_up_cup","stack_blocks","put_groceries_in_cupboard","put_money_in_safe","slide_block_to_color_target_peract","reach_and_drag_peract","close_jar_peract","light_bulb_in_peract","put_cube_in_safe","lamp_on","pick_and_lift_cylinder","pick_and_lift_star","pick_and_lift_moon","pick_and_lift_toy","open_drawer_long","close_fridge2","close_laptop_lid2","close_microwave2","open_door2","open_box2","open_drawer2","open_drawer3","close_door","close_box","toilet_seat_up","open_fridge","open_laptop_lid","open_microwave","close_drawer","close_grill","push_buttons4","take_shoes_out_of_box","put_items_in_drawer","tower4","stack_cups","put_all_groceries_in_cupboard"],"taskListScope":"Complete 44 unique released task IDs, deduplicated from official train/L1, L2, L3 and L4 task-variation registries. These lists have 31/28/21/12 entries. Task IDs retain implementation spelling.","capabilities":["Novel placements","Novel rigid objects","Novel articulated objects","Novel long-horizon compositions"],"metrics":"Binary episode success; per-variation average and five-seed mean/standard deviation.","robots":"Franka Panda in RLBench","sensors":"Four 256 × 256 calibrated RGB-D views: front, left shoulder, right shoulder and wrist; robot proprioception.","dataCollection":"RLBench-style scripted experts generate demonstrations for the training task variations; collection is separate from held-out evaluation rollouts.","data":"100 scripted demonstrations for each of 31 training variations = 3,100 training demonstrations. Validation uses 20 episodes per training variation, separately from testing.","protocol":"Four test levels with 31/28/21/12 variations; 20 episodes × five seeds × 92 variations = 9,200 test episodes; maximum 25 keyframe steps.","baselines":"Hiveformer,PolarNet,3D Diffuser Actor,RVT-2,3D-LOTUS,3D-LOTUS++; external RLBench 18-task comparison table separate."}},{"id":"maniskill3","name":"ManiSkill3","title":"ManiSkill3: GPU Parallelized Robotics Simulation and Rendering for Generalizable Embodied AI","date":"2024-10-01","year":2024,"collections":["benchmark"],"categories":["general-manipulation","supporting-resources"],"resourceType":"simulator","summary":"GPU-parallel SAPIEN robotics simulation and learning framework with diverse manipulation environments, RGB-D/point-cloud observations and IL/RL baselines.","institution":"University of California San Diego / collaborating institutions","venue":"RSS 2025","modalities":["RGB","Depth","Proprioception"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper","Dexterous hand","Bimanual","Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2410.00425","project":"https://maniskill.ai/","code":"https://github.com/mani-skill/ManiSkill"},"benchmark":{"simulator":"ManiSkill / SAPIEN / PhysX","simulatorVersion":"ManiSkill3 means mani_skill>=3.0.0; exact SAPIEN pin varies by framework release. Original ManiSkill2 versions <=0.5.3 are a different line.","taskCount":"Growing environment library; not one fixed canonical task-count leaderboard","tasks":["PickCube-v1","PegInsertionSide-v1","PlugCharger-v1","AssemblingKits-v1","StackCube-v1","PushCube-v1","PullCube-v1","OpenCabinetDrawer-v1"],"taskListScope":"Representative official environment IDs only; full versioned catalogue in documentation/source.","capabilities":["GPU-parallel simulation/rendering","Rigid/articulated manipulation","Assembly","IL/RL","Multiple embodiments"],"metrics":"Environment-specific success/reward; throughput; policy-learning baselines.","robots":"Many registered arms, grippers, hands and mobile embodiments.","sensors":"Selectable RGB, depth, segmentation, point clouds and state; tactile support is extension/task-specific, not universal.","dataCollection":"Task-dependent motion planning, RL rollouts and teleoperation; replay and generation tools support multiple collectors rather than one method for every task.","data":"Replayable demonstrations and trajectory-generation tools; task-specific datasets rather than one homogeneous corpus.","protocol":"Gymnasium-compatible vectorized tasks; state and visual RL / learning-from-demonstrations settings.","baselines":"PPO, SAC, behavior cloning, Diffusion Policy and other task-dependent examples."},"release":{"code":"Available","weights":"Partial","data":"Available","license":"Rigid-body environment code permissive (e.g. Apache-2.0); assets CC-BY-NC-4.0 per repository","notes":"Simulator and task framework public; non-commercial asset terms differ from code."},"evidence":[{"url":"https://arxiv.org/abs/2410.00425","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://maniskill.ai/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/mani-skill/ManiSkill","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://arxiv.org/abs/2410.00425","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Framework support for depth does not establish a fixed RGB-D baseline protocol.","Do not compare task counts across moving framework versions without a catalogue snapshot."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Partial","data":"Available","license":"Rigid-body environment code permissive (e.g. Apache-2.0); assets CC-BY-NC-4.0 per repository","notes":"Simulator and task framework public; non-commercial asset terms differ from code."}},"relatedIds":[]},{"id":"hpt","name":"Heterogeneous Pre-trained Transformers (HPT)","title":"Scaling Proprioceptive-Visual Learning with Heterogeneous Pre-trained Transformers","date":"2024-09-30","year":2024,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Embodiment-specific visual/proprioceptive tokenizers share a scalable transformer trunk that transfers to new robot tasks.","institution":"MIT CSAIL / Meta FAIR","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2409.20537","project":"https://liruiw.github.io/hpt/","code":"https://github.com/liruiw/HPT","weights":"https://huggingface.co/liruiw/hpt-base"},"policy":{"family":"Transformer policy","baseModel":"Learned modality/embodiment tokenizers, shared transformer trunk and flexible task-specific action heads.","modelSize":"Reported trunk sizes: Small 3.1M, Base 12.6M, Large 50.5M, XLarge 226.8M, Huge 1.1B; head/tokenizer parameters are separate.","inputs":"Embodiment-specific RGB views and proprioception; optional heterogeneous modalities depend on data source.","outputs":"Task/embodiment-specific robot controls through a separately adapted action head.","trainingData":"Default: 27 datasets / 16k trajectories; scaled: 52 datasets / 270k trajectories.","trainingTime":"Default pretraining: 80k iterations. Downstream example: 20k iterations / about four hours; these are different stages.","trainingHardware":"Downstream example uses one RTX 2080 Ti; full scaled pretraining hardware not summarized as the same machine.","controlRate":"Local forward-pass study on RTX 3070: 39 Hz Base / 33 Hz XLarge excluding preprocessing and encoder; a separate transfer study reports different rates.","trainingMethod":"Heterogeneous multi-dataset behavior pretraining followed by downstream policy-head adaptation/fine-tuning.","evaluation":"Simulated and real manipulation transfer across tasks and embodiments."},"release":{"code":"Available","weights":"Available","data":"Partial","license":"MIT for repository code; external model, dataset and dependency terms remain separate.","notes":"Official pretrained trunks and policy-transfer code; contributing demonstration sources retain separate terms."},"evidence":[{"url":"https://arxiv.org/abs/2409.20537","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://liruiw.github.io/hpt/","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/liruiw/HPT","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."},{"url":"https://github.com/liruiw/HPT/blob/HEAD/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Actual license file inspected; code license does not automatically license data or weights."}],"caveats":["Model size numbers describe the transformer trunk, not a universal full policy.","Do not mix training corpus variants, forward-only timings and end-to-end transfer rates."],"verified":"2026-10-17"},{"id":"canonical-3d-tactile","name":"Canonical 3D Tactile Pretraining","title":"Canonical Representation and Force-Based Pretraining of 3D Tactile for Dexterous Visuo-Tactile Policy Learning","date":"2024-09-26","year":2024,"collections":["policy"],"categories":["tactile-il","tactile-foundation"],"resourceType":"policy","summary":"Canonical i zed distributed 3D touch and force-based pretraining improve dexterous visual-tactile imitation.","institution":"Peking University / PKU-Agibot Lab","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2409.17549","project":"https://3dtacdex.github.io","code":"https://github.com/tianhaowuhz/3dtacdex"},"policy":{"family":"Diffusion","baseModel":"Canonical tactile point/force representation, force self-supervised encoder and diffusion policy.","modelSize":"Not reported","inputs":"RGB, 3D tactile geometry/force and robot proprioception.","outputs":"Arm end-effector pose and 16-Do F hand joint targets.","trainingData":"Real dexterous demonstrations across open box, reorientation, flip and assembly; exact dataset count Not reported in inspected source.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"5 Hz demonstration collection and inference.","trainingMethod":"Force-based tactile pretraining then visuotactile diffusion behavior cloning.","evaluation":"Four real contact-rich dexterous task families."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT","notes":"Author-linked repository and actual LICENSE inspected; code, checkpoints anddatasets have independent availability."},"evidence":[{"url":"https://arxiv.org/abs/2409.17549","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://3dtacdex.github.io","label":"Official author project","fields":["links","release"],"note":"Live project artifact links inspected on2026-10-17."},{"url":"https://github.com/tianhaowuhz/3dtacdex","label":"Official released implementation and LICENSE","fields":["links","release"],"note":"Repository metadata,README and actualLICENSE inspected; availability scoped to the listed component."}],"caveats":["3D tactile contact coordinates are local touch geometry, not external measured depth."],"verified":"2026-10-17"},{"id":"match-policy","name":"MATCH Policy","title":"MATCH POLICY: A Simple Pipeline from Point Cloud Registration to Manipulation Policies","date":"2024-09-23","year":2024,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Point-cloud registration directly transfers demonstrated keyframes without fitting task-policy weights.","institution":"Northeastern University; Worcester Polytechnic Institute","venue":"arXiv preprint","modalities":["Depth","Point cloud","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2409.15517","project":"https://haojhuang.github.io/match_page/"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2409.15517","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://haojhuang.github.io/match_page/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."}],"caveats":["Training-free refers to the geometric task policy; segmentation and motion execution remain external requirements."],"verified":"2026-10-17","policy":{"family":"Geometric transfer","baseModel":"Demonstration retrieval and geometric registration of gripper/object and object/target point clouds.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Segmented RGB-D point clouds and stored manipulation demonstrations.","outputs":"Relative pick/place/keyframe transforms for robot execution.","trainingMethod":"Training-free point-cloud registration and demo transfer.","trainingData":"One or few demonstrations; no learned task-policy training.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"RLBench and six physical robot tasks, including precision and articulated manipulation.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"racer","name":"RACER","title":"RACER: Rich Language-Guided Failure Recovery Policies for Imitation Learning","date":"2024-09-23","year":2024,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A vision-language supervisor issues rich corrective instructions to a visuomotor actor trained with failure-recovery trajectories.","institution":"University of Michigan","venue":"ICRA 2025","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2409.14674","project":"https://rich-language-failure-recovery.github.io/","code":"https://github.com/sled-group/RACER","data":"https://huggingface.co/datasets/Yinpei/augmented_rlbench","weights":"https://huggingface.co/Yinpei/racer-visuomotor-policy-rich"},"policy":{"family":"Hybrid","baseModel":"LLaVA-NeXT / Llama-3 8B supervisor plus an RVT-derived actor whose CLIP language encoder is replaced by T5-11B.","modelSize":"8B supervisor backbone and a separate T5-11B actor language encoder; remaining actor parameters are additional and a combined total is not reported.","inputs":"Task / rich language instruction, four RGB-D views and proprioceptive state for the RVT-derived actor; a front RGB view and robot-state description for the supervisor.","outputs":"Detailed online language guidance and RVT end-effector / gripper actions.","trainingData":"Expert RLBench demonstrations augmented with injected failures and recovery trajectories, annotated with GPT-4-turbo. Physical examples include kinesthetic demonstrations / perturbations.","trainingTime":"Approximately 30 hours for the reported training processes; supervisor trains two epochs and actor eighteen epochs.","trainingHardware":"Eight NVIDIA A40 GPUs, described as 40 GB each in the paper.","controlRate":"Not reported","trainingMethod":"Both supervisor and actor are trained; LoRA rank 128 / alpha 256 adapts the supervisor. At deployment the supervisor supplies corrective language without per-episode retraining.","evaluation":"RLBench long-horizon tasks, dynamic goal changes, failures and selected real Panda sim-to-real experiments."},"release":{"code":"Available","weights":"Partial","data":"Partial","license":"Not verified","notes":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact. Public rich-instruction actor checkpoint and sample / augmentation data are author-linked. The full supervisor, actor and replay artifacts have separate endpoints and requirements. This dataset card declares mit. Public rich-instruction actor checkpoint and sample / augmentation data are author-linked. The full supervisor, actor and replay artifacts have separate endpoints and requirements. This model card declares mit."},"evidence":[{"url":"https://arxiv.org/abs/2409.14674","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/sled-group/RACER/main/readme.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."},{"url":"https://rich-language-failure-recovery.github.io/","label":"Official project links / artifacts","fields":["links","release"],"note":"Author-linked repository / model / dataset endpoints are audited individually; a link alone does not establish a permissive asset license."},{"url":"https://huggingface.co/datasets/Yinpei/augmented_rlbench","label":"Author-linked Hugging Face dataset files / card","fields":["links.data","release.data","release.notes"],"note":"Public rich-instruction actor checkpoint and sample / augmentation data are author-linked. The full supervisor, actor and replay artifacts have separate endpoints and requirements. Card license: mit."},{"url":"https://huggingface.co/Yinpei/racer-visuomotor-policy-rich","label":"Author-linked Hugging Face model files / card","fields":["links.weights","release.weights","release.notes"],"note":"Public rich-instruction actor checkpoint and sample / augmentation data are author-linked. The full supervisor, actor and replay artifacts have separate endpoints and requirements. Card license: mit."}],"caveats":["Ground-truth object locations and failure types are used to generate training annotations, not claimed as supervisor deployment observations.","The paper's stated A40 memory capacity is retained as reported rather than substituted from product specifications."],"verified":"2026-10-17"},{"id":"tactile-functasets","name":"Tactile Functasets / TactFunc","title":"Tactile Functasets: Neural Implicit Representations of Tactile Datasets","date":"2024-09-22","year":2024,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Represents tactile recordings through compact modulations of a meta-learned implicit function, supporting cross-sensor in-hand pose estimation.","institution":"University of Michigan","venue":"Preprint","modalities":["Tactile"],"environments":["Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2409.14592","project":"https://www.mmintlab.com/tactile-functasets","code":"https://github.com/MMintLab/tactile_functasets"},"model":{"role":"Reusable representation or perception model; downstream controllers are separate systems","family":"Tactile encoder","baseModel":"Meta-learned neural implicit trunk with sample-specific latent modulation vectors; downstream MLP","modelSize":"Not reported","inputs":"GelSlim or Soft Bubble tactile observations and sample coordinates during implicit fitting","outputs":"Compact latent modulations, reconstructed tactile signals and downstream in-hand pose estimates","trainingMethod":"Meta-learn a shared implicit function; fit per-observation latent variables; optional SGLD or nearest-neighbor inference","trainingData":"Paired GelSlim and Soft Bubble grasping data from Touch2Touch, using normalized contact-difference images.","trainingTime":"Not reported","trainingHardware":"Not reported","inference":"Not reported","evaluation":"Reconstruction and compression, cross-sensor features, and in-hand pose estimation using compact representations."},"release":{"code":"Partial — meta-learning and functaset code public; downstream pose section marked Coming Soon","weights":"Not verified","data":"Not verified","license":"Apache-2.0 source LICENSE; third-party tactile data retains original terms.","notes":"Code, pretrained encoders and downstream task data are separate artifacts; no policy checkpoint is implied by an encoder release."},"evidence":[{"url":"https://arxiv.org/abs/2409.14592","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Method, experiments and implementation sections; specifications retain component and evaluation scope."},{"url":"https://www.mmintlab.com/tactile-functasets","label":"Official project","fields":["links","release"],"note":"Author project; author-reported release scope is distinguished from an executed reproduction."},{"url":"https://github.com/MMintLab/tactile_functasets","label":"Official implementation and current release instructions","fields":["release","links"],"note":"README and repository files inspected on 2026-10-17."},{"url":"https://github.com/MMintLab/tactile_functasets/blob/master/LICENSE","label":"Actual Apache-2.0 repository license","fields":["release.license"],"note":"LICENSE fetched independently of README description."}],"caveats":["Latent fitting can require inference-time optimization; a compact representation is not automatically a one-forward-pass control policy.","The official README leaves downstream pose instructions as Coming Soon; paper results do not imply a complete current reproduction release."],"relatedIds":["cttp"],"verified":"2026-10-17"},{"id":"tinyvla","name":"TinyVLA","title":"TinyVLA: Towards Fast, Data-Efficient Vision-Language-Action Models for Robotic Manipulation","date":"2024-09-19","year":2024,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Connects compact LLaVA/Pythia vision-language models to diffusion actions with low-rank robot adaptation.","institution":"East China Normal University / Midea AI Lab","venue":"IEEE Robotics and Automation Letters 2025","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2409.12514","project":"https://tiny-vla.github.io/","code":"https://github.com/liyaxuanliyaxuan/TinyVLA","weights":"https://huggingface.co/lesjie/Llava-Pythia-400M"},"policy":{"family":"VLA","baseModel":"LLaVA-style VLM with Pythia language backend and diffusion action decoder","modelSize":"Paper explores70M–1.4B language backends; released VLM assets400M, 700M, 1.3B; full policy totals vary","inputs":"Multi-view RGB, task language and robot proprioceptive state; normalized visual-language features are concatenated with the state vector","outputs":"Continuous robot actions through diffusion decoder; robot/experiment-specific output dimensions","trainingData":"VLM vision-language pretraining; no large robot pretraining required; five real tasks with 100 demos/task","trainingTime":"Wall-clock and full training schedule not reported in inspected manuscript","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"LoRA adaptation of compact VLM with jointly trained diffusion policy; compare MLP/ACT action heads","evaluation":"Five real manipulation tasks, distractor/generalization experiments and model-scale/action-head ablations"},"release":{"code":"Available","weights":"Partial","data":"Not verified","license":"MIT","notes":"Availability reflects public source/model links; complete paper-scale reproduction and all dataset access terms were not tested."},"evidence":[{"url":"https://arxiv.org/abs/2409.12514","label":"Primary manuscript and first-submission history","fields":["date","title","policy","modalities","environments","embodiments","imageCandidates"],"note":"Latest available full manuscript inspected; publication date retains the first arXiv submission. Sizes, training cost and deployment rates keep their experimental scope."},{"url":"https://github.com/liyaxuanliyaxuan/TinyVLA","label":"Official policy implementation","fields":["links","release"],"note":"Source and checkpoint availability checked separately; no local policy execution was performed."},{"url":"https://raw.githubusercontent.com/liyaxuanliyaxuan/TinyVLA/main/LICENSE","label":"Repository license file","fields":["release.license"],"note":"Code scope; model, data and third-party component terms can differ."}],"caveats":["Linked Hugging Face assets are pretrained VLM components; availability of every complete robot-policy checkpoint is not verified."],"verified":"2026-10-17"},{"id":"tacdiffusion","name":"TacDiffusion","title":"TacDiffusion: Force-domain Diffusion Policy for Precise Tactile Manipulation","date":"2024-09-17","year":2024,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"Generates force-domain actions from wrench and velocity feedback for transferable high-precision insertion.","institution":"Technical University of Munich","venue":"ICRA 2025","modalities":["Force/Torque","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2409.11047","code":"https://github.com/popnut123/TacDiffusion"},"policy":{"family":"Diffusion","baseModel":"Conditional force-domain diffusion MLP; dynamic-system interpolating filter","modelSize":"Not reported","inputs":"Current/previous external wrench, internal wrench and end-effector velocity","outputs":"Six-dimensional desired wrench fed to impedance control","trainingData":"Demonstrations of one insertion task; 80% training split; exact count not reported","trainingTime":"Up to 1M optimization iterations; wall-clock not reported","trainingHardware":"NVIDIA RTX3090 for training/inference","controlRate":"Model variants51.2–503.8 Hz; selected512-neuron model141.8 Hz; filtered low-level control1 kHz","trainingMethod":"Force-domain behavior cloning plus dynamic filtering and feed-forward impedance control","evaluation":"Five tight-clearance insertion geometries with zero-shot transfer"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Official implementation includes learned models and real-robot insertion/control scripts; license not independently verified."},"evidence":[{"url":"https://arxiv.org/abs/2409.11047","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/popnut123/TacDiffusion","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2409.11047","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Uses force/torque observations rather than raw skin or optical tactile images.","1 kHz is interpolated impedance control, not diffusion inference."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Official implementation includes learned models and real-robot insertion/control scripts; license not independently verified."}},"relatedIds":[]},{"id":"rhyme","name":"RHyME","title":"One-Shot Imitation under Mismatched Execution","date":"2024-09-10","year":2024,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Sequence-level optimal transport aligns unpaired human and robot video, creating demonstration prompts for cross-embodiment manipulation.","institution":"Cornell University","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2409.06615","project":"https://portal-cornell.github.io/rhyme/","code":"https://github.com/portal-cornell/rhyme","data":"https://huggingface.co/datasets/prithwishdan/RHyME"},"policy":{"family":"Diffusion","baseModel":"Video representation encoder, optimal-transport demonstration retrieval and a conditional diffusion visuomotor policy.","modelSize":"Not reported","inputs":"Human / cross-embodiment demonstration video and current robot observations.","outputs":"Robot action trajectory conditioned on a retrieved / observed demonstration representation.","trainingData":"Long-horizon robot demonstrations plus over three hours of cross-embodiment play data in the simulated benchmark; no paired human-robot trajectory requirement.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Learn video representations, synthesize aligned human demonstrations from short clips, and train a hybrid robot/human-conditioned policy.","evaluation":"Multi-stage simulated manipulation with increasingly mismatched demonstrators, and real human-hand demonstration imitation."},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"Not verified","notes":"Official code and simulation dataset are linked; availability of exact trained weights is not established by checkpoint argument examples."},"evidence":[{"url":"https://arxiv.org/abs/2409.06615","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://portal-cornell.github.io/rhyme/","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/portal-cornell/rhyme","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."}],"caveats":["Unpaired-training alignment is distinct from a paired demonstration corpus."],"verified":"2026-10-17"},{"id":"texttoucher","name":"TextToucher","title":"TextToucher: Fine-Grained Text-to-Touch Generation","date":"2024-09-09","year":2024,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Generates optical tactile images from fine-grained text descriptions of contact texture, shape and gel state.","institution":"Zhejiang University; Yale University","venue":"AAAI 2025","modalities":["Language"],"environments":["Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2409.05427","code":"https://github.com/TtuHamg/TextToucher","weights":"https://drive.google.com/drive/folders/1207ia6VuSRlrQAVSoH3P5XJFSK7Kk8UK?usp=sharing"},"model":{"role":"Reusable representation or perception model; downstream controllers are separate systems","family":"Generative tactile model","baseModel":"PixArt-α diffusion-transformer backbone with dual-grain textual conditioning and gel-state embeddings","modelSize":"Not reported","inputs":"Fine-grained language descriptions and tactile sensor/gel-state conditions","outputs":"Synthetic tactile images","trainingMethod":"Text-conditioned diffusion using tactile shape/texture captions and learned gel-status representations","trainingData":"HCT and SSVTP optical-touch data; LLaVA-v1.6-34B used for annotation, not robot control","trainingTime":"Generator: 200 epochs; auxiliary ViT-Tiny: 200 epochs. Wall-clock duration not reported.","trainingHardware":"Generator: 4 NVIDIA RTX 4090 24GB GPUs; auxiliary encoder: 1 RTX 4090","inference":"50 sampling steps with DPM-Solver for the reported generation evaluation","evaluation":"Text-to-touch generation fidelity, image/text alignment and tactile-specific image metrics."},"release":{"code":"Available","weights":"Available — official download link","data":"Not verified","license":"Apache-2.0 repositoryLICENSE; PixArt/annotation/backbone and data terms remain separate","notes":"Code, pretrained encoders and downstream task data are separate artifacts; no policy checkpoint is implied by an encoder release."},"evidence":[{"url":"https://arxiv.org/abs/2409.05427","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Method, experiments and implementation sections; specifications retain component and evaluation scope."},{"url":"https://github.com/TtuHamg/TextToucher","label":"Official implementation and current release instructions","fields":["release","links"],"note":"README and repository files inspected on 2026-10-17."},{"url":"https://github.com/TtuHamg/TextToucher/blob/main/LICENSE","label":"Repository license file","fields":["release.license"],"note":"Applies to repository scope; model/data/base-model terms can differ."}],"caveats":["Tactile images are generated targets; this model does not measure tactile contact at deployment or produce actions.","Synthetic tactile imagery is not a validated replacement for measured force dynamics."],"relatedIds":["tvl","octopi"],"verified":"2026-10-17"},{"id":"rekep","name":"ReKep","title":"ReKep: Spatio-Temporal Reasoning of Relational Keypoint Constraints for Robotic Manipulation","date":"2024-09-03","year":2024,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Generates relational constraints over visual 3D keypoints and solves a sequence of constrained optimization problems for manipulation.","institution":"Stanford University","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2409.01652","project":"https://rekep-robot.github.io/","code":"https://github.com/huangwl18/ReKep"},"policy":{"family":"Robot agent","baseModel":"GPT-4o for relational constraint generation; DINOv2 features support visual keypoint proposals.","modelSize":"Not reported","inputs":"Task language, RGB-D-derived 3D keypoints, robot state and visual execution feedback.","outputs":"Python relational-keypoint constraint functions, followed by optimized end-effector subgoals and trajectories.","trainingData":"No task-specific model training; pretrained vision / language models and program examples define the constraint interface.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen visual / language backbones; keypoint proposal, constraint synthesis and hierarchical nonlinear optimization with closed-loop re-solving.","evaluation":"Real and simulated manipulation, including bimanual and mobile-robot settings and multi-stage contact interactions."},"release":{"code":"Available","weights":"Not applicable","data":"Not verified","license":"Not reported — no repository license found","notes":"Author-linked implementation includes keypoint/constraint generation, environment interfaces and path/subgoal optimization. No repository license was found in the inspected root inventory."},"evidence":[{"url":"https://arxiv.org/abs/2409.01652","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/huangwl18/ReKep","label":"Official implementation, README and file/license inspection on 19 October 2026","fields":["links","release"],"note":"Author-linked implementation includes keypoint/constraint generation, environment interfaces and path/subgoal optimization. No repository license was found in the inspected root inventory. Not reported — no repository license found."}],"caveats":["Constraint satisfaction and motion optimization supply substantial geometric control.","Visual keypoints are not contact sensing, and RGB-D input is distinct from tactile feedback."],"verified":"2026-10-19"},{"id":"dppo","name":"DPPO","title":"Diffusion Policy Policy Optimization","date":"2024-09-01","year":2024,"collections":["policy"],"categories":["visual-precision-rl"],"resourceType":"policy","summary":"Fine-tunes diffusion policies with PPO by treating denoising steps as an inner decision process.","institution":"Princeton University / Toyota Research Institute / MIT","venue":"ICLR 2025","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2409.00588","project":"https://diffusion-ppo.github.io/","code":"https://github.com/irom-princeton/dppo","data":"https://github.com/irom-princeton/dppo#pre-training","weights":"https://github.com/irom-princeton/dppo#pre-training"},"policy":{"family":"RL","baseModel":"Conditional diffusion MLP/U-Net/Transformer depending on task; optional visual encoder","modelSize":"Task-dependent; no single canonical parameter count","inputs":"RGB plus proprioception or low-dimensional state, explicitly separate benchmark settings","outputs":"Continuous action chunks produced by denoising","trainingData":"Task-dependent demonstration initialization; Furniture-Bench uses 50 simulated human demos per task plus online simulation rollouts","trainingTime":"Task-dependent; wall-clock tables in appendix, no single full-run cost","trainingHardware":"Robomimic timing: RTX 2080 + 40 CPU threads; GPU-parallel Furniture-Bench setup differs","controlRate":"Furniture simulation and hardware policy execute at 10 Hz; chunk horizon commonly 8","trainingMethod":"On-policy PPO over diffusion steps; sparse task reward; sim-to-real deployment","evaluation":"Gym/D3IL, Robomimic and Furniture-Bench, including real zero-shot assembly deployment"},"release":{"code":"Available","weights":"Available","data":"Available","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2409.00588","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/irom-princeton/dppo","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://diffusion-ppo.github.io/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/irom-princeton/dppo/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["A fine-tuning framework, not one universal pretrained checkpoint.","Privileged state experiments and pixel experiments should not be merged as the same observation protocol."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Available","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"icrt","name":"In-Context Robot Transformer (ICRT)","title":"In-Context Imitation Learning via Next-Token Prediction","date":"2024-08-28","year":2024,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A causal sensorimotor transformer learns to imitate a prompted task from demonstration trajectories without target-task fine-tuning.","institution":"University of California, Berkeley; Autodesk","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2408.15980","project":"https://icrt.dev","code":"https://github.com/Max-Fu/icrt","data":"https://huggingface.co/datasets/Ravenh97/ICRT-MT","weights":"https://huggingface.co/mlfu7/ICRT"},"policy":{"family":"In-context policy","baseModel":"Llama2-style causal transformer with a pretrained vision transformer and modality-specific projectors; an additional Llama2-7B initialization is evaluated with LoRA.","modelSize":"7B for the ICRT-Llama2 backbone variant; this is distinct from the smaller default ICRT model.","inputs":"Wrist and side RGB observations, absolute end-effector pose / gripper state and in-context demonstration state–action trajectories; language is not required at deployment.","outputs":"Continuous delta end-effector translation / rotation and gripper action chunks; 16 future actions are predicted and ten are executed in the reported setup.","trainingData":"Approximately 2,000 grouped DROID trajectories after filtering, then ICRT-MT: 1,098 trajectories covering 29 tasks and six primitives.","trainingTime":"Default ICRT: 56 minutes of DROID pretraining and 18 hours on ICRT-MT. The Llama2-7B variant takes roughly 28 hours to fine-tune.","trainingHardware":"Four NVIDIA A100 80 GB GPUs for training; RTX 3090 Ti is the reported inference workstation GPU.","controlRate":"Measured inference: 39.6 Hz for ICRT and 10.7 Hz for ICRT-Llama2, averaged over 100 steps on the reported workstation.","trainingMethod":"Offline next-token prediction on task-grouped sensorimotor sequences; demonstration-prefix losses are masked. At deployment, demonstrations provide context without parameter updates.","evaluation":"Real Franka manipulation: unseen task / object / environment combinations, including pick-and-place, poking and drawer behaviors."},"release":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 for repository code, verified from LICENSE.txt; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Official project links code, ICRT checkpoints and ICRT-MT dataset; component licenses require separate inspection."},"evidence":[{"url":"https://arxiv.org/abs/2408.15980","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/Max-Fu/icrt","label":"Author-linked repository","fields":["links.code"],"note":"Repository identity; code and checkpoint release status are stated separately."},{"url":"https://github.com/Max-Fu/icrt/blob/main/LICENSE.txt","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["The default randomly initialized transformer and the LoRA-adapted Llama2-7B model are different variants.","ICRT-MT offline fine-tuning is part of model preparation; absence of target-task fine-tuning does not mean the model is untrained."],"verified":"2026-10-19"},{"id":"crossformer","name":"CrossFormer","title":"Scaling Cross-Embodied Learning: One Policy for Manipulation, Navigation, Locomotion and Aviation","date":"2024-08-21","year":2024,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"A cross-embodiment transformer supports heterogeneous sensors, action dimensions and control frequencies, including single-arm and bimanual manipulation.","institution":"UC Berkeley / Carnegie Mellon University","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2408.11812","project":"https://crossformer-model.github.io","code":"https://github.com/rail-berkeley/crossformer","weights":"https://huggingface.co/rail-berkeley/crossformer"},"policy":{"family":"Transformer policy","baseModel":"Modality-specific tokenizers, cross-embodiment transformer and embodiment-specific action readouts.","modelSize":"130M released checkpoint, as labeled in the official repository.","inputs":"Robot-dependent RGB observations, optional proprioception, and goal/task conditioning; observation layouts vary by embodiment.","outputs":"Embodiment-specific continuous actions with variable action chunk lengths.","trainingData":"900k manipulation trajectories from an OXE subset, supplemented by ALOHA, GNM navigation, Go1 and additional Franka data; domains are not all manipulation.","trainingTime":"300k gradient steps, batch size 512; 47 hours in the reported training configuration.","trainingHardware":"TPU v5e-256 pod for training; RTX 4090 or similar GPU for manipulation inference.","controlRate":"Embodiment-dependent training/deployment frequencies; figure reports a 5–20 Hz range rather than one universal policy frequency.","trainingMethod":"Cross-embodiment behavior cloning with heterogeneous observation/action spaces and goal relabeling.","evaluation":"Real single-arm and bimanual manipulation, alongside navigation, quadruped locomotion and aviation."},"release":{"code":"Available","weights":"Available","data":"Partial","license":"MIT for repository code; external model, dataset and dependency terms remain separate.","notes":"Public 130M checkpoint, inference notebook/server and training code; contributing datasets retain separate release terms."},"evidence":[{"url":"https://arxiv.org/abs/2408.11812","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://crossformer-model.github.io","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/rail-berkeley/crossformer","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."},{"url":"https://github.com/rail-berkeley/crossformer/blob/HEAD/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Actual license file inspected; code license does not automatically license data or weights."}],"caveats":["Included for its actual manipulation evaluations; navigation / locomotion results are not counted as manipulation tasks."],"verified":"2026-10-17"},{"id":"tacsl","name":"TacSL","title":"TacSL: A Library for Visuotactile Sensor Simulation and Learning","date":"2024-08-12","year":2024,"collections":["benchmark"],"categories":["supporting-resources","precision-assembly"],"resourceType":"simulator","summary":"GPU tactile image and spatial force-field simulation with contact-task learning tools; original research toolkit and current Isaac Lab integration have separate software scopes.","institution":"NVIDIA / University of Washington","venue":"IEEE Transactions on Robotics 2025","modalities":["RGB","Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2408.06506","project":"https://iakinola23.github.io/tacsl/","code":"https://github.com/isaac-sim/IsaacLab"},"benchmark":{"simulator":"Original Isaac Gym; current Isaac Lab integration with PhysX","simulatorVersion":"Original toolkit is Isaac Gym-based. Current Isaac Lab main README lists Isaac Sim6.2 compatibility; this current integration stack is distinct from the paper and earlier Isaac Lab beta snapshots.","taskCount":"3 contact-task families; paper control results report 2, with Bolt-on-Nut left as a challenge","tasks":["Peg Placement","Peg Insertion","Bolt-on-Nut"],"taskListScope":"Complete original learning-task suite; quantitative results focus on placement and insertion.","capabilities":["Optical tactile rendering","Normal/shear force fields","Asymmetric RL distillation","Sim-to-real tactile policy learning"],"metrics":"Rendering/simulation throughput; placement/insertion success; multimodal/distillation ablations and physical transfer.","robots":"Franka Panda with sensorized parallel gripper.","sensors":"GelSight R1.5-style tactile RGB and spatial normal/shear force fields; wrist camera and proprioception. Current force fields use penalty/SDF queries.","dataCollection":"Privileged-state PPO expert policies generate simulated interaction and offline/online distillation trajectories; this is separate from physical transfer evaluation.","data":"RL/offline expert trajectories generated through toolkit; standard public data count Not reported.","protocol":"Compare observation modalities and online/offline distillation; randomize object-in-gripper pose and target location; physical peg placement/insertion validation.","baselines":"AACD, asymmetric actor-critic, DAgger and offline behavior cloning."},"release":{"code":"Available","weights":"Not verified","data":"Partial","license":"Isaac Lab BSD-3-Clause code; simulator/sensor asset terms separate","notes":"Current integration available in Isaac Lab; legacy toolkit status and API differ."},"evidence":[{"url":"https://arxiv.org/abs/2408.06506","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://iakinola23.github.io/tacsl/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/isaac-sim/IsaacLab","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://isaac-sim.github.io/IsaacLab/v3.0.0-beta2/source/experimental-features/visuo_tactile_sensor.html","label":"Official implementation / documentation","fields":["benchmark.simulatorVersion","benchmark.sensors"],"note":"Versioned current integration: SDF penalty fields, tactile outputs and PhysX-only support."},{"url":"https://arxiv.org/abs/2408.06506","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."},{"url":"https://github.com/isaac-sim/IsaacLab","label":"Official current source / artifact inventory — 21 October 2026","fields":["benchmark.simulatorVersion"],"note":"Current main README compatibility table and repository inventory reviewed; historical toolkit and current simulator stack remain distinct."}],"caveats":["Current force fields are model-estimated, not a guarantee of calibrated real contact-force distributions.","Current demo supports PhysX, not Newton simulation backend.","Tactile depth means local elastomer deformation geometry, distinct from scene depth cameras."],"verified":"2026-10-21","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not verified","data":"Partial","license":"Isaac Lab BSD-3-Clause code; simulator/sensor asset terms separate","notes":"Current integration available in Isaac Lab; legacy toolkit status and API differ."}},"relatedIds":[]},{"id":"unit","name":"UniT","title":"UniT: Data Efficient Tactile Representation with Generalization to Unseen Objects","date":"2024-08-12","year":2024,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Uses a VQGAN tactile autoencoder trained with a single simple object, transferring compact tactile features to unseen objects and downstream manipulation.","institution":"Purdue University; University of Arkansas","venue":"IEEE Robotics and Automation Letters 2025","modalities":["Tactile"],"environments":["Simulation","Real world"],"embodiments":["Robot arm with parallel-jaw tactile gripper"],"links":{"paper":"https://arxiv.org/abs/2408.06481","project":"https://zhengtongxu.github.io/unit-website/","code":"https://github.com/ZhengtongXu/UniT","data":"https://drive.google.com/drive/folders/1CkPqgNFCE6B1mr2pxYdNdSR-xAkSnxQc?usp=sharing"},"model":{"role":"Reusable representation or perception model; downstream controllers are separate systems","family":"Tactile encoder","baseModel":"VQGAN encoder, quantized codebook and adversarial/reconstruction decoder; downstream frozen encoder heads","modelSize":"79.81M for the reported 16×20 representation configuration; includes autoencoder scope","inputs":"GelSight Mini tactile image","outputs":"Quantized tactile latent map; downstream pose, material or policy features","trainingMethod":"Unsupervised VQGAN reconstruction using contacts with a simple object; separate supervised decoders or Diffusion Policy downstream","trainingData":"Representation data from one simple object per training setup; downstream demonstrations: chicken-leg hanging 200, chip grasping 180, Allen-key insertion 170","trainingTime":"Not reported","trainingHardware":"Not reported","inference":"Not reported","evaluation":"Unseen-object reconstruction; in-hand 3D/6D pose and classification; three physical policy tasks, plus paper simulated policy comparisons."},"release":{"code":"Available","weights":"Not verified — no separately confirmed pretrained model file","data":"Available — official resource link","license":"MIT code LICENSE; linked data/checkpoints require their own artifact terms","notes":"Code, pretrained encoders and downstream task data are separate artifacts; no policy checkpoint is implied by an encoder release."},"evidence":[{"url":"https://arxiv.org/abs/2408.06481","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Method, experiments and implementation sections; specifications retain component and evaluation scope."},{"url":"https://zhengtongxu.github.io/unit-website/","label":"Official project","fields":["links","release"],"note":"Author project; author-reported release scope is distinguished from an executed reproduction."},{"url":"https://github.com/ZhengtongXu/UniT","label":"Official implementation and current release instructions","fields":["release","links"],"note":"README and repository files inspected on 2026-10-17."},{"url":"https://github.com/ZhengtongXu/UniT/blob/main/LICENSE","label":"Repository license file","fields":["release.license"],"note":"Applies to repository scope; model/data/base-model terms can differ."}],"caveats":["Earlier lists call this “Unified Tactile Representation for Robot Learning”; current paper title is Data Efficient Tactile Representation with Generalization to Unseen Objects.","Single-object training is representation pretraining, not zero-demonstration training of the downstream robot controller."],"relatedIds":["t3","sparsh"],"verified":"2026-10-17"},{"id":"shape-conditioned-tactile-agent","name":"Shape-Conditioned Tactile Agent","title":"Learning a Shape-Conditioned Agent for Purely Tactile In-Hand Manipulation of Various Objects","date":"2024-07-26","year":2024,"collections":["policy"],"categories":["tactile-foundation","visual-precision-rl"],"resourceType":"policy","summary":"Shape-conditioned tactile state estimation and control reorient diverse objects without external object tracking.","institution":"DLR / Technical University of Munich","venue":"Preprint","modalities":["Force/Torque","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2407.18834"},"policy":{"family":"RL","baseModel":"PPO/D2 RL actor, recurrent state estimator and Vec BPS object shape encoding.","modelSize":"Not reported","inputs":"Joint position/control error history, known object shape, estimated pose/uncertainty and orientation goal.","outputs":"12 hand joint targets.","trainingData":"4096 parallel domain-randomized Isaac simulation instances across multiple object shapes.","trainingTime":"Tactile policy+estimator~60 h; oracle ablation 7–11 h.","trainingHardware":"Single NVIDIA T4.","controlRate":"60 Hz joint measurements; policy frequency Not reported here.","trainingMethod":"Estimator-coupled RL with differentiable shape conditioning andsim-to-real transfer.","evaluation":"DLR Hand II diverse-object goal-conditioned reorientation."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2407.18834","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."}],"caveats":["Known object shape is an explicit input; vision-free does not mean shape-free."],"verified":"2026-10-17"},{"id":"automate","name":"AutoMate","title":"AutoMate: Specialist and Generalist Assembly Policies over Diverse Geometries","date":"2024-07-10","year":2024,"collections":["benchmark"],"categories":["precision-assembly","supporting-resources"],"resourceType":"dataset","summary":"Assembly learning framework with a 100-assembly geometry collection and specialist/generalist sim-to-real experiments.","institution":"University of Southern California / NVIDIA","venue":"RSS 2024","modalities":["RGB","Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2407.08028","project":"https://bingjietang718.github.io/automate/","code":"https://github.com/isaac-sim/IsaacGymEnvs"},"benchmark":{"simulator":"Isaac Gym / Factory-style contact simulation","simulatorVersion":"Exact simulator build Not reported in project overview.","taskCount":"100 plug-socket assembly geometries; specialist results over 80; generalist trained/evaluated on 20; perception-initialized real study on 5","tasks":["Plug-socket assembly (100 geometry IDs)"],"taskListScope":"One assembly task family with geometry IDs, not 100 distinct semantic task names; exact IDs supplied by released assembly collection.","capabilities":["Geometry-diverse insertion","Specialist/generalist policies","Perception-initialized assembly","Sim-to-real transfer"],"metrics":"Insertion success over assembly geometries; specialist/generalist success; physical perception-initialized study.","robots":"Franka Panda with shore 30A finger pads and wrist RealSense D435.","sensors":"RGB-D camera feeds SAM + FoundationPose in perception-initialized workflow. Assembly actors use estimated/privileged geometry and state, not a raw RGBDT observation stack.","dataCollection":"Automatically generated disassembly paths are reversed into assembly demonstrations and guide subsequent RL policy learning.","data":"100 assembly CAD assets compatible with simulation and physical printing; policy training via RL plus assembly trajectories.","protocol":"Disassembly trajectories guide insertion-policy learning; specialist per geometry and generalist multi-geometry evaluation; physical pose-based trials and a separate five-assembly perception study.","baselines":"Specialist and generalist AutoMate policies; IndustReal-related and guidance/distillation ablations."},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"IsaacGymEnvs BSD-3-Clause code; assembly asset terms separately verified at download","notes":"Code and geometry dataset linked by official project; legacy simulator dependency."},"evidence":[{"url":"https://arxiv.org/abs/2407.08028","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://bingjietang718.github.io/automate/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/isaac-sim/IsaacGymEnvs","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://arxiv.org/abs/2407.08028","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["100 assembly assets do not imply 100 language-conditioned tasks.","Perception initialization includes user clicks for SAM segmentation in the described workflow."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not verified","data":"Available","license":"IsaacGymEnvs BSD-3-Clause code; assembly asset terms separately verified at download","notes":"Code and geometry dataset linked by official project; legacy simulator dependency."}},"relatedIds":[]},{"id":"tactile-skin-translation","name":"Tactile Skin Translation","title":"Learning In-Hand Translation Using Tactile Skin With Shear and Normal Force Sensing","date":"2024-07-10","year":2024,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"Transfers tactile RL policies that use ternary shear and binary normal skin signals for in-hand object translation.","institution":"Meta AI / University of Pennsylvania","venue":"Preprint","modalities":["Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2407.07885","project":"https://jessicayin.github.io/tactile-skin-rl/","code":"https://github.com/jessicayin/tactile_skin_model"},"policy":{"family":"RL","baseModel":"Privileged teacher / deployable tactile-proprioceptive student","modelSize":"Not reported","inputs":"Allegro joint state/history and binarized3-axis ReSkin palm signals","outputs":"16 target joint positions","trainingData":"Simulation RL over randomized cylinders; no real demonstrations","trainingTime":"Not reported","trainingHardware":"2 RTX4090 GPUs; ≈5,000 simulation training FPS with tactile","controlRate":"Real policy20 Hz /PD300 Hz; ReSkin78 Hz; paper simulation section reports10 Hz control","trainingMethod":"Fast tactile skin model, two-stage teacher/student training and sim-to-real randomization","evaluation":"Real in-hand translation under unseen object properties and hand orientations"},"release":{"code":"Available","weights":"Not verified","data":"Not applicable","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Official project links tactile simulation and learning code."},"evidence":[{"url":"https://arxiv.org/abs/2407.07885","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/jessicayin/tactile_skin_model","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2407.07885","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."},{"url":"https://github.com/jessicayin/tactile_skin_model/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["Touch is discretized; this is not a continuous full-force observation.","Reported simulation-control and real-policy rates differ."],"verified":"2026-10-19","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Not applicable","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Official project links tactile simulation and learning code."}},"relatedIds":[]},{"id":"equibot","name":"EquiBot","title":"EquiBot: SIM(3)-Equivariant Diffusion Policy for Generalizable and Data Efficient Learning","date":"2024-07-01","year":2024,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Enforces SIM(3) equivariance in diffusion policies to generalize across object translations, rotations and scales.","institution":"Stanford University","venue":"CoRL 2024","modalities":["Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2407.01479","project":"https://equi-bot.github.io/","code":"https://github.com/yjy0625/equibot"},"policy":{"family":"Diffusion","baseModel":"SIM(3)-equivariant PointNet/vector-neuron encoder + equivariant diffusion U-Net","modelSize":"Not reported","inputs":"Object-centric single-view depth point cloud and robot proprioception","outputs":"End-effector 6D velocity and gripper actions, including coordinated mobile/bimanual settings","trainingData":"50 synthetic demos per simulated task; approximately five minutes of human demonstrations per real task","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Real demonstrations recorded at 15 Hz then subsampled to 3 Hz; not a claimed 15 Hz policy loop","trainingMethod":"Equivariant diffusion behavior cloning; human-video demonstrations converted into robot action coordinates","evaluation":"Simulation manipulation and six real household tasks including mobile/bimanual manipulation"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2407.01479","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/yjy0625/equibot","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://equi-bot.github.io/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/yjy0625/equibot/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["Object segmentation and stereo-to-depth preprocessing are required for real demonstrations.","Equivariance assumptions are task-dependent; gravity-sensitive outcomes are not automatically scale/rotation invariant."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"robopack","name":"RoboPack","title":"RoboPack: Learning Tactile-Informed Dynamics Models for Dense Packing","date":"2024-07-01","year":2024,"collections":["policy"],"categories":["tactile-foundation","rgbd-3d"],"resourceType":"policy","summary":"Tactile-informed recurrent graph dynamics support predictive control for tool-use pushing and dense packing.","institution":"Stanford University / University of Illinois Urbana-Champaign / A*STAR","venue":"RSS 2024","modalities":["RGB","Depth","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2407.01418","project":"https://robo-pack.github.io","code":"https://github.com/BoAi01/robopack"},"policy":{"family":"Model-based planning","baseModel":"Recurrent GNN particle/state/latent physics model with sampling-based MPC.","modelSize":"Not reported","inputs":"Multi-viewRGB-D object key points and Soft Bubble tactile geometry/flow history.","outputs":"Future object states for planning and robot motion actions.","trainingData":"Approximately 30 min real interaction/task on average; detailed cohorts include~12 k box-pushing frames and~6 k packing frames.","trainingTime":"Dynamics training converges within 25 h for non-prehensile pushing and 8 h for dense packing.","trainingHardware":"One NVIDIA RTX A5000 GPU.","controlRate":"Not reported","trainingMethod":"Offline dynamics learning from teleoperated random interactions; online MPC.","evaluation":"Franka Panda non-prehensile tool-use pushing and dense packing of rigid/deformable objects."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Not verified; no unambiguous repository license identified.","notes":"Author-linked repository and actual LICENSE inspected; code, checkpoints anddatasets have independent availability."},"evidence":[{"url":"https://arxiv.org/abs/2407.01418","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://robo-pack.github.io","label":"Official author project","fields":["links","release"],"note":"Live artifact status reviewed separately from paper claims."},{"url":"https://github.com/BoAi01/robopack","label":"Official released implementation and LICENSE","fields":["links","release"],"note":"Repository metadata,README and actualNone inspected; availability scoped to the listed component."}],"caveats":["Learned latent physics captures partially observed properties; not full-state oracle planning."],"verified":"2026-10-17"},{"id":"robouniview","name":"RoboUniView","title":"RoboUniView: Visual-Language Model with Unified View Representation for Robotic Manipulation","date":"2024-06-27","year":2024,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"A view-unified 3D representation decouples multi-camera perception from language-conditioned robot action learning.","institution":"Meituan and collaborating authors; exact affiliations in primary manuscript","venue":"arXiv preprint","modalities":["RGB","Language","Proprioception"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2406.18977","code":"https://github.com/liufanfanlff/RoboUniview"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2406.18977","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://github.com/liufanfanlff/RoboUniview","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/liufanfanlff/RoboUniview/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"195 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["A calibrated 3D feature representation is not necessarily a measured depth observation; depth may provide occupancy supervision."],"verified":"2026-10-17","policy":{"family":"VLA","baseModel":"UVFormer occupancy-pretrained representation with RoboFlamingo-derived policy.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Multi-view RGB, camera intrinsics/extrinsics, language and robot proprioception.","outputs":"Continuous robot manipulation actions.","trainingMethod":"3D occupancy view-representation pretraining followed by imitation action learning.","trainingData":"CALVIN and multi-camera/multi-dataset robot data.","trainingTime":"CALVIN D→D ~2 days; ABC→D ~5 days; camera experiments ~3 days","trainingHardware":"8 NVIDIA A100 80GB GPUs","evaluation":"CALVIN D→D/ABC→D and unseen camera calibration settings.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"lrll","name":"Lifelong Robot Library Learning (LRLL)","title":"Lifelong Robot Library Learning: Bootstrapping Composable and Generalizable Skills for Embodied Control with Language Models","date":"2024-06-26","year":2024,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Alternates human-guided exploration with skill abstraction to compress successful experiences into a growing composable robot-program library.","institution":"University of Groningen","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2406.18746","project":"https://gtziafas.github.io/LRLL_project/"},"policy":{"family":"Robot agent","baseModel":"GPT-3.5-turbo for language calls; text-embedding-ada-002 for memory; MDETR / CLIP provide frozen perception.","modelSize":"Not reported","inputs":"Task demonstrations or human hints, current observations, retrieved instruction–code experiences and the current skill library.","outputs":"Proposed exploration tasks, executable robot policy code and newly abstracted library skills.","trainingData":"Online human guidance and simulated exploration; an experience store is compressed into callable skills in the sleep phase.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen LLM; wake-phase exploration and memory accumulation alternate with sleep-phase skill abstraction / library extension.","evaluation":"Visual reasoning and rearrangement curricula in simulation, with physical robot transfer."},"release":{"code":"Not released","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The project page is marked under construction and its code button links the literal YOUR REPO HERE placeholder; no released implementation is established."},"evidence":[{"url":"https://arxiv.org/abs/2406.18746","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://gtziafas.github.io/LRLL_project/","label":"Official project release status","fields":["release.code","release.notes"],"note":"The author project page marks the implementation as coming soon or links a placeholder rather than a released implementation."}],"caveats":["A supplied initial vision / action primitive library is required.","Library growth changes external programs and memory, not the LLM parameters."],"verified":"2026-10-17"},{"id":"ical","name":"In-Context Abstraction Learning (ICAL)","title":"VLM Agents Generate Their Own Memories: Distilling Experience into Embodied Programs of Thought","date":"2024-06-20","year":2024,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Refines noisy demonstrations into reusable multimodal programs of thought, using human feedback and retrieval to improve embodied task execution.","institution":"Carnegie Mellon University; Google DeepMind","venue":"NeurIPS 2024","modalities":["RGB","Language"],"environments":["Simulation"],"embodiments":["Simulated household agent"],"links":{"paper":"https://arxiv.org/abs/2406.14596","project":"https://ical-learning.github.io/","code":"https://github.com/Gabesarch/ICAL"},"policy":{"family":"Robot agent","baseModel":"GPT-4-1106-preview and GPT-4-1106-vision-preview; text-embedding-ada-002 retrieval. GPT-3.5 and Qwen2-VL fine-tuning are separate variants.","modelSize":"Not reported","inputs":"Task dialogue, RGB observations, noisy demonstration trajectories, retrieved abstractions and human feedback.","outputs":"Programs of thought and discrete household interaction actions through the TEACh execution interface.","trainingData":"250 noisy TEACh trajectories; 122 successfully abstracted examples support the reported GPT-3.5 fine-tuning experiment. Other domains use separate datasets.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen-VLM reflection / example refinement with retrieval is one condition; supervised fine-tuning on the resulting examples is another.","evaluation":"TEACh household object interaction; VisualWebArena and Ego4D are separately reported non-robot domains."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact. The paper includes trained component / policy variants; a complete method-specific checkpoint release has not been established."},"evidence":[{"url":"https://arxiv.org/abs/2406.14596","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/Gabesarch/ICAL/main/README.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."}],"caveats":["TEACh uses discrete household-agent actions; results do not establish continuous arm positioning or contact precision.","Web-agent and action-forecasting scores are not manipulation-policy success rates."],"verified":"2026-10-17"},{"id":"t3","name":"T3 / Foundation Tactile","title":"Transferable Tactile Transformers for Representation Learning Across Diverse Sensors and Tasks","date":"2024-06-19","year":2024,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Learns shared tactile features with sensor-specific encoders and task-specific heads, including downstream sub-millimeter electronics insertion.","institution":"MIT CSAIL","venue":"CoRL 2024","modalities":["Tactile"],"environments":["Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2406.13640","project":"https://t3.alanz.info/","code":"https://github.com/alanzjl/t3","data":"https://huggingface.co/datasets/alanz-mit/FoundationTactile","weights":"https://huggingface.co/datasets/alanz-mit/FoundationTactile"},"model":{"role":"Reusable representation or perception model; downstream controllers are separate systems","family":"Tactile encoder","baseModel":"Transformer sensor encoders + shared transformer trunk + task-specific reconstruction/regression/classification decoders","modelSize":"Tiny 12M / Small 45M / Medium 174M / Large 308M, including all encoders and decoders (paper Table 1)","inputs":"Optical tactile images from one or more sensors; sensor-specific frontend","outputs":"Shared latent tactile tokens and task-specific perception outputs; a separately trained insertion controller consumes the encoder","trainingMethod":"Stage I masked reconstruction; Stage II multitask supervised objectives distilled from existing datasets; optional downstream fine-tuning","trainingData":"FoTa aggregate: over 3 million data points, 13 sensors and 11 tasks; not every FoTa task is used in T3 pretraining","trainingTime":"Not reported","trainingHardware":"Not reported","inference":"Not reported","evaluation":"Cross-sensor/task prediction, fine-tuning data budgets and scaling; downstream insertion of three electronics parts, with separate policy training."},"release":{"code":"Available","weights":"Available — official download link","data":"Available — official resource link","license":"MIT code LICENSE; constituent FoTa datasets retain source-specific terms","notes":"Code, pretrained encoders and downstream task data are separate artifacts; no policy checkpoint is implied by an encoder release."},"evidence":[{"url":"https://arxiv.org/abs/2406.13640","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Method, experiments and implementation sections; specifications retain component and evaluation scope."},{"url":"https://t3.alanz.info/","label":"Official project","fields":["links","release"],"note":"Author project; author-reported release scope is distinguished from an executed reproduction."},{"url":"https://github.com/alanzjl/t3","label":"Official implementation and current release instructions","fields":["release","links"],"note":"README and repository files inspected on 2026-10-17."},{"url":"https://github.com/alanzjl/t3/blob/main/LICENSE","label":"Repository license file","fields":["release.license"],"note":"Applies to repository scope; model/data/base-model terms can differ."}],"caveats":["FoTa sensor/task totals describe heterogeneous source data, not 11 closed-loop manipulation environments.","Reported parameter counts include sensor encoders and task decoders; they are not shared-trunk-only counts."],"relatedIds":["sparsh","htt","unit"],"verified":"2026-10-17"},{"id":"imagination-policy","name":"Imagination Policy","title":"Imagination Policy: Using Generative Point Cloud Models for Learning Manipulation Policies","date":"2024-06-17","year":2024,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Generates desired object point clouds and converts their rigid transformation into precise keyframe actions.","institution":"Northeastern University; Worcester Polytechnic Institute; Brown University; Boston Dynamics AI Institute","venue":"CoRL 2024","modalities":["RGB","Depth","Point cloud","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2406.11740","project":"https://haojhuang.github.io/imagine_page/","code":"https://github.com/HaojHuang/imagination-policy-cor24"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT for repository code; pretrained models, data and figure rights remain separate","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2406.11740","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://haojhuang.github.io/imagine_page/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/HaojHuang/imagination-policy-cor24","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/HaojHuang/imagination-policy-cor24/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/HaojHuang/imagination-policy-cor24/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"6704 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["Object/target segmentation and downstream action execution are separate from the point generator."],"verified":"2026-10-17","policy":{"family":"Flow matching","baseModel":"PVCNN point encoders, CLIP language encoder and rectified-flow point generation.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Segmented RGB-D point clouds, language and gripper geometry.","outputs":"Rigidly estimated pick/place keyframe poses.","trainingMethod":"Task-conditioned local point generation rather than direct action regression.","trainingData":"Few-demonstration RLBench/real pick-place datasets; exact subsets follow original protocol.","trainingTime":"200K training steps, approximately seven hours","trainingHardware":"Single RTX 4090","evaluation":"RLBench precision tasks including charger/knife insertion and real manipulation.","controlRate":"Approximately 20 s per generated batch with 1,000 integration steps in reported implementation."}},{"id":"sgrv2","name":"SGRv2","title":"Leveraging Locality to Boost Sample Efficiency in Robotic Manipulation","date":"2024-06-15","year":2024,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Local geometric action prediction improves sample efficiency for both keyframe and dense RGB-D control.","institution":"Tsinghua University; Shanghai AI Laboratory; Shanghai Qi Zhi Institute","venue":"CoRL 2024","modalities":["RGB","Depth","Point cloud","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2406.10615","project":"https://sgrv2-robot.github.io/","code":"https://github.com/TongZhangTHU/sgr"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Apache-2.0 for repository code; pretrained models, data and figure rights remain separate","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2406.10615","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://sgrv2-robot.github.io/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/TongZhangTHU/sgr","label":"Official current implementation linked by author project","fields":["release"],"note":"Actual runnable release and model artifacts checked independently."},{"url":"https://github.com/TongZhangTHU/sgr/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/TongZhangTHU/sgr/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"75 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["Keyframe and dense control protocols have different action spaces; demonstration counts are setting-specific."],"verified":"2026-10-17","policy":{"family":"Transformer / BC","baseModel":"SGR CLIP/PointNeXt fusion plus locality-aware per-point action aggregation.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Single/multi-view RGB-D and proprioception; language in language-conditioned settings.","outputs":"Sparse 6-DoF keyframes or dense delta-pose/gripper actions according to benchmark.","trainingMethod":"Behavior cloning with action-locality inductive bias.","trainingData":"RLBench: 26 tasks, 5 demos each. ManiSkill2: 4 tasks; MimicGen: 7 tasks, generally 50 demos each. Low-data real settings: 8 demos.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"RLBench, ManiSkill2, MimicGen and real manipulation.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"openvla","name":"OpenVLA","title":"OpenVLA: An Open-Source Vision-Language-Action Model","date":"2024-06-13","year":2024,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Open 7 B VLA combines a pretrained multimodal backbone with autoregressive robot action tokens.","institution":"Stanford University / UC Berkeley","venue":"CoRL 2024","modalities":["RGB","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2406.09246","project":"https://openvla.github.io/","code":"https://github.com/openvla/openvla","weights":"https://huggingface.co/openvla/openvla-7b"},"policy":{"family":"VLA","baseModel":"Prismatic: DINOv2 + SigLIP visual encoder, Llama-2 language model","modelSize":"7B class (~7.5B including visual components in OFT comparison)","inputs":"Single RGB image and natural-language instruction in original base","outputs":"Seven-dimensional end-effector delta/rotation/gripper actions, discretized into tokens","trainingData":"970k real robot episodes from OXE; downstream 10–150 demos/task in original fine-tuning evaluation","trainingTime":"Paper: 14 days pretraining (~21,500 A100-hours); project rounds to 15 days","trainingHardware":"64 A100 GPUs; LoRA possible on one 27–80 GB GPU","controlRate":"Approximately 6 Hz inference on one RTX 4090; robot tests use setup-specific rates","trainingMethod":"Autoregressive token behavior cloning; full/LoRA fine-tuning","evaluation":"WidowX BridgeData, Google robot and seven Franka fine-tuning tasks"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"MIT (code); pretrained models inherit Llama Community License; dataset terms are source-specific","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2406.09246","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/openvla/openvla","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://openvla.github.io/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/openvla/openvla/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["Original base does not take measured depth, tactile, joint state or multi-camera observations.","The OFT successor changes the input/output and inference protocol; use its separate entry for those results."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Not verified","license":"MIT (code); pretrained models inherit Llama Community License; dataset terms are source-specific","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"rvt2","name":"RVT-2","title":"RVT-2: Learning Precise Manipulation from Few Demonstrations","date":"2024-06-12","year":2024,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Adds coarse-to-fine virtual-view zooming and location-conditioned rotation to improve precise manipulation from few demonstrations.","institution":"NVIDIA","venue":"RSS 2024","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2406.08545","project":"https://robotic-view-transformer-2.github.io/","code":"https://github.com/NVlabs/RVT","weights":"https://huggingface.co/ankgoyal/rvt/tree/main/rvt2"},"policy":{"family":"Other","baseModel":"Two-stage RVT multi-view Transformer; convex upsampling","modelSize":"Not reported","inputs":"RGB-D point cloud, instruction and robot/gripper state","outputs":"Fine 6-DoF keyposes, gripper state and collision flag, executed by planner","trainingData":"100 demos each for 18 RLBench tasks; roughly 10 demos per real precision task","trainingTime":"Approximately 20 h, 83.3k steps, batch 192 (RLBench)","trainingHardware":"8 V100 16 GB GPUs","controlRate":"20.6 FPS model inference on RTX 3090; physical planner/control rate separate","trainingMethod":"Keypose behavior cloning with mixed precision, 8-bit LAMB and SE(3) augmentation","evaluation":"18 RLBench tasks; real peg/plug insertion and other precision tasks"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"NVIDIA custom license (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2406.08545","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/NVlabs/RVT","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://robotic-view-transformer-2.github.io/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/NVlabs/RVT/master/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["Millimeter-level tasks are demonstrated with RGB-D; tactile is not an input.","Coarse-to-fine keyposes depend on camera geometry and downstream planning."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Not verified","license":"NVIDIA custom license (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"proc3s","name":"PRoC3S","title":"Trust the PRoC3S: Solving Long-Horizon Robotics Problems with LLMs and Constraint Satisfaction","date":"2024-06-08","year":2024,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Prompts an LLM to synthesize programs with open continuous parameters, then solves their geometric / kinematic constraints and replans infeasible specifications.","institution":"Massachusetts Institute of Technology","venue":"CoRL 2024","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2406.05572","code":"https://github.com/Learning-and-Intelligent-Systems/proc3s"},"policy":{"family":"Hybrid","baseModel":"GPT-4 gpt-4-0125-preview with a continuous constraint-satisfaction solver over parameterized robot skills.","modelSize":"Not reported","inputs":"Task goal, object-centric state, an example language-model program and available skill / constraint specifications.","outputs":"A skill sequence and continuous parameter values obtained through sampling or optimization; infeasible specifications trigger a revised program.","trainingData":"Prompt examples and constraint / rollout feedback; no target-task language-model weight training.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen LLM generates CCSP-form programs; analytic sampling / optimization enforces constraints before execution.","evaluation":"Three simulated 3D domains plus real-robot task variants in the revised paper."},"release":{"code":"Available","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms."},"evidence":[{"url":"https://arxiv.org/abs/2406.05572","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/Learning-and-Intelligent-Systems/proc3s/main/README.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."}],"caveats":["The first arXiv submission is June 2024 despite the PMLR proceedings volume being published in 2025.","The planner depends on explicit state / constraints and a simulator or constraint checker; it is not direct RGB-only action prediction."],"verified":"2026-10-17"},{"id":"tlv-link","name":"TLV-Link / Touch100k","title":"Touch100k: A Large-Scale Touch-Language-Vision Dataset for Touch-Centric Multimodal Representation","date":"2024-06-06","year":2024,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Uses curriculum linking between touch, multigranular language and vision, accompanying the Touch100k tactile description dataset.","institution":"Beijing Jiaotong University; WeChat AI, Tencent","venue":"Information Fusion 2025","modalities":["Tactile","RGB","Language"],"environments":["Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2406.03813","project":"https://cocacola-lab.github.io/Touch100k/","code":"https://github.com/cocacola-lab/TLV-Link","data":"https://drive.google.com/drive/folders/1QOvbkIZtpJpz4Ry_Zg3ouXX-zLJNqu9m?usp=sharing"},"model":{"role":"Reusable representation or perception model; downstream controllers are separate systems","family":"Multimodal representation","baseModel":"CLIP-style image/touch branches and frozen text encoder; sentence- then phrase-level curriculum","modelSize":"Not reported","inputs":"GelSight tactile image; paired RGB and sentence/phrase tactile descriptions during training","outputs":"Shared tactile/visual/language embedding for retrieval and property/grasp recognition","trainingMethod":"Two-stage curriculum linking from sentence-level semantics to phrase-level tactile detail","trainingData":"Touch100k: paired touch–language–vision samples at approximately 100K scale, with sentence- and phrase-level annotations.","trainingTime":"12 epochs; wall-clock duration not reported","trainingHardware":"2 NVIDIA A40 GPUs; batch size 96","inference":"Not reported","evaluation":"Material/property recognition and grasp-success classification; zero-shot tactile understanding and data-scale ablations."},"release":{"code":"Available — training, evaluation and model source present in the official repository","weights":"Not verified","data":"Available — official resource link","license":"MIT code LICENSE; the official README states Touch100k CC BY-NC 4.0 and research-only use of models trained on that data.","notes":"Code, pretrained encoders and downstream task data are separate artifacts; no policy checkpoint is implied by an encoder release."},"evidence":[{"url":"https://arxiv.org/abs/2406.03813","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Method, experiments and implementation sections; specifications retain component and evaluation scope."},{"url":"https://cocacola-lab.github.io/Touch100k/","label":"Official project","fields":["links","release"],"note":"Author project; author-reported release scope is distinguished from an executed reproduction."},{"url":"https://github.com/cocacola-lab/TLV-Link","label":"Official implementation and current release instructions","fields":["release","links"],"note":"README and repository files inspected on 2026-10-17."},{"url":"https://github.com/cocacola-lab/TLV-Link/blob/main/LICENSE","label":"Repository license file","fields":["release.license"],"note":"Applies to repository scope; model/data/base-model terms can differ."}],"caveats":["Touch100k counts multimodal samples, rather than 100K independent robot demonstrations.","Code and dataset terms differ; the official data notice limits downstream models trained on Touch100k to research use."],"relatedIds":["unitouch","anytouch","tvl"],"verified":"2026-10-17"},{"id":"dreureka","name":"DrEureka","title":"DrEureka: Language Model Guided Sim-To-Real Transfer","date":"2024-06-04","year":2024,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Generates reward functions and domain-randomization configurations with an LLM to support sim-to-real reinforcement learning.","institution":"Not reported","venue":"Preprint","modalities":["Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand","Legged robot"],"links":{"paper":"https://arxiv.org/abs/2406.01967","project":"https://eureka-research.github.io/dr-eureka","code":"https://github.com/eureka-research/DrEureka"},"policy":{"family":"RL","baseModel":"GPT-4 for reward / domain-randomization design; task-specific reinforcement-learning policies.","modelSize":"Not reported","inputs":"Design stage: task, simulator code and reward-aware physics priors. LEAP-hand deployment uses 16 joint angles and proprioceptive history, without cube pose.","outputs":"Reward code and domain-randomization parameter distributions; trained motor policies execute the resulting skills.","trainingData":"Simulated RL rollouts under 16 generated randomization configurations, followed by physical policy evaluation.","trainingTime":"The paper reports an approximately three-hour DrEureka workflow in its runtime comparison, versus ten / twenty hours for CEM / BayRn.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen LLM generates rewards and randomization; RL updates task-policy weights before zero-shot sim-to-real deployment.","evaluation":"Quadruped locomotion and LEAP-hand in-hand cube rotation, including a quadruped balancing task."},"release":{"code":"Partial — generation framework and locomotion environments","weights":"Not applicable","data":"Not verified","license":"MIT","notes":"Repository releases reward/domain-randomization generation with forward-locomotion and globe-walking environments. This does not verify a released dexterous-manipulation environment."},"evidence":[{"url":"https://arxiv.org/abs/2406.01967","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/eureka-research/DrEureka","label":"Official implementation, README and file/license inspection on 19 October 2026","fields":["links","release"],"note":"Repository releases reward/domain-randomization generation with forward-locomotion and globe-walking environments. This does not verify a released dexterous-manipulation environment. MIT."}],"caveats":["The manipulation hand is LEAP, not Allegro.","The in-hand policy does not observe cube pose; it uses proprioception rather than RGB-D or tactile images."],"verified":"2026-10-19"},{"id":"orion","name":"ORION","title":"Vision-based Manipulation from Single Human Video with Open-World Object Graphs","date":"2024-05-30","year":2024,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Extracts open-world object-graph plans from a single human video and grounds object-centric interaction geometry to execute the task in a new scene.","institution":"University of Texas at Austin; Sony AI","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2405.20321","project":"https://ut-austin-rpl.github.io/ORION-release"},"policy":{"family":"Hybrid","baseModel":"GPT-4o-assisted object identification, visual tracking / segmentation and object-centric geometric registration; analytic reaching / interaction primitives.","modelSize":"Not reported","inputs":"One RGB or RGB-D human demonstration, object descriptions and deployment RGB-D from a RealSense camera, plus robot state.","outputs":"Object-centric grasp / interaction targets derived from the matched open-world object graph, executed on a Franka Panda.","trainingData":"One human demonstration per task; no task-specific learned motor-policy training is required by the geometric transfer pipeline.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen foundation models and geometric plan transfer; object registration / correspondence grounds the demonstration in the deployment scene.","evaluation":"Seven real-world tasks, with short / long horizons, RGB-D and RGB-only demonstration variants, and varied backgrounds / objects / viewpoints."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The original paper is public; code, model and dataset release scopes are verified separately where established. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms."},"evidence":[{"url":"https://arxiv.org/abs/2405.20321","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."}],"caveats":["RGB-only refers to the demonstration video; robot deployment still uses RGB-D.","The robot resets to a default joint pose before object interactions to obtain an unoccluded observation."],"verified":"2026-10-17"},{"id":"imop","name":"IMOP","title":"One-Shot Imitation Learning with Invariance Matching for Robotic Manipulation","date":"2024-05-21","year":2024,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Learned invariant-region correspondence and state routing transfer a demonstrated end-effector pose to new task scenes.","institution":"Rutgers University","venue":"RSS 2024","modalities":["RGB","Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2405.13178","project":"https://mlzxy.github.io/imop/","code":"https://github.com/mlzxy/imop"},"policy":{"family":"Geometric transfer","baseModel":"Invariance-matching one-shot manipulation policy. Point-cloud invariant-region estimator/matcher and state-routing network with correspondence-based pose regression.","modelSize":"80.8M parameters (paper Table III); compared RVT+FT 104M and RVT+HDT 131M are separate baselines.","inputs":"Current scene point cloud and one-shot point-cloud/action demonstration; colored geometry comes from RGB-D cameras.","outputs":"Next end-effector pose and gripper action via correspondence regression and motion planning.","trainingData":"18 RLBench base tasks with 100 demonstrations each; novel tasks receive one demonstration each. Simulator instance masks construct invariant-region supervision.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Offline supervised region/routing learning; frozen one-shot correspondence transfer to novel tasks.","evaluation":"Eighteen base and twenty-two novel RLBench tasks, five real tasks and a separate grocery shape-transfer study."},"release":{"code":"Available","weights":"Available","data":"Partial","license":"No repository-wide license found in inspected tree","notes":"Code, data and weights public; README explicitly documents simplified state routing and privileged invariant-region labels in the released evaluation."},"evidence":[{"url":"https://arxiv.org/abs/2405.13178","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://mlzxy.github.io/imop/","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://github.com/mlzxy/imop","label":"Official code","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."}],"caveats":["The grocery shape-generalization experiment assumes access to the required object segmentation mask.","A simulated-to-real threshold change is reported for invariant-region predictions; zero task fine-tuning does not mean zero deployment adjustment.","The released implementation removes learned state routing and directly uses RLBench instance mask names to obtain ground-truth invariant regions, with task heuristics; released-code inputs differ from the paper formulation."],"verified":"2026-10-17"},{"id":"octo","name":"Octo","title":"Octo: An Open-Source Generalist Robot Policy","date":"2024-05-20","year":2024,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Open generalist diffusion policy supports language/goal conditioning and data-efficient adaptation to new robot inputs and actions.","institution":"UC Berkeley","venue":"RSS 2024","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2405.12213","project":"https://octo-models.github.io/","code":"https://github.com/octo-models/octo","weights":"https://huggingface.co/rail-berkeley/octo-base-1.5"},"policy":{"family":"Diffusion","baseModel":"Transformer with modular observation tokenizers and diffusion action head","modelSize":"Octo-Small 27M / Octo-Base 93M parameters","inputs":"RGB history, proprioception when configured, language or goal image","outputs":"Four-step action chunks; action dimension/controller adapted per robot","trainingData":"800k trajectories from 25 OXE datasets; fine-tuning evaluations ~100 demonstrations per domain","trainingTime":"Base pretraining 14 h; single-GPU fine-tuning approximately 5 h","trainingHardware":"TPU v4-128 pod pretraining; NVIDIA A5000 24 GB fine-tuning","controlRate":"Robot-specific: e.g. Franka DROID 15 Hz; no universal controller frequency","trainingMethod":"Multirobot diffusion behavior cloning, then downstream fine-tuning","evaluation":"Nine real robot setups across four institutions; zero-shot WidowX/Google robot and precision fine-tuning tasks"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2405.12213","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/octo-models/octo","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://octo-models.github.io/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/octo-models/octo/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["Changing sensing/action spaces requires tokenizer/head adaptation and target demonstrations.","Generalist pretraining size does not establish precision-contact coverage."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Not verified","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"anyrotate","name":"AnyRotate","title":"AnyRotate: Gravity-Invariant In-Hand Object Rotation with Sim-to-Real Touch","date":"2024-05-12","year":2024,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"Uses dense tactile contact features and simulation learning for gravity-invariant multi-axis in-hand rotation.","institution":"University of Bristol","venue":"CoRL 2024","modalities":["Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2405.07391","project":"https://maxyang27896.github.io/anyrotate/"},"policy":{"family":"RL","baseModel":"Simulation-trained dense tactile RL policy with sim-to-real tactile perception","modelSize":"Not reported","inputs":"Dense tactile contact features and robot proprioception","outputs":"Dexterous-hand joint commands","trainingData":"Simulation RL over objects/hand orientations; count not reported","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Rich tactile simulation-to-real transfer and orientation randomization","evaluation":"Unseen objects, arbitrary rotation axes and varying hand directions"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."},"evidence":[{"url":"https://arxiv.org/abs/2405.07391","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://maxyang27896.github.io/anyrotate/","label":"Author project page","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2405.07391","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Dense tactile features are not equivalent to externally measured object pose."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."}},"relatedIds":[]},{"id":"screwmimic","name":"ScrewMimic","title":"ScrewMimic: Bimanual Imitation from Human Videos with Screw Space Projection","date":"2024-05-06","year":2024,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"A human bimanual video initializes screw-axis actions that are refined through force-informed robot interaction.","institution":"University of Texas at Austin","venue":"RSS 2024","modalities":["RGB","Depth","Proprioception","Force/Torque"],"environments":["Real world"],"embodiments":["Bimanual"],"links":{"paper":"https://arxiv.org/abs/2405.03666","project":"https://robin-lab.cs.utexas.edu/ScrewMimic/","code":"https://github.com/UT-Austin-RobIn/ScrewMimic"},"policy":{"family":"Geometric transfer","baseModel":"Bimanual screw-action imitation and interaction refinement. Hand tracking, PointNet screw-axis/grasp-contact models and cross-entropy action-parameter optimization.","modelSize":"Not reported","inputs":"One human RGB-D demonstration, object point cloud and hand trajectories; robot refinement uses right-wrist force/torque and episode progress.","outputs":"Grasp contact locations and synchronized screw-action trajectories for both robot arms.","trainingData":"One demonstration seeds screw parameters; PointNet fitting uses demonstrated contact/screw labels and geometric variation. Interaction refinement explores five episodes per epoch.","trainingTime":"PointNet fitting 2,000 epochs; CEM evaluations allot five epochs of five episodes for selected comparison tasks.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Estimate relative hand screw motion, train geometric predictors, then refine compact screw action with self-generated force/episode-length reward.","evaluation":"Complex real bimanual behaviors including opening a bottle, inserting a roll and closing a laptop; additional screwing/cutting/stirring demonstrations."},"release":{"code":"Partial","weights":"Not verified","data":"Partial","license":"No repository-wide license found in inspected tree","notes":"Author repository includes demonstration data and scripts; README references perception/model paths whose complete release is not established."},"evidence":[{"url":"https://arxiv.org/abs/2405.03666","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://robin-lab.cs.utexas.edu/ScrewMimic/","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://github.com/UT-Austin-RobIn/ScrewMimic","label":"Official code","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."}],"caveats":["The measured contact signal is a wrist force/torque sensor, not an image-based tactile skin.","Includes robot interaction and parameter refinement; not pure frozen one-video execution."],"verified":"2026-10-17"},{"id":"octopi","name":"Octopi / PhysiCLeAR","title":"Octopi: Object Property Reasoning with Large Tactile-Language Models","date":"2024-05-05","year":2024,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Connects tactile video representations to a language model for physical-property description and scenario reasoning.","institution":"National University of Singapore; University of Washington","venue":"RSS 2024","modalities":["Tactile","Language"],"environments":["Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2405.02794","project":"https://github.com/clear-nus/octopi","code":"https://github.com/clear-nus/octopi","weights":"https://drive.google.com/drive/folders/1AdLCaD_5CiNvsm_cm6IT7KDeXgZVSQlY?usp=sharing"},"model":{"role":"Reusable representation or perception model; downstream controllers are separate systems","family":"Tactile-language model","baseModel":"CLIP-based tactile video encoder + two-layer projection + Vicuna 7B/13B","modelSize":"Vicuna 7B and 13B language backbones; tactile/projection parameters additional","inputs":"GelSight tactile videos and natural-language prompt","outputs":"Physical-property labels/descriptions and scenario-reasoning text","trainingMethod":"Tactile encoder fine-tuning, tactile-feature alignment, and LoRA/end-to-end language adaptation","trainingData":"PhysiCLeAR tactile videos and hardness/roughness/bumpiness labels; pressing/rotation exploratory procedures; 8K alignment samples reported","trainingTime":"Encoder fine-tuning 6h; alignment + end-to-end adaptation 5h (7B) /6.5h (13B)","trainingHardware":"1 NVIDIA RTX A6000 (7B), 2 RTX A6000 (13B); encoder reported <5GB VRAM","inference":"Not reported","evaluation":"Physical-property understanding, object description, comparison and scenario reasoning; no action-generating robot controller."},"release":{"code":"Available","weights":"Available — official download link","data":"Not verified — dataset release not independently confirmed for the original paper snapshot","license":"No standalone top-level repository LICENSE identified; component and Vicuna/LLaMA terms remain separate","notes":"Code, pretrained encoders and downstream task data are separate artifacts; no policy checkpoint is implied by an encoder release."},"evidence":[{"url":"https://arxiv.org/abs/2405.02794","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Method, experiments and implementation sections; specifications retain component and evaluation scope."},{"url":"https://github.com/clear-nus/octopi","label":"Official project","fields":["links","release"],"note":"Author project; author-reported release scope is distinguished from an executed reproduction."},{"url":"https://github.com/clear-nus/octopi","label":"Official implementation and current release instructions","fields":["release","links"],"note":"README and repository files inspected on 2026-10-17."}],"caveats":["The CLIP-like tactile encoder is fed tactile videos, rather than a measured scene-depth stream.","Reasoning about how to manipulate an object does not itself provide an executable manipulation policy."],"relatedIds":["octopi-15","tvl"],"verified":"2026-10-17"},{"id":"dreamer-tactile-insertion","name":"Dreamer Tactile Insertion","title":"Learning Tactile Insertion in the Real World","date":"2024-05-01","year":2024,"collections":["policy"],"categories":["tactile-foundation","visual-precision-rl"],"resourceType":"policy","summary":"Dreamer-v 3 learns a tactile insertion controller from scratch directly on a physical robot.","institution":"Technical University of Darmstadt","venue":"Preprint","modalities":["Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2405.00383"},"policy":{"family":"RL","baseModel":"Dreamer-v 3 latent world model and actor–critic.","modelSize":"Not reported","inputs":"64×64 Gel Sight Mini image and 3 Dend-effector position; no external vision required.","outputs":"Relative end-effector position targets.","trainingData":"Autonomous real/simulation interaction with reset procedure; exact step budget Not reported here.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Tactile/state observations 25 Hz, policy position targets 20 Hz.","trainingMethod":"Model-based RL from scratch with restricted work space and autonomous resets.","evaluation":"Franka Research 3 tactile peg-in-hole insertion under partial observability."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2405.00383","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."}],"caveats":["Safety work space and reset infrastructure are essential experimental conditions, not a general unconstrained RL system."],"verified":"2026-10-17"},{"id":"hato","name":"HATO","title":"Learning Visuotactile Skills with Two Multifingered Hands","date":"2024-04-25","year":2024,"collections":["policy"],"categories":["tactile-il","rgbd-3d"],"resourceType":"policy","summary":"Learns two-hand dexterous skills from teleoperation using visual, depth, pressure and proprioceptive observations.","institution":"UC Berkeley","venue":"ICRA 2025","modalities":["RGB","Depth","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2404.16823","project":"https://toruowo.github.io/hato/","code":"https://github.com/ToruOwO/hato","data":"https://berkeley.app.box.com/s/379cf57zqm1akvr00vdcloxqxi3ucb9g"},"policy":{"family":"Diffusion","baseModel":"Diffusion Policy with separate RGB/depth/tactile encoders","modelSize":"Not reported","inputs":"Multi-view RGB, depth images, finger pressure readings and robot state","outputs":"Two-arm/two-hand actions","trainingData":"100 demos each for Slippery Handover/Block Stacking; 300 each for Wine Pouring/Steak Serving","trainingTime":"Demonstration acquisition ≈30 min–2 h per task; policy training wall-clock not reported","trainingHardware":"Not reported","controlRate":"Data collection 10 Hz (official code example)","trainingMethod":"End-to-end diffusion imitation from HATO teleoperation","evaluation":"Slippery Handover, Tower Block Stacking, Wine Pouring, Steak Serving"},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"Not verified","notes":"Official repo covers collection, training and deployment and links task data."},"evidence":[{"url":"https://arxiv.org/abs/2404.16823","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/ToruOwO/hato","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2404.16823","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Pressure sensors on Ability hands differ from optical tactile sensors."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Available","license":"Not verified","notes":"Official repo covers collection, training and deployment and links task data."}},"relatedIds":[]},{"id":"rise","name":"RISE","title":"RISE: 3D Perception Makes Real-World Robot Imitation Simple and Effective","date":"2024-04-18","year":2024,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Uses sparse 3 D convolution and a diffusion decoder to predict continuous manipulation actions from a single-view point cloud.","institution":"Shanghai Noematrix / Shanghai Jiao Tong University","venue":"IROS 2024","modalities":["RGB","Depth"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2404.12281","project":"https://rise-policy.github.io/","code":"https://github.com/rise-policy/RISE","data":"https://github.com/rise-policy/RISE#dataset"},"policy":{"family":"Diffusion","baseModel":"Sparse 3D convolution + Transformer encoder/decoder + diffusion head","modelSize":"Not reported","inputs":"Single-view RGB-D point cloud in camera coordinates","outputs":"20-step continuous end-effector pose/gripper action horizon","trainingData":"50 demonstrations per real task; six tasks in four families","trainingTime":"Not reported","trainingHardware":"2 A100 GPUs, batch 240; RTX 3090 deployment workstation","controlRate":"Not reported","trainingMethod":"Diffusion behavior cloning, 100 training/20 inference diffusion steps","evaluation":"Collect Cups, Collect Pens, Pour Balls, Push Block, Push Ball, Stack Blocks"},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"CC-BY-NC-SA-4.0 (RISE code); dependency licenses separately apply; data/weights terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2404.12281","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/rise-policy/RISE","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://rise-policy.github.io/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/rise-policy/RISE/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["Policy observes camera-frame point clouds; physical calibration and robot action conversion still matter.","Current LICENSE begins CC-BY-NC-SA-4.0; third-party license text also appears in the file."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Available","license":"CC-BY-NC-SA-4.0 (RISE code); dependency licenses separately apply; data/weights terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"keypoint-action-tokens","name":"Keypoint Action Tokens (KAT)","title":"Keypoint Action Tokens Enable In-Context Imitation Learning in Robotics","date":"2024-03-28","year":2024,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Represents visual scenes and demonstrated actions as 3D keypoint / action tokens so an LLM can imitate manipulation from in-context examples.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2403.19578","project":"https://www.robot-learning.uk/keypoint-action-tokens"},"policy":{"family":"Robot agent","baseModel":"GPT-4 Turbo in the main system; other language-model backbones are evaluated separately.","modelSize":"Not reported","inputs":"Calibrated RGB-D-derived 3D visual keypoints, robot state and demonstration action-token sequences.","outputs":"Textual action tokens decoded into end-effector motion and gripper commands.","trainingData":"Ten demonstration trajectories per task in the reported nine-task real-world study; no target-task LLM weight fine-tuning.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Demonstration end-effector poses are recorded at 4 Hz; this is not a measured LLM API query rate.","trainingMethod":"Frozen language model; keypoint and action tokenization provide a compact in-context imitation interface.","evaluation":"Nine real-world manipulation tasks, with ten test trials per task and tokenization / backbone ablations."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2403.19578","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["3D keypoints require the stated visual / geometric preprocessing and calibration.","The 4 Hz demonstration recording rate should not be presented as language-model inference throughput."],"verified":"2026-10-17"},{"id":"ditto","name":"DITTO","title":"DITTO: Demonstration Imitation by Trajectory Transformation","date":"2024-03-22","year":2024,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Object-relative motion extracted from one human RGB-D video is transformed to a new scene and executed with grasping and motion planning.","institution":"University of Freiburg","venue":"IROS 2024","modalities":["RGB","Depth","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2403.15203","project":"https://ditto.cs.uni-freiburg.de/","code":"https://github.com/robot-learning-freiburg/DITTO"},"policy":{"family":"Geometric transfer","baseModel":"Human-demo object trajectory transformation. Hands23/SAM segmentation, LoFTR/CNOS correspondence and re-detection, Contact-GraspNet and motion planning.","modelSize":"Not reported","inputs":"Single static-camera human RGB-D video; current RGB-D scene, object/secondary-object masks and calibrated robot geometry.","outputs":"Warped object-relative trajectory, selected grasp and feasible robot joint trajectory.","trainingData":"Uses off-the-shelf pretrained modules; no task-specific retraining. Evaluation dataset has ten tasks, normally five demonstrations each.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Offline segmentation/3D trajectory extraction followed by online re-detection, pose transfer, grasp selection and planning.","evaluation":"Ten tabletop/articulated-furniture tasks and over 150 executions on a Franka Panda mounted on a mobile base."},"release":{"code":"Partial","weights":"Not verified","data":"Not verified","license":"GPL-3.0 code; dependencies and model/data terms separate","notes":"Trajectory extraction/correspondence code is public; full physical robot execution is not supplied as a complete reproducible system."},"evidence":[{"url":"https://arxiv.org/abs/2403.15203","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://ditto.cs.uni-freiburg.de/","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://github.com/robot-learning-freiburg/DITTO","label":"Official code","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://raw.githubusercontent.com/robot-learning-freiburg/DITTO/master/LICENSE","label":"Actual source license file","fields":["release.license"],"note":"Scope limited to the file/repository subtree carrying these terms, not paper figures or external models/data."}],"caveats":["Demonstration preprocessing includes manual verification of masks.","Author repository mainly exposes trajectory extraction; full robot execution/reproduction requires additional integration.","Successful trajectory warping and successful physical execution are distinct metrics."],"verified":"2026-10-17"},{"id":"vid2robot","name":"Vid2Robot","title":"Vid2Robot: End-to-end Video-conditioned Policy Learning with Cross-Attention Transformers","date":"2024-03-19","year":2024,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A cross-attention policy converts a human or robot demonstration video and current camera observations into robot actions.","institution":"Google DeepMind Robotics","venue":"RSS 2024","modalities":["RGB"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2403.12943","project":"https://vid2robot.github.io"},"policy":{"family":"Transformer policy","baseModel":"ViT-B/16 image encoders, Perceiver resamplers and cross-attention state/prompt/action transformer.","modelSize":"Not reported","inputs":"16-frame demonstration video and eight recent robot RGB frames; task is specified by video.","outputs":"Four-step action prediction; 11-dimensional discretized robot vector includes arm, gripper, mode and mobile-base components. Evaluations keep the base stationary.","trainingData":"Approximately 100k robot videos and 10k human videos; over 90% of paired data is robot-robot.","trainingTime":"200k AdamW iterations, batch size 2,048; wall-clock duration not reported.","trainingHardware":"Not reported","controlRate":"Approximately 5–7 Hz action execution, including policy response latency.","trainingMethod":"Behavior cloning with 256-bin action classification plus temporal / cross-modal contrastive losses.","evaluation":"Real-world manipulation using video prompts, including cross-object motion transfer and chained subtasks."},"release":{"code":"Not released","weights":"Not released","data":"Partial","license":"Not verified","notes":"Official project provides demonstrations and paper; the paper identifies the public robot dataset separately from its complete human-video pairing corpus."},"evidence":[{"url":"https://arxiv.org/abs/2403.12943","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://vid2robot.github.io","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."}],"caveats":["The 11-D output format includes base actions, but the reported experiments use a stationary base."],"verified":"2026-10-17"},{"id":"vital-pretraining","name":"VITaL Pretraining","title":"VITaL Pretraining: Visuo-Tactile Pretraining for Tactile and Non-Tactile Manipulation Policies","date":"2024-03-18","year":2024,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"Visuo-tactile representation pretraining improves both tactile-conditioned and vision-only imitation policies.","institution":"Carnegie Mellon University","venue":"ICRA 2025","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2403.11898","project":"https://sites.google.com/andrew.cmu.edu/visuo-tactile-pretraining","code":"https://github.com/Abraham190137/TactileACT"},"policy":{"family":"ACT","baseModel":"CLIP-style visual/tactile encoders; ACT and Diffusion Policy downstream heads","modelSize":"Not reported","inputs":"Six RGB views, robot state and optional GelSight tactile images","outputs":"End-effector position and gripper action chunks","trainingData":"100 demonstrations each for USB plugging and two block-stacking variants; 80/20 split","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Synchronized recording 10 Hz","trainingMethod":"Contrastive visual–tactile alignment then downstream imitation learning","evaluation":"USB cable plugging, thin-block stacking and cube stacking"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Project links the TactileACT implementation."},"evidence":[{"url":"https://arxiv.org/abs/2403.11898","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/Abraham190137/TactileACT","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2403.11898","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."},{"url":"https://github.com/Abraham190137/TactileACT/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["Some evaluated policies use tactile only during pretraining and receive no touch at deployment.","Distinct from ViTaL local skills (2025)."],"verified":"2026-10-19","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Project links the TactileACT implementation."}},"relatedIds":[]},{"id":"3d-vla","name":"3D-VLA","title":"3D-VLA: A 3D Vision-Language-Action Generative World Model","date":"2024-03-14","year":2024,"collections":["policy"],"categories":["geometry-models","rgbd-3d"],"resourceType":"model","summary":"A 3D language model aligns interaction tokens with goal-image and goal-point-cloud generators.","institution":"UMass Amherst; SJTU; SCUT; Wuhan University; MIT; UCLA; MIT-IBM","venue":"ICML 2024","modalities":["RGB","Depth","Point cloud","Language"],"environments":["Simulation","Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2403.09631","project":"https://vis-www.cs.umass.edu/3dvla/","code":"https://github.com/UMass-Embodied-AGI/3D-VLA"},"release":{"code":"Partial","weights":"Partial","data":"Not verified","license":"Not verified","notes":"Released goal diffusion models; official model card labels unified LLM/full 3D-VLA checkpoints coming soon."},"evidence":[{"url":"https://arxiv.org/abs/2403.09631","label":"Original manuscript and first submission history","fields":["title","date","summary","model","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://vis-www.cs.umass.edu/3dvla/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/UMass-Embodied-AGI/3D-VLA","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/UMass-Embodied-AGI/3D-VLA/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"127 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["Action-token prediction and goal generation do not by themselves demonstrate an executable closed-loop robot policy."],"verified":"2026-10-17","model":{"role":"3D multimodal reasoning, goal generation and action representation","family":"Geometry representation","baseModel":"BLIP2-FlanT5-XL / 3D-LLM-style features and embodied diffusion goal decoders.","modelSize":"Not reported in the inspected primary-source sections","inputs":"3D scene features from multi-view observations plus language; dataset may use measured or estimated depth.","outputs":"Language/localization/action tokens, goal RGB-D images and point clouds.","trainingMethod":"3D instruction tuning and language-to-diffusion alignment.","trainingData":"Approximately 2M 3D language/action data pairs extracted from heterogeneous embodied datasets.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Paper reports 6×32 V100 pretraining and 6×64 V100 alignment configurations; separate goal decoders have their own budgets.","evaluation":"Held-in embodied reasoning, goal generation and offline action prediction; physical closed-loop manipulation is not established.","inference":"Not reported in the inspected primary-source sections"}},{"id":"manigaussian","name":"ManiGaussian","title":"ManiGaussian: Dynamic Gaussian Splatting for Multi-task Robotic Manipulation","date":"2024-03-13","year":2024,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Dynamic Gaussian reconstruction supervises a language-conditioned multi-task keyframe policy.","institution":"Tsinghua University; Carnegie Mellon University; collaborating authors","venue":"ECCV 2024","modalities":["RGB","Depth","Point cloud","Proprioception"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2403.08321","project":"https://guanxinglu.github.io/ManiGaussian/","code":"https://github.com/GuanxingLu/ManiGaussian"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT for repository code; pretrained models, data and figure rights remain separate","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2403.08321","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://guanxinglu.github.io/ManiGaussian/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/GuanxingLu/ManiGaussian","label":"Official current implementation linked by author project","fields":["release"],"note":"Actual runnable release and model artifacts checked independently."},{"url":"https://github.com/GuanxingLu/ManiGaussian/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/GuanxingLu/ManiGaussian/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"11994 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["Future Gaussian reconstruction is training supervision; executed policy actions and reconstruction quality are separate metrics."],"verified":"2026-10-17","policy":{"family":"Transformer / BC","baseModel":"Semantic/geometric Gaussian embeddings, Gaussian world-model reconstruction and PerAct-style action prediction.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Calibrated multi-view RGB-D, language and proprioception.","outputs":"6-DoF end-effector keyframes, gripper and collision state.","trainingMethod":"Imitation learning with future-scene reconstruction auxiliary supervision.","trainingData":"RLBench multi-task demonstrations; ten task families with166variations.","trainingTime":"100K iterations; wall-clock not reported","trainingHardware":"2 NVIDIA RTX 4090 GPUs","evaluation":"Ten RLBench tasks; task variation and held-out scene tests.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"letac-mpc","name":"LeTac-MPC","title":"LeTac-MPC: Learning Model Predictive Control for Tactile-reactive Grasping","date":"2024-03-07","year":2024,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"A learned tactile embedding and differentiable MPC layer provide reactive grasp regulation across object properties.","institution":"Purdue University","venue":"IEEE Transactions on Robotics 2024","modalities":["Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2403.04934","code":"https://github.com/ZhengtongXu/LeTac-MPC","data":"https://github.com/ZhengtongXu/LeTac-MPC/tree/main/LeTac-MPC/dataset"},"policy":{"family":"Model-based planning","baseModel":"Pretrained Res Net 152 tactile encoder, MLP and differentiable MPC output layer.","modelSize":"Not reported","inputs":"Gel Sight tactile images and gripper state.","outputs":"Gripper motions for reactive force/contact regulation.","trainingData":"Automated real tactile grasp interaction data; exact corpus count Not reported in inspected text.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"25 Hz real-time controller.","trainingMethod":"End-to-end tactile feature/differentiable MPC learning.","evaluation":"Dynamic andforce-interactive grasping of daily rigid/deformable objects."},"release":{"code":"Available","weights":"Not verified","data":"Available — bundled tactile-image dataset","license":"Not verified; no unambiguous repository license identified.","notes":"Actual nested LeTac-MPC/scripts/training.py is retrievable; the repository also contains the named tactile-image dataset directory and files. Large recursive listings can truncate before implementation paths. No unambiguous repository license or separate pretrained checkpoint release was verified; code/data availability does not imply either."},"evidence":[{"url":"https://arxiv.org/abs/2403.04934","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://github.com/ZhengtongXu/LeTac-MPC","label":"Official released implementation and LICENSE","fields":["links"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://github.com/ZhengtongXu/LeTac-MPC","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."},{"url":"https://github.com/ZhengtongXu/LeTac-MPC/tree/main/LeTac-MPC/dataset","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["Controller frequency does not measure training cost."],"verified":"2026-10-19"},{"id":"dp3","name":"3D Diffusion Policy (DP3)","title":"3D Diffusion Policy: Generalizable Visuomotor Policy Learning via Simple 3D Representations","date":"2024-03-06","year":2024,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Pairs a compact sparse-point encoder with action diffusion for low-data manipulation, including dexterous real tasks.","institution":"Tsinghua University","venue":"RSS 2024","modalities":["Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2403.03954","project":"https://3d-diffusion-policy.github.io/","code":"https://github.com/YanjieZe/3D-Diffusion-Policy","data":"https://github.com/YanjieZe/3D-Diffusion-Policy#data"},"policy":{"family":"Diffusion","baseModel":"DP3 PointNet-style MLP encoder + convolutional Diffusion Policy","modelSize":"Not reported","inputs":"Measured-depth XYZ point clouds without RGB colors, plus robot pose","outputs":"Continuous robot action chunks; embodiment-dependent gripper/arm or dexterous commands","trainingData":"72 simulation tasks; typically 10 demos; four real tasks with 40 demos each","trainingTime":"1,000 epochs MetaWorld; 3,000 epochs other tasks; wall-clock not reported","trainingHardware":"Not reported","controlRate":"Paper appendix: 12.7 FPS DP3 and 25.3 FPS Simple DP3; hardware scope not specified there","trainingMethod":"End-to-end diffusion behavior cloning, DDIM sampling","evaluation":"Adroit, DexArt, MetaWorld and four real tasks: Roll-Up, Dumpling, Drill, Pour"},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2403.03954","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/YanjieZe/3D-Diffusion-Policy","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://3d-diffusion-policy.github.io/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/YanjieZe/3D-Diffusion-Policy/master/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["Main DP3 setting discards RGB color; it is a depth/geometry policy rather than an RGB-D fusion policy.","Workspace cropping, camera calibration and point sampling are part of the observation pipeline."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Available","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"tac-man","name":"Tac-Man","title":"Tac-Man: Tactile-Informed Prior-Free Manipulation of Articulated Objects","date":"2024-03-04","year":2024,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Contact-stability tactile servoing manipulates unknown articulated objects without a kinematic prior.","institution":"Not reported","venue":"Preprint","modalities":["Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2403.01694","project":"https://tacman-aom.github.io"},"policy":{"family":"Hybrid","baseModel":"Tactile contact geometry/deviation feedback and adaptive contact-preserving motion.","modelSize":"Not reported","inputs":"Initial grasp/contact reference, tactile geometry and robot state.","outputs":"Arm position/orientation updates maintaining stable contact.","trainingData":"No learned object kinematic model or policy training corpus required.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Prior-free feedback execution rather than learned VLA/RL control.","evaluation":"Simulated play boards and real articulated doors/drawers under ambiguity and disturbance."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2403.01694","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://tacman-aom.github.io","label":"Official author project","fields":["links","release"],"note":"Live project artifact links inspected on2026-10-17."}],"caveats":["Prior-free refers to articulated object kinematics; an initial contact/grasp state is still required."],"verified":"2026-10-17"},{"id":"dinobot","name":"DINOBot","title":"DINOBot: Robot Manipulation via Retrieval and Alignment with Vision Foundation Models","date":"2024-02-20","year":2024,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"DINO global features retrieve an appropriate demonstration, while dense features geometrically align the robot before replay.","institution":"Imperial College London","venue":"ICRA 2024","modalities":["RGB","Depth","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2402.13181","project":"https://www.robot-learning.uk/dinobot"},"policy":{"family":"Geometric transfer","baseModel":"Retrieval, 3D alignment and trajectory replay. Frozen DINO-ViT image and pixel features with correspondence-based alignment.","modelSize":"Not reported","inputs":"Wrist RGB-D observation and task-indexed demonstration image/trajectory buffer; depth lifts DINO pixel correspondences. Robot kinematics executes alignments, but the stated learned observation excludes proprioceptive features.","outputs":"Alignment motions followed by replayed demonstration end-effector velocities.","trainingData":"Small task demonstration buffer; pretrained DINO visual model, without target-task network fitting.","trainingTime":"No per-target policy fine-tuning.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Image-level semantic retrieval, pixel-level geometric alignment, then replay.","evaluation":"Real grasping, pouring, sweeping, flipping, dishwasher opening, insertion and assembly; seen/unseen objects and distractors, ten trials per task/condition."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No author-linked implementation was verified in the inspected primary manuscript/project. This is an evidence gap, not proof that no implementation exists."},"evidence":[{"url":"https://arxiv.org/abs/2402.13181","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://www.robot-learning.uk/dinobot","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."}],"caveats":["The survey citation key says 2023; first arXiv submission is February 20, 2024.","The pipeline is an alignment-and-replay controller rather than an end-to-end DINO action network."],"verified":"2026-10-17"},{"id":"tvl","name":"TVL / TVL-LLaMA","title":"A Touch, Vision, and Language Dataset for Multimodal Alignment","date":"2024-02-20","year":2024,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Aligns tactile, visual and language observations, releasing an optical-touch encoder, tactile-conditioned language model and paired tactile descriptions.","institution":"UC Berkeley; FAIR at Meta; TU Dresden","venue":"ICML 2024","modalities":["Tactile","RGB","Language"],"environments":["Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2402.13232","project":"https://tactile-vlm.github.io/","code":"https://github.com/Max-Fu/tvl","data":"https://huggingface.co/datasets/mlfu7/Touch-Vision-Language-Dataset","weights":"https://huggingface.co/mlfu7/Touch-Vision-Language-Models"},"model":{"role":"Reusable representation or perception model; downstream controllers are separate systems","family":"Tactile-language model","baseModel":"Random-initialized tactile ViT aligned with CLIP vision/text; ImageBind-LLM-style adapter on LLaMA-2 7B","modelSize":"Tactile ViT-Tiny 5.7M / Small 22M / Base 86M; LLaMA-2 7B for language generation","inputs":"Optical tactile image, paired RGB observation and textual prompt; encoder supports touch-only embeddings","outputs":"Open-vocabulary touch embeddings and generated tactile descriptions","trainingMethod":"Pairwise tactile–vision–language contrastive alignment, then tactile-adapter pretraining and language-model fine-tuning","trainingData":"43,741 in-contact pairs (44K rounded): HCT 39,154 plus SSVTP 4,587; 10% human text labels and 90% GPT-4V pseudo-labels","trainingTime":"Encoder: 200 epochs; language-stage wall-clock duration not reported","trainingHardware":"Encoder experiments: 1 NVIDIA A100; TVL-LLaMA: 4 NVIDIA A100 with gradient accumulation 2","inference":"Not reported","evaluation":"Open-vocabulary tactile classification/retrieval and the TVL description benchmark; human and model-assisted text-rating protocols."},"release":{"code":"Available","weights":"Available — official download link","data":"Available — official resource link","license":"Apache-2.0 code LICENSE.txt; pretrained LLaMA-2 base and datasets retain separate terms","notes":"Code, pretrained encoders and downstream task data are separate artifacts; no policy checkpoint is implied by an encoder release."},"evidence":[{"url":"https://arxiv.org/abs/2402.13232","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Method, experiments and implementation sections; specifications retain component and evaluation scope."},{"url":"https://tactile-vlm.github.io/","label":"Official project","fields":["links","release"],"note":"Author project; author-reported release scope is distinguished from an executed reproduction."},{"url":"https://github.com/Max-Fu/tvl","label":"Official implementation and current release instructions","fields":["release","links"],"note":"README and repository files inspected on 2026-10-17."},{"url":"https://github.com/Max-Fu/tvl/blob/main/LICENSE.txt","label":"Repository license file","fields":["release.license"],"note":"Applies to repository scope; model/data/base-model terms can differ."},{"url":"https://huggingface.co/mlfu7/Touch-Vision-Language-Models","label":"Official released encoder and adapter files","fields":["release","model"],"note":"Official artifact metadata inspected separately from code."}],"caveats":["No robot action head or autonomous manipulation policy is provided by TVL-LLaMA.","Language generation uses a LLaMA-2 base whose access/terms remain separate from the released tactile adapter."],"relatedIds":["unitouch","octopi","vtv-llm"],"verified":"2026-10-17"},{"id":"lmpc","name":"Language Model Predictive Control (LMPC)","title":"Learning to Learn Faster from Human Feedback with Language Model Predictive Control","date":"2024-02-18","year":2024,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Learns to predict future human–robot teaching interactions, then searches imagined interaction rollouts to choose code responses that require fewer corrections.","institution":"Google DeepMind","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Bimanual","Dexterous hand","Mobile manipulator","Legged robot"],"links":{"paper":"https://arxiv.org/abs/2402.11450","project":"https://robot-teaching.github.io/"},"policy":{"family":"Hybrid","baseModel":"PaLM 2-S code-writing language model; LMPC-Rollouts and LMPC-Skip are fine-tuned variants, coupled to MuJoCo MPC / MJPC execution.","modelSize":"Not reported","inputs":"Multi-turn human language feedback, previous reward-code responses and robot / task context; physical motion planning uses estimated object state.","outputs":"Robot reward code interpreted by model-predictive control; inference can sample predicted future feedback / code sequences before selecting the next response.","trainingData":"Approximately three million tokens of augmented / original human teaching interactions; around 350 evaluation chat sessions across embodiments.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Offline LLM fine-tuning improves teachability, followed by online in-context human feedback and rollout-based inference search.","evaluation":"Five embodiments including robot dog, mobile manipulator, ALOHA, bimanual KUKA and KUKA with hand; selected real mobile-manipulation / robot-dog tasks."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The original paper is public; code, model and dataset release scopes are verified separately where established."},"evidence":[{"url":"https://arxiv.org/abs/2402.11450","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://robot-teaching.github.io/","label":"LMPC official project author affiliation","fields":["institution"],"note":"The author list on the project page explicitly identifies Google DeepMind."}],"caveats":["This updates the language-model weights during preparation; it is not purely a frozen-model context method.","MPC converts generated rewards into motion, and some physical settings use an external object-state estimation pipeline."],"verified":"2026-10-17"},{"id":"3dda","name":"3D Diffuser Actor","title":"3D Diffuser Actor: Policy Diffusion with 3D Scene Representations","date":"2024-02-16","year":2024,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Denoises end-effector trajectories using 3 D scene features and relative geometric attention.","institution":"Carnegie Mellon University","venue":"CoRL 2024","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2402.10885","project":"https://3d-diffuser-actor.github.io/","code":"https://github.com/nickgkan/3d_diffuser_actor","data":"https://huggingface.co/katefgroup/3d_diffuser_actor/tree/main","weights":"https://huggingface.co/katefgroup/3d_diffuser_actor/tree/main"},"policy":{"family":"Diffusion","baseModel":"CLIP image/text features lifted to 3D + relative-attention trajectory diffusion","modelSize":"Not reported","inputs":"Calibrated RGB-D views, language embedding and gripper-pose history","outputs":"Trajectory of 3D poses, continuous 6D rotation and gripper openness; keypose mode also supported","trainingData":"RLBench PerAct split (100 demos/task), CALVIN play data; real demonstrations across 12 tasks","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"CALVIN comparison reports 600 ms for six predicted poses (10 actions/s), not 10 fresh observations/s","trainingMethod":"Diffusion behavior cloning with keypose-segmented demonstration trajectories","evaluation":"RLBench, CALVIN and 12 real manipulation tasks"},"release":{"code":"Available","weights":"Available","data":"Available","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2402.10885","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/nickgkan/3d_diffuser_actor","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://3d-diffuser-actor.github.io/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/nickgkan/3d_diffuser_actor/master/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["Requires depth and camera calibration.","Action throughput from predicted chunks differs from closed-loop replanning frequency."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Available","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"colosseum","name":"The Colosseum","title":"The Colosseum: A Benchmark for Evaluating Generalization for Robotic Manipulation","date":"2024-02-13","year":2024,"collections":["benchmark"],"categories":["robustness-evaluation"],"resourceType":"benchmark","summary":"RLBench-based perturbation benchmark testing robustness of learned manipulation policies under controlled visual and physical changes.","institution":"University of Southern California / collaborating institutions","venue":"RSS 2024","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2402.08191","project":"https://robot-colosseum.github.io/","code":"https://github.com/robot-colosseum/robot-colosseum"},"benchmark":{"simulator":"CoppeliaSim / PyRep / RLBench","simulatorVersion":"RLBench-compatible CoppeliaSim stack; exact release Not verified.","taskCount":"20 tasks with 14 controlled perturbation axes","tasks":["basketball in hoop","close box","close laptop lid","empty dishwasher","get ice from fridge","hockey","meat on grill","move hanger","wipe desk","open drawer","slide block to target","reach and drag","put money in safe","place wine at rack location","insert onto square peg","stack cups","turn oven on","straighten rope","setup chess","scoop with spatula"],"taskListScope":"Complete twenty task labels from paper evaluation table.","capabilities":["Visual robustness","Physical-property shift","Camera robustness","Composed perturbations"],"metrics":"Success and relative degradation per perturbation; hardware correlation on selected tasks.","robots":"Franka Panda","sensors":"RGB-D / point clouds depending baseline; proprioception and language. No tactile input in original suite.","dataCollection":"RLBench scripted experts: motion planners execute manually specified task waypoints with randomized object poses.","data":"100 unperturbed demonstrations/task for training; perturbed evaluation configurations and printable real validation assets.","protocol":"Train on common clean demonstrations; evaluate 25 episodes per perturbation condition; combine multiple factors separately; physical validation is a subset.","baselines":"Five manipulation methods, including PerAct, RVT and zero-shot VoxPoser; exact method/pretraining setting in manuscript."},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"MIT code; underlying RLBench/CoppeliaSim/asset terms separate","notes":"Perturbation code, task config and physical-object assets public."},"evidence":[{"url":"https://arxiv.org/abs/2402.08191","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://robot-colosseum.github.io/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/robot-colosseum/robot-colosseum","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://arxiv.org/abs/2402.08191","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Colosseum V2 uses ManiSkill and new tasks, so values across editions are not directly comparable."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not verified","data":"Available","license":"MIT code; underlying RLBench/CoppeliaSim/asset terms separate","notes":"Perturbation code, task config and physical-object assets public."}},"relatedIds":[]},{"id":"introspective-planning","name":"Introspective Planning","title":"Introspective Planning: Aligning Robots' Uncertainty with Inherent Task Ambiguity","date":"2024-02-09","year":2024,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Retrieves post-hoc explanations of human-approved choices to align robot plan uncertainty with ambiguous instructions and request clarification when needed.","institution":"Princeton University","venue":"NeurIPS 2024","modalities":["Language"],"environments":["Simulation"],"embodiments":["Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2402.06529","project":"https://introplan.github.io","code":"https://github.com/kevinliang888/IntroPlan","data":"https://huggingface.co/datasets/kaiquliang/Safe-Mobile-Manipulation"},"policy":{"family":"Robot agent","baseModel":"GPT-4 Turbo gpt-4-1106-preview and GPT-3.5 text-davinci-003; Sentence-BERT retrieves three knowledge entries.","modelSize":"Not reported","inputs":"Instruction, candidate robot plans, retrieved instruction–valid-option–rationale examples and optional user clarification.","outputs":"A set of acceptable task-level action choices or a request for user help, followed by skill execution.","trainingData":"400 calibration / knowledge examples and 200 test examples in the mobile-manipulation dataset; separate distribution-shift datasets.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen language model; human-valid options supervise post-hoc rationale construction, retrieval guides inference, and conformal calibration determines an uncertainty-aware action set.","evaluation":"Mobile / safe-mobile manipulation plan-choice datasets and simulated tabletop rearrangement, with uncertainty / help / safety metrics and distribution-shift studies."},"release":{"code":"Available","weights":"Not applicable","data":"Available","license":"MIT (code); model, dependency and dataset terms are separate.","notes":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms. Actual dataset / annotation files are present in the author-linked Hub repository."},"evidence":[{"url":"https://arxiv.org/abs/2402.06529","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/kevinliang888/IntroPlan/main/README.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."},{"url":"https://raw.githubusercontent.com/kevinliang888/IntroPlan/main/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Code-license text checked directly; this license is not automatically assigned to model weights or third-party data."},{"url":"https://introplan.github.io","label":"Official project links / artifacts","fields":["links","release"],"note":"Author-linked repository / model / dataset endpoints are audited individually; a link alone does not establish a permissive asset license."},{"url":"https://huggingface.co/datasets/kaiquliang/Safe-Mobile-Manipulation","label":"Author-linked Hugging Face dataset files / card","fields":["links.data","release.data","release.notes"],"note":"Actual dataset / annotation files are present in the author-linked Hub repository. No card license was identified; code terms are not assigned to this asset."}],"caveats":["Reported success primarily evaluates valid task-level choices; this does not establish contact-level positioning accuracy.","Human labels are used to construct rationales and calibrate uncertainty.","Mobile-manipulation results are task-choice datasets; this record does not claim autonomous physical arm deployment."],"verified":"2026-10-17"},{"id":"unitouch","name":"UniTouch / Touch-LLM","title":"Binding Touch to Everything: Learning Unified Multimodal Tactile Representations","date":"2024-01-31","year":2024,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Binds optical touch to an existing multimodal embedding space through image alignment and sensor-specific tokens, with zero-shot tactile understanding and Touch-LLM demonstrations.","institution":"Yale University; University of Michigan","venue":"CVPR 2024","modalities":["Tactile","RGB","Language","Audio"],"environments":["Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2401.18084","project":"https://cfeng16.github.io/UniTouch/","code":"https://github.com/cfeng16/UniTouch","weights":"https://huggingface.co/chfeng/Touch-LLM"},"model":{"role":"Reusable representation or perception model; downstream controllers are separate systems","family":"Multimodal representation","baseModel":"ViT tactile encoder aligned to frozen ImageBind image embeddings; sensor-specific tokens; LLaMA-based Touch-LLM extension","modelSize":"24-layer tactile ViT; feature width 1024; total trainable parameter count not reported","inputs":"Optical tactile image plus sensor identity; paired scene image for alignment; language/audio queries through the frozen shared space","outputs":"Shared embeddings, zero-shot property/grasp predictions and retrieval; optional tactile-conditioned text or image generation","trainingMethod":"Touch–image contrastive alignment against a frozen multimodal anchor with sensor-specific sampling and tokens","trainingData":"Paired visual–tactile datasets from heterogeneous optical sensors; paper uses Touch and Go, Feeling of Success, YCB-Slide and ObjectFolder sources","trainingTime":"150 epochs for touch embedding alignment; wall-clock duration not reported","trainingHardware":"4 NVIDIA A40 GPUs, batch size 48 per GPU for alignment","inference":"Not reported","evaluation":"Zero-shot material and grasp-success prediction, cross-modal retrieval, Touch-LLM question answering and tactile-conditioned image synthesis; no closed-loop manipulation benchmark."},"release":{"code":"Available","weights":"Available — official download link","data":"Not verified","license":"No standalone repository LICENSE identified; ImageBind, language and generative backbones retain separate terms","notes":"Code, pretrained encoders and downstream task data are separate artifacts; no policy checkpoint is implied by an encoder release."},"evidence":[{"url":"https://arxiv.org/abs/2401.18084","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Method, experiments and implementation sections; specifications retain component and evaluation scope."},{"url":"https://cfeng16.github.io/UniTouch/","label":"Official project","fields":["links","release"],"note":"Author project; author-reported release scope is distinguished from an executed reproduction."},{"url":"https://github.com/cfeng16/UniTouch","label":"Official implementation and current release instructions","fields":["release","links"],"note":"README and repository files inspected on 2026-10-17."}],"caveats":["Language and audio can query the shared embedding space; they are not additional physical sensor streams required by the tactile encoder.","Zero-shot grasp prediction and language answers do not constitute an executable manipulation policy."],"relatedIds":["tvl","anytouch"],"verified":"2026-10-17"},{"id":"serl","name":"SERL","title":"SERL: A Software Suite for Sample-Efficient Robotic Reinforcement Learning","date":"2024-01-29","year":2024,"collections":["policy"],"categories":["visual-precision-rl"],"resourceType":"policy","summary":"Open real-robot RL software combines sample-efficient off-policy learning, demonstration replay, learned rewards and reset routines.","institution":"UC Berkeley","venue":"ICRA 2024","modalities":["RGB","Proprioception"],"environments":["Real world","Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2401.16013","project":"https://serl-robot.github.io/","code":"https://github.com/rail-berkeley/serl"},"policy":{"family":"RL","baseModel":"RLPD/SAC-style actor-critic with pretrained ResNet visual features","modelSize":"Not reported","inputs":"RGB camera images and robot proprioception","outputs":"Cartesian end-effector commands and gripper actions through impedance control","trainingData":"Small initial demo sets plus online robot interaction; 20-demo configuration is documented","trainingTime":"25–50 min per evaluated real task","trainingHardware":"Single RTX 4090","controlRate":"10 Hz high-level setpoints with a separate fast robot impedance controller","trainingMethod":"High-update-to-data off-policy RL with demonstration replay and image augmentation","evaluation":"PCB insertion, cable routing, object relocation and insertion examples"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Apache-2.0 (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2401.16013","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/rail-berkeley/serl","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://serl-robot.github.io/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/rail-berkeley/serl/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["Current repository is deprecated in favor of HIL-SERL.","Learned sparse reward classifiers and robot-specific controllers are part of the full learning system."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Apache-2.0 (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"autort","name":"AutoRT","title":"AutoRT: Embodied Foundation Models for Large Scale Orchestration of Robotic Agents","date":"2024-01-23","year":2024,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Orchestrates fleets of real robots by proposing visually grounded tasks, filtering them and selecting available autonomous or teleoperated execution policies.","institution":"Google DeepMind","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Real world"],"embodiments":["Mobile manipulator","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2401.12963","project":"https://auto-rt.github.io/"},"policy":{"family":"Hybrid","baseModel":"Vision-language scene description, language-model task proposal / filtering, and execution policies including scripted control, teleoperation and RT-2.","modelSize":"Not reported","inputs":"Environment imagery and semantic mapping, robot capabilities, fleet state, task constraints and execution feedback.","outputs":"Selected robot tasks and execution-policy assignments; individual low-level policies generate control actions.","trainingData":"77,000 real-robot episodes over seven months and four office buildings, using 53 robots in total and more than 20 simultaneously at peak.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Foundation-model task / fleet orchestration collects data; low-level execution combines pretrained autonomous policies, scripted control and teleoperation.","evaluation":"Real fleet-scale data collection, task diversity, feasibility / filtering and policy-specific outcomes."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2401.12963","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["53 is the number of robots used over the full campaign; over 20 is peak simultaneous deployment.","77,000 collected trials include teleoperation and scripted episodes, not exclusively learned-policy autonomous successes."],"verified":"2026-10-17"},{"id":"mvitac","name":"MViTac","title":"Multimodal Visual-Tactile Representation Learning through Self-Supervised Contrastive Pre-Training","date":"2024-01-22","year":2024,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Learns visual–tactile features through intra- and cross-modal self-supervision for material recognition and grasp-success prediction.","institution":"Cyber-Physical-Systems Lab, Montanuniversität Leoben, Austria","venue":"Preprint","modalities":["Tactile","RGB"],"environments":["Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2401.12024","project":"https://sites.google.com/view/mvitac/home","code":"https://github.com/ligerfotis/mvitac","data":"https://drive.google.com/drive/folders/1wHEg_RR8YAQjMnt9r5biUwo5z3P6bjR3"},"model":{"role":"Reusable representation or perception model; downstream controllers are separate systems","family":"Multimodal representation","baseModel":"ImageNet-pretrained ResNet-18 branches with momentum encoders; two-layer intra/inter-modal contrastive projection heads","modelSize":"ResNet-18 backbone, 512-dimensional features and 128-dimensional contrastive projections; complete-system parameter count not reported","inputs":"Paired scene RGB and optical tactile images","outputs":"Visual/tactile representations and lightweight property/grasp prediction heads","trainingMethod":"Self-supervised contrastive pretraining combining intra-modal augmented-view and inter-modal paired losses","trainingData":"Touch and Go, ObjectFolder and tactile grasping data under dataset-specific experiments","trainingTime":"240 pretraining epochs and 60 downstream epochs; wall-clock duration not reported","trainingHardware":"1 NVIDIA RTX 4090","inference":"Not reported","evaluation":"Material/hardness/roughness classification and binary grasp-success prediction; no learned control action output."},"release":{"code":"Available","weights":"Not verified","data":"Available — official resource link","license":"MIT text embedded in official README; no standalone LICENSE file identified; upstream data terms remain separate","notes":"Code, pretrained encoders and downstream task data are separate artifacts; no policy checkpoint is implied by an encoder release."},"evidence":[{"url":"https://arxiv.org/abs/2401.12024","label":"Original model paper and first submission","fields":["title","date","summary","institution","model"],"note":"Method, experiments and implementation sections; specifications retain component and evaluation scope."},{"url":"https://sites.google.com/view/mvitac/home","label":"Official project","fields":["links","release"],"note":"Author project; author-reported release scope is distinguished from an executed reproduction."},{"url":"https://github.com/ligerfotis/mvitac","label":"Official implementation and current release instructions","fields":["release","links"],"note":"README and repository files inspected on 2026-10-17."}],"caveats":["This paper is MViTac, not the similarly named VITaL pretraining or VisuoTactile Local policy.","Grasp outcome classification is evaluated from recorded tactile/visual data and is not autonomous grasp control."],"relatedIds":["convitac","unitouch"],"verified":"2026-10-17"},{"id":"fmb","name":"FMB","title":"FMB: a Functional Manipulation Benchmark for Generalizable Robotic Learning","date":"2024-01-16","year":2024,"collections":["benchmark"],"categories":["precision-assembly"],"resourceType":"benchmark","summary":"Reproducible physical functional assembly benchmark with procedurally varied 3D-printed parts, multi-stage skills and a large RGB-D demonstration dataset.","institution":"University of California Berkeley","venue":"The International Journal of Robotics Research 2024","modalities":["RGB","Depth","Force/Torque","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2401.08553","project":"https://functional-manipulation-benchmark.github.io/","code":"https://github.com/rail-berkeley/fmb","data":"https://huggingface.co/datasets/charlesxu0124/functional-manipulation-benchmark"},"benchmark":{"simulator":"Not applicable to original physical benchmark","simulatorVersion":"Not applicable","taskCount":"2 task families: single-object and multi-object multi-stage manipulation; 54 single-object variants and 3 four-object interlocking assembly sets","tasks":["Single-Object Multi-Stage Manipulation","Multi-Object Multi-Stage Manipulation"],"taskListScope":"Complete two task-family definitions; primitive names are Grasp, Place on Fixture, Regrasp, Rotate, Move to Board and Insert.","capabilities":["Functional grasping","Environment-assisted reorientation","Precise insertion","Sequential assembly","Geometry/appearance generalization"],"metrics":"Per-primitive success, complete multi-stage success, held-out object/pose generalization.","robots":"Franka Panda","sensors":"2 global and 2 wrist RGB-D views; robot state, gripper and force/torque signals in data.","dataCollection":"SpaceMouse teleoperation commands end-effector motion; selected skill-collection stages are partially scripted to exploit repeatable fixture states.","data":"22,550 expert demonstration trajectories. Single-object tasks last about 20–40 s; complete multi-object assembly can exceed 100 s.","protocol":"Standardized workspace and reset; held-out object variations and board positions; skill-level models can be combined into longer manipulation pipelines.","baselines":"Provided ResNet-based and Transformer-based imitation-learning skill policies."},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"Dataset card: CC-BY-4.0; code license Not verified","notes":"Robot/controller infrastructure, dataset builder, training/evaluation code, CAD files and data public."},"evidence":[{"url":"https://arxiv.org/abs/2401.08553","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://functional-manipulation-benchmark.github.io/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/rail-berkeley/fmb","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://arxiv.org/abs/2401.08553","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["54 part variants are not 54 independently defined semantic tasks.","No tactile image sensor is included; F/T is a distinct sensing channel."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not verified","data":"Available","license":"Dataset card: CC-BY-4.0; code license Not verified","notes":"Robot/controller infrastructure, dataset builder, training/evaluation code, CAD files and data public."}},"relatedIds":[]},{"id":"droc","name":"DROC","title":"Distilling and Retrieving Generalizable Knowledge for Robot Manipulation via Language Corrections","date":"2023-11-17","year":2023,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Distills reusable knowledge from online language corrections and retrieves past visual / textual experiences to correct robot plans and skill parameters.","institution":"Stanford University; Google DeepMind","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2311.10678","project":"https://sites.google.com/stanford.edu/droc","code":"https://github.com/Stanford-ILIAD/droc"},"policy":{"family":"Robot agent","baseModel":"GPT-4 for language modules; Segment Anything / CLIP visual grounding and DINOv2 visual retrieval.","modelSize":"Not reported","inputs":"Task instruction, robot visual observations, online human language corrections and retrieved visual / textual experiences.","outputs":"Robot-control code changes at plan and skill-parameter levels, plus retained generalizable correction knowledge.","trainingData":"Human-guided manipulation / correction interactions and retained experience memory; no correction-interpretation training dataset is required.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen foundation models; corrections change generated code and distilled external knowledge rather than LLM weights.","evaluation":"Real Franka Emika Panda tabletop manipulation with skill-level and plan-level correction / retrieval experiments."},"release":{"code":"Available","weights":"Not applicable","data":"Not verified","license":"Not reported — no repository license found","notes":"Official paper-linked repository contains correction prompts, cache, utilities and execution scripts. A public repository is not treated as proof of licensed open source."},"evidence":[{"url":"https://arxiv.org/abs/2311.10678","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://github.com/Stanford-ILIAD/droc","label":"Official implementation, README and file/license inspection on 19 October 2026","fields":["links","release"],"note":"Official paper-linked repository contains correction prompts, cache, utilities and execution scripts. A public repository is not treated as proof of licensed open source. Not reported — no repository license found."}],"caveats":["The method learns from human corrections and is not autonomous self-improvement without supervision.","Visual retrieval and downstream control APIs are part of the deployment system."],"verified":"2026-10-19"},{"id":"ecrl","name":"EcRL","title":"Estimator-Coupled Reinforcement Learning for Robust Purely Tactile In-Hand Manipulation","date":"2023-11-07","year":2023,"collections":["policy"],"categories":["tactile-foundation","visual-precision-rl"],"resourceType":"policy","summary":"Estimator-coupled reinforcement learning co-trains tactile pose inference and control to reduce sim-to-real estimation failures.","institution":"DLR / Technical University of Munich / Deggendorf Institute of Technology","venue":"Preprint","modalities":["Force/Torque","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2311.04060","project":"https://dlr-alr.github.io/dlr-tactile-manipulation"},"policy":{"family":"RL","baseModel":"PPO actor and supervised recurrent state estimator trained together.","modelSize":"Not reported","inputs":"Hand joint positions/control errors and estimated object pose/goal.","outputs":"12 DLR Hand II joint targets.","trainingData":"4096 parallel Isaac simulation environments; new rollouts jointly update estimator and policy.","trainingTime":"Median 6.5 h convergence per object;~95%final performance after 5 h.","trainingHardware":"Single NVIDIA T4.","controlRate":"10 Hz policy/estimator;60 Hz stacked measurements;1000 Hz impedance controller.","trainingMethod":"Alternating on-policy RL and supervised estimator updates with ground truth-state curriculum.","evaluation":"Palm-down goal-conditioned reorientation of several objects on DLR Hand II."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2311.04060","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://dlr-alr.github.io/dlr-tactile-manipulation","label":"Official author project","fields":["links","release"],"note":"Live project artifact links inspected on2026-10-17."}],"caveats":["Tactile inference comes from joint position/control error; no skin tactile array."],"verified":"2026-10-17"},{"id":"m3l","name":"M3L — Power of the Senses","title":"The Power of the Senses: Generalizable Manipulation from Vision and Touch through Masked Multimodal Learning","date":"2023-11-02","year":2023,"collections":["policy"],"categories":["tactile-foundation","visual-precision-rl"],"resourceType":"policy","summary":"Masked multimodal learning jointly trains visual-tactile representations and manipulation policies, including vision-only deployment transfer.","institution":"University of California, Berkeley / KAIST","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2311.00924"},"policy":{"family":"RL","baseModel":"Masked multi modal auto encoder coupled to RL policy.","modelSize":"Not reported","inputs":"Visual/tactile observations during training; configured vision-only or visual-tactile deployments.","outputs":"Manipulation actions.","trainingData":"Robot manipulation RL interactions; exact training budget Not reported in inspected text.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Joint masked representation learning and policy optimization.","evaluation":"Generalization across visual/contact conditions and vision-only ablation."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2311.00924","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."}],"caveats":["Modality availability differs between full visual-tactile and vision-only evaluation variants."],"verified":"2026-10-17"},{"id":"robogen","name":"RoboGen","title":"RoboGen: Towards Unleashing Infinite Data for Automated Robot Learning via Generative Simulation","date":"2023-11-02","year":2023,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A generative simulation pipeline proposes robot tasks, assets and learning supervision, then chooses reinforcement learning, motion planning or trajectory optimization to acquire skills.","institution":"Not reported","venue":"ICML 2024","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper","Dexterous hand","Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2311.01455","project":"https://robogen-ai.github.io/","code":"https://github.com/Genesis-Embodied-AI/RoboGen"},"policy":{"family":"Hybrid","baseModel":"Generative foundation models including GPT-4 for task / supervision generation, with task-specific RL or planning executors.","modelSize":"Not reported","inputs":"Task proposals, scene / asset descriptions, simulation state and learning feedback; deployment observations depend on the generated skill.","outputs":"Simulation tasks, scene configurations, reward / supervision code and trained or planned robot skills.","trainingData":"Automatically generated simulation experiences and task-specific policy rollouts; no single shared robot-policy training dataset is implied.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Foundation-model-assisted environment / task construction followed by the appropriate learned or analytic skill-acquisition method.","evaluation":"Generated manipulation and other embodied skill-acquisition tasks in physics simulation."},"release":{"code":"Partial — PyBullet reimplementation","weights":"Not verified","data":"Not verified","license":"Apache-2.0 code, verified from LICENSE; parsed PartNet/Objaverse resources and dependencies retain separate terms.","notes":"Actual task/scene generation, rigid-manipulation/locomotion skill learning, execution and example-task source are public. The official README identifies this as a PyBullet reimplementation and excludes the full original soft-body/Genesis pipeline. Pre-generated tasks and data-generation tools are distinct from a verified complete learned-policy checkpoint or dataset release."},"evidence":[{"url":"https://arxiv.org/abs/2311.01455","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/Genesis-Embodied-AI/RoboGen","label":"Author-linked repository","fields":["links.code"],"note":"Repository identity; code and checkpoint release status are stated separately."},{"url":"https://github.com/Genesis-Embodied-AI/RoboGen/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."},{"url":"https://github.com/Genesis-Embodied-AI/RoboGen","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["This work is arXiv: 2311.01455. The similarly numbered 2311.01745 is unrelated and is excluded.","RoboGen is a skill / data-generation pipeline rather than one fixed observation-to-action foundation policy."],"verified":"2026-10-19"},{"id":"mimictouch","name":"MimicTouch","title":"MimicTouch: Leveraging Multi-modal Human Tactile Demonstrations for Contact-rich Manipulation","date":"2023-10-25","year":2023,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"Learns a tactile-guided base policy from human hand demonstrations and uses real residual RL to bridge the embodiment gap.","institution":"Georgia Institute of Technology","venue":"CoRL 2024 (PMLR publication 2025)","modalities":["Tactile","Audio","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2310.16917","project":"https://sites.google.com/view/MimicTouch"},"policy":{"family":"Hybrid","baseModel":"Tactile/audio representation learning + nonparametric imitation + residual RL","modelSize":"Not reported","inputs":"Tactile images, audio and robot state; human pose used in demonstrations","outputs":"Robot gripper manipulation actions","trainingData":"Human tactile-guided demonstrations; exact aggregate count not reported","trainingTime":"Representative task reaches96% after3 h real residual RL (25 evaluation rollouts)","trainingHardware":"Not reported","controlRate":"Synchronized human data15 Hz; teleoperation comparison sampled5 Hz","trainingMethod":"Multimodal representation learning, nearest-neighbor imitation and online residual RL","evaluation":"Real contact-rich insertion/assembly"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."},"evidence":[{"url":"https://arxiv.org/abs/2310.16917","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://sites.google.com/view/MimicTouch","label":"Author project page","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2310.16917","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."},{"url":"https://proceedings.mlr.press/v270/yu25c.html","label":"Official conference proceedings","fields":["venue"],"note":"8th CoRL was held in November 2024; proceedings published2025."}],"caveats":["Human pose tracking is demonstration supervision, not deployment visual sensing.","Requires hours of real robot refinement in the reported setting."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."}},"relatedIds":[]},{"id":"equivact","name":"EquivAct","title":"EquivAct: SIM(3)-Equivariant Visuomotor Policies beyond Rigid Object Manipulation","date":"2023-10-24","year":2023,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"SIM(3)-equivariant 3D perception/action structure transfers tabletop demonstrations to larger deformable and articulated manipulation.","institution":"Stanford University; Princeton University","venue":"ICRA 2024","modalities":["Depth","Point cloud","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2310.16050","project":"https://equivact.github.io/"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2310.16050","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://equivact.github.io/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."}],"caveats":["Scale/pose equivariance is a structural property; visibility, contact and gravity changes remain deployment limitations."],"verified":"2026-10-17","policy":{"family":"Transformer / BC","baseModel":"Equivariant point-cloud representation pretraining and equivariant behavior-cloning policy.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Partial object point clouds and proprioception.","outputs":"Equivariant robot action/velocity predictions.","trainingMethod":"Contrastive geometry pretraining in simulation, then few-demonstration imitation learning.","trainingData":"Twenty real human demonstrations per tabletop task; separate simulated representation data.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Cloth folding, object covering and box closing in simulation/real mobile manipulation.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"eureka","name":"Eureka","title":"Eureka: Human-Level Reward Design via Coding Large Language Models","date":"2023-10-19","year":2023,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Uses language-model reward-code generation and evolutionary reward reflection to train robot skills through reinforcement learning.","institution":"NVIDIA; University of Pennsylvania; California Institute of Technology","venue":"Preprint","modalities":["Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand","Parallel-jaw gripper","Legged robot"],"links":{"paper":"https://arxiv.org/abs/2310.12931","project":"https://eureka-research.github.io","code":"https://github.com/eureka-research/Eureka"},"policy":{"family":"RL","baseModel":"GPT-4 gpt-4-0314 for reward generation; task-specific PPO policies execute robot actions.","modelSize":"Not reported","inputs":"Reward-design stage: simulator environment code, task description and reward-component / training feedback. PPO policies use the task's state-based simulator observations; no universal RGB input is claimed.","outputs":"Executable reward functions for PPO training; trained specialist policies output robot motor actions.","trainingData":"Isaac Gym / Bi-DexHands reinforcement-learning rollouts across 29 tasks and ten robot embodiments.","trainingTime":"Each reported Eureka run takes less than one day on the stated workstation.","trainingHardware":"One workstation with eight NVIDIA A100 GPUs; at most 16 reward candidates are evaluated concurrently.","controlRate":"Not reported","trainingMethod":"Frozen coding LLM evolves reward programs; PPO updates robot-policy weights. Optional human feedback guides reward reflection.","evaluation":"29 tasks across ten embodiments, with dexterous pen spinning and real-world transfer demonstrations."},"release":{"code":"Available","weights":"Not applicable","data":"Not verified","license":"MIT","notes":"Official repository contains reward generation, IsaacGym environments and RL training code; references to VIMA are baselines rather than Eureka-specific artifacts."},"evidence":[{"url":"https://arxiv.org/abs/2310.12931","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/eureka-research/Eureka","label":"Official implementation, README and file/license inspection on 19 October 2026","fields":["links","release"],"note":"Official repository contains reward generation, IsaacGym environments and RL training code; references to VIMA are baselines rather than Eureka-specific artifacts. MIT."}],"caveats":["The language model designs rewards offline; it is not the high-rate deployed robot controller.","Robot-policy weights are trained even though the reward-writing LLM is frozen."],"verified":"2026-10-19"},{"id":"robotool","name":"RoboTool","title":"Creative Robot Tool Use with Large Language Models","date":"2023-10-19","year":2023,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A four-role language-model system reasons about objects as tools and generates robot programs for creative tool-use tasks.","institution":"Carnegie Mellon University; Google DeepMind","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Legged robot"],"links":{"paper":"https://arxiv.org/abs/2310.13065","project":"https://creative-robotool.github.io/"},"policy":{"family":"Robot agent","baseModel":"GPT-4 in Analyzer, Planner, Calculator and Coder roles.","modelSize":"Not reported","inputs":"Task descriptions, scene / object geometry, physical constraints and configured robot-control functions.","outputs":"Tool-use plans, geometric calculations and executable robot programs.","trainingData":"Few-shot role-specific code / reasoning prompts; no target-task language-model weight training.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen LLM decomposes scene analysis, planning, quantitative calculation and code generation; local robot control executes the program.","evaluation":"Six creative tool-use tasks with robot-arm and quadruped embodiments in simulation and physical experiments."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2310.13065","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["Correct paper identifier is arXiv: 2310.13065; 2309.07983 is unrelated and excluded.","The system receives structured geometric information and robot APIs; results should not be characterized as raw RGB-only direct motor control."],"verified":"2026-10-17"},{"id":"trajectory-transfer-pose","name":"Trajectory Transfer via Unseen Pose Estimation","title":"One-Shot Imitation Learning: A Pose Estimation Perspective","date":"2023-10-18","year":2023,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"One-shot imitation is formulated as object-relative end-effector trajectory transfer, enabling direct comparison of unseen-object pose estimators.","institution":"Imperial College London","venue":"CoRL 2023","modalities":["RGB","Depth","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2310.12077","project":"https://www.robot-learning.uk/pose-estimation-perspective"},"policy":{"family":"Geometric transfer","baseModel":"Unseen-object pose estimation and trajectory transfer. Eight pose estimators, including ICP, DINO/GMFlow/ASpanFormer correspondences and Siamese PointNet++ regression/classification; OWL-ViT/SAM segmentation.","modelSize":"Approximately 2M parameters for each PointNet++ regression/classification baseline; other compared estimators differ.","inputs":"Single kinesthetic demonstration with segmented reference RGB-D and end-effector trajectory; segmented deployment RGB-D and calibrated camera/robot geometry.","outputs":"Object-pose-transformed end-effector trajectory.","trainingData":"No new-task data after the demonstration. Learned pose baselines use randomized object-centric synthetic training and 71 real image pairs for early stopping.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Sawyer robot control at 30 Hz.","trainingMethod":"Pretrain pose estimators, then estimate novel-object relative pose and analytically transfer the demonstration trajectory.","evaluation":"Ten physical tasks plus camera/pose-error sensitivity and spatial-generalization studies; 1,100 simulated image pairs from 55 objects for pose comparison."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No author-linked implementation was verified in the inspected primary manuscript/project. This is an evidence gap, not proof that no implementation exists."},"evidence":[{"url":"https://arxiv.org/abs/2310.12077","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://www.robot-learning.uk/pose-estimation-perspective","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."}],"caveats":["This is both an implemented control formulation and an empirical pose-estimator study, rather than a single uniform neural policy.","No target-task training is compatible with substantial generic pose-estimator pretraining."],"verified":"2026-10-17"},{"id":"amago","name":"AMAGO","title":"AMAGO: Scalable In-Context Reinforcement Learning for Adaptive Agents","date":"2023-10-15","year":2023,"collections":["policy"],"categories":["agent-rsi-icl","visual-precision-rl"],"resourceType":"policy","summary":"An off-policy transformer learns contextual adaptation over long interaction histories, including hidden-goal Meta-World manipulation.","institution":"The University of Texas at Austin / NVIDIA","venue":"Preprint","modalities":["Proprioception"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2310.09971","project":"https://ut-austin-rpl.github.io/amago/","code":"https://github.com/UT-Austin-RPL/amago"},"policy":{"family":"RL","baseModel":"Long-sequence transformer actor-critic with multi-discount off-policy updates and hindsight relabeling.","modelSize":"Not reported","inputs":"State/action/reward history and goals; Meta-World arm experiments mask goal information from observations.","outputs":"Continuous arm control actions in Meta-World; other domains use their own action spaces.","trainingData":"Online replay-buffer experience generated by the agent; domain-specific training budgets.","trainingTime":"Not reported","trainingHardware":"One NVIDIA A5000 GPU per trained AMAGO agent, as stated in the compute appendix; domain-specific elapsed times are not interchangeable.","controlRate":"Not reported","trainingMethod":"End-to-end off-policy in-context RL over full rollouts, with multi-goal hindsight relabeling for goal-conditioned environments.","evaluation":"Meta-World ML1 subset, along with memory, navigation and other meta-RL domains."},"release":{"code":"Available","weights":"Not verified","data":"Not applicable","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Official method code; online-RL experience is generated during training. The paper’s eight-hour POPGym run is not a manipulation training budget."},"evidence":[{"url":"https://arxiv.org/abs/2310.09971","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://ut-austin-rpl.github.io/amago/","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/UT-Austin-RPL/amago","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."},{"url":"https://github.com/UT-Austin-RPL/amago/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["Included because the original full paper evaluates Meta-World arm tasks, despite its broader generic-RL framing.","A5000 throughput profiling and POPGym timing must not be relabeled as complete arm-policy training cost."],"verified":"2026-10-19"},{"id":"rt-x","name":"Open X-Embodiment / RT-X","title":"Open X-Embodiment: Robotic Learning Datasets and RT-X Models","date":"2023-10-13","year":2023,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Cross-robot pretraining produces RT-1-X and RT-2-X manipulation policies from standardized robot datasets.","institution":"Open X-Embodiment Collaboration","venue":"Preprint","modalities":["RGB","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2310.08864","project":"https://robotics-transformer-x.github.io","code":"https://github.com/google-deepmind/open_x_embodiment","data":"https://github.com/google-deepmind/open_x_embodiment#dataset-access","weights":"https://github.com/google-deepmind/open_x_embodiment#rt-1-x-model-checkpoint"},"policy":{"family":"VLA","baseModel":"RT-1-X: FiLM EfficientNet plus transformer; RT-2-X: vision-language model with action-as-text decoding.","modelSize":"RT-1-X 35M; RT-2-X 55B in the paper. These are distinct models, not additive components.","inputs":"RGB camera observations and a natural-language task instruction.","outputs":"Discretized seven-dimensional end-effector/gripper action representation, normalized per dataset / robot.","trainingData":"Open X-Embodiment assembles data from 22 robots and 21 institutions, covering 527 skills / 160,266 task descriptions; model-training mixtures are subsets, not the entire dataset count.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Cross-embodiment behavior cloning with standardized observation/action representations; RT-2-X co-fine-tunes vision-language and robot data.","evaluation":"Positive transfer and adaptation across multiple real robot manipulation platforms."},"release":{"code":"Available","weights":"Partial","data":"Available","license":"Apache-2.0 for repository code; external model, dataset and dependency terms remain separate.","notes":"RT-1-X TensorFlow/JAX checkpoints and loading code are public. This does not imply release of RT-2-X weights or all original datasets under one license."},"evidence":[{"url":"https://arxiv.org/abs/2310.08864","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://robotics-transformer-x.github.io","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/google-deepmind/open_x_embodiment","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."},{"url":"https://github.com/google-deepmind/open_x_embodiment/blob/HEAD/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Actual license file inspected; code license does not automatically license data or weights."}],"caveats":["Dataset-scale and model-training-mixture counts differ.","This canonical record represents the paper’s RT-X policies; standalone RT-1 and RT-2 have separate original works."],"verified":"2026-10-17"},{"id":"d3fields","name":"D³Fields","title":"D³Fields: Dynamic 3D Descriptor Fields for Zero-Shot Generalizable Rearrangement","date":"2023-09-28","year":2023,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Dynamic semantic 3D descriptor fields support image-goal rearrangement with a model-predictive execution system.","institution":"Columbia; UIUC; NUS; Boston Dynamics AI Institute; Stanford","venue":"CoRL 2024","modalities":["RGB","Depth","Point cloud","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2309.16118","project":"https://robopil.github.io/d3fields/","code":"https://github.com/WangYixuan12/d3fields"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT for repository code; pretrained models, data and figure rights remain separate","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2309.16118","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://robopil.github.io/d3fields/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/WangYixuan12/d3fields","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/WangYixuan12/d3fields/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/WangYixuan12/d3fields/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"188 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["Four calibrated fixed RGB-D views used in real experiments; descriptor extraction speed excludes downstream planning."],"verified":"2026-10-17","policy":{"family":"Planning / hybrid","baseModel":"Multi-view visual-foundation features fused into distance, descriptor and instance fields; action optimization.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Multiple calibrated RGB-D views and a 2D goal image.","outputs":"Rearrangement action sequences via geometry-aware planning.","trainingMethod":"Training-free representation construction with pretrained foundation models and task optimization.","trainingData":"No task-specific descriptor training; demonstrations are not required for its image-goal specification.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Real Kinova Gen3 household rearrangement and OmniGibson/Fetch mobile manipulation.","controlRate":"0.166 ± 0.002 s representation construction on A6000 for four views; not the complete planning/control rate."}},{"id":"polarnet","name":"PolarNet","title":"PolarNet: 3D Point Clouds for Language-Guided Robotic Manipulation","date":"2023-09-27","year":2023,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Predicts language-conditioned keyframe actions directly from point clouds with point-wise 3 D attention.","institution":"Inria / ENS / CNRS","venue":"CoRL 2023","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2309.15596","project":"https://www.di.ens.fr/willow/research/polarnet/","code":"https://github.com/vlc-robot/polarnet"},"policy":{"family":"Other","baseModel":"ShapeNet-pretrained PointNeXt-S + CLIP text + Transformer","modelSize":"Not reported","inputs":"Calibrated multi-view RGB-D point clouds, language and gripper state","outputs":"End-effector pose and gripper-open state at key steps","trainingData":"100 demonstrations per RLBench task in main comparisons; real-model demonstrations described in supplement","trainingTime":"~1 h single task; ~30 h multitask/multivariation; real model ~9 h / 100k iterations","trainingHardware":"1 V100 single-task/real; 4 V100 multitask","controlRate":"Not reported; MoveIt executes between predicted real key steps","trainingMethod":"Point-wise behavior cloning; 20k/200k/600k iterations depending on task scope","evaluation":"RLBench single-task/multitask settings and real Franka manipulation"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2309.15596","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/vlc-robot/polarnet","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://www.di.ens.fr/willow/research/polarnet/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://github.com/vlc-robot/polarnet/blob/master/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["Keyframe action prediction with a planner, not a dense servoing policy.","Repository uses master branch; weights and exact license not verified."],"verified":"2026-10-19","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"see-to-touch","name":"See to Touch / TAVI","title":"See to Touch: Learning Tactile Dexterity through Visual Incentives","date":"2023-09-21","year":2023,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"Learns dexterous visuotactile policies using online residual RL with a vision-based optimal-transport reward from one demonstration.","institution":"New York University","venue":"ICRA 2024","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2309.12300","project":"https://see-to-touch.github.io/","code":"https://github.com/irmakguzey/see-to-touch","data":"https://drive.google.com/drive/folders/1IpU97D4nSosdyyWmvuLO-E3phRA-nf8c"},"policy":{"family":"RL","baseModel":"FISH residual policy; contrastive visual encoder; pretrained tactile encoder","modelSize":"Not reported","inputs":"RGB observations, Xela tactile measurements and robot state","outputs":"Dexterous Allegro-hand actions","trainingData":"One expert trajectory per task; tactile encoder pretrained on 2.5 h robot play","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Self-supervised vision representation + visual optimal-transport reward + online residual RL","evaluation":"Six real dexterous tasks including peg pick-and-place, bowl unstacking and slender-object flipping"},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"Not verified","notes":"Official project links code and data."},"evidence":[{"url":"https://arxiv.org/abs/2309.12300","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/irmakguzey/see-to-touch","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2309.12300","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Visual reward is training feedback; tactile and vision are policy inputs.","Online real-robot interactions are additional to the one demonstration."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Available","license":"Not verified","notes":"Official project links code and data."}},"relatedIds":[]},{"id":"rotateit","name":"RotateIt","title":"General In-Hand Object Rotation with Vision and Touch","date":"2023-09-18","year":2023,"collections":["policy"],"categories":["tactile-il","rgbd-3d"],"resourceType":"policy","summary":"Uses a visuotactile Transformer to distill a privileged RL teacher into a depth/touch-conditioned multi-axis rotation policy.","institution":"UC Berkeley / Meta AI","venue":"CoRL 2023","modalities":["Depth","Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2309.09979","project":"https://haozhi.io/rotateit/"},"policy":{"family":"RL","baseModel":"PPO teacher + visuotactile Transformer student","modelSize":"Not reported","inputs":"Noisy depth, tactile observations and proprioceptive history","outputs":"16 Allegro target joint positions","trainingData":"Simulation RL; 32,768 parallel environments; no human demonstrations","trainingTime":"Not reported","trainingHardware":"4 GPUs for simulation training; models not reported","controlRate":"Policy20 Hz; PD300 Hz; physics200 Hz","trainingMethod":"Privileged RL then observation-history distillation and direct sim-to-real deployment","evaluation":"Multi-axis in-hand rotation and unseen object shapes/properties"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."},"evidence":[{"url":"https://arxiv.org/abs/2309.09979","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://haozhi.io/rotateit/","label":"Author project page","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2309.09979","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["Uses depth as the visual input; tactile simulation/perception should not be treated as identical to raw GelSight RGB."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."}},"relatedIds":[]},{"id":"roboagent","name":"RoboAgent / MT-ACT","title":"RoboAgent: Generalization and Efficiency in Robot Manipulation via Semantic Augmentations and Action Chunking","date":"2023-09-05","year":2023,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Semantic augmentation and language-conditioned action chunking train a multi-task manipulation policy from a moderate demonstration set.","institution":"Carnegie Mellon University / FAIR-MetaAI","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2309.01918","project":"https://robopen.github.io/","code":"https://github.com/robopen/roboagent","data":"https://huggingface.co/datasets/jdvakil/RoboSet-Teleoperation","weights":"https://huggingface.co/jdvakil/RoboAgent"},"policy":{"family":"ACT","baseModel":"Multi-task ACT (MT-ACT), visual encoders, language task embeddings and chunked transformer action decoding.","modelSize":"Not reported","inputs":"RGB camera observations, robot state and task/language embedding.","outputs":"Robot action chunks for multi-task manipulation.","trainingData":"RoboSet: 7,500 teleoperated trajectories spanning 12 skills and 38 tasks, expanded using semantic visual augmentation.","trainingTime":"2,000 epochs; augmented MT-ACT model takes about 48 hours, batch size eight.","trainingHardware":"One NVIDIA RTX 2080 Ti GPU for the reported full MT-ACT run.","controlRate":"Robot operation and teleoperation at 5 Hz.","trainingMethod":"Behavior cloning with action chunking and semantic data augmentation.","evaluation":"Real Franka kitchen manipulation, with novel objects, scenes and task configurations."},"release":{"code":"Available","weights":"Available","data":"Available","license":"Not verified","notes":"Author code, RoboAgent checkpoint and RoboSet teleoperation data are linked."},"evidence":[{"url":"https://arxiv.org/abs/2309.01918","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://robopen.github.io/","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/robopen/roboagent","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."}],"caveats":["RoboAgent is the learned MT-ACT system, distinct from generic LLM agent frameworks with similar names."],"verified":"2026-10-17"},{"id":"gnfactor","name":"GNFactor","title":"GNFactor: Multi-Task Real Robot Learning with Generalizable Neural Feature Fields","date":"2023-08-31","year":2023,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Jointly learns a generalizable neural feature field and a voxel-based manipulation policy to improve single-view multi-task learning.","institution":"University of California San Diego","venue":"CoRL 2023 (Oral)","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2308.16891","project":"https://yanjieze.com/GNFactor/","code":"https://github.com/YanjieZe/GNFactor","weights":"https://github.com/YanjieZe/GNFactor#-pre-trained-checkpoints"},"policy":{"family":"Other","baseModel":"0.3M shared 3D U-Net + PerAct-style Perceiver; diffusion-derived semantic features for supervision","modelSize":"0.3M voxel encoder + 25.2M Perceiver; total including auxiliary components not reported","inputs":"Single-view RGB-D, language and gripper state","outputs":"6-DoF keyframe pose, gripper-open and collision flag","trainingData":"10 RLBench tasks × 20 demos; real-world multi-task experiments","trainingTime":"2 days / 100k iterations, batch 2","trainingHardware":"2 RTX 3090 GPUs","controlRate":"Not reported","trainingMethod":"Behavior cloning jointly with novel-view RGB/semantic reconstruction","evaluation":"10 selected RLBench tasks and real Franka tasks"},"release":{"code":"Available","weights":"Available","data":"Partial","license":"MIT (code); model and data terms not verified","notes":"Code, generation/evaluation scripts and checkpoints released; authors do not host the ~92 GB demonstration set."},"evidence":[{"url":"https://arxiv.org/abs/2308.16891","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/YanjieZe/GNFactor","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://yanjieze.com/GNFactor/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/YanjieZe/GNFactor/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["Novel-view and semantic supervision can use information that is not a deployment observation.","Released demos are generated with provided scripts rather than a hosted full dataset."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Partial","license":"MIT (code); model and data terms not verified","notes":"Code, generation/evaluation scripts and checkpoints released; authors do not host the ~92 GB demonstration set."}},"relatedIds":[]},{"id":"robotap","name":"RoboTAP","title":"RoboTAP: Tracking Arbitrary Points for Few-Shot Visual Imitation","date":"2023-08-30","year":2023,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Dense tracked points reveal task-relevant motion and parameterize a few-demonstration image-based servo controller.","institution":"Google DeepMind / University College London","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2308.15975","project":"https://robotap.github.io/","code":"https://github.com/google-deepmind/tapnet","data":"https://github.com/google-deepmind/tapnet/tree/main/tapnet/tapvid#downloading-robotap"},"policy":{"family":"Geometric transfer","baseModel":"Tracking-based few-shot imitation and visual servoing. TAPIR / online causal TAPIR, temporal segmentation, active-point selection and image-based visual servoing.","modelSize":"Not reported","inputs":"A small set of RGB demonstration videos and current RGB point tracks; controller/gripper calibration and task execution state.","outputs":"Image-servo arm corrections, trajectory-following stages and gripper events inferred from demonstration structure.","trainingData":"As few as four to six task demonstrations; released RoboTAP tracking benchmark has 265 videos and 11,592 annotated point tracks, a separate dataset from policy demonstrations.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Use pretrained dense point tracking, optimize task-relevant motion/points and servo toward demonstrated/averaged point targets.","evaluation":"Physical shape matching, stacking, glue application, multi-body rearrangement, deformables and irreversible manipulations."},"release":{"code":"Partial","weights":"Partial","data":"Available","license":"Apache-2.0 code; model/data/backbone terms separate","notes":"TAPIR tracker weights, point-clustering code and RoboTAP annotated tracking data are released; complete physical robot execution code is not established."},"evidence":[{"url":"https://arxiv.org/abs/2308.15975","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://robotap.github.io/","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://github.com/google-deepmind/tapnet","label":"Official code","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://raw.githubusercontent.com/google-deepmind/tapnet/main/LICENSE","label":"Actual source license file","fields":["release.license"],"note":"Scope limited to the file/repository subtree carrying these terms, not paper figures or external models/data."}],"caveats":["Depth cameras occur in the source tracking dataset, but RoboTAP’s stated deployed visual servo input is RGB point locations.","Released tracking model/data do not establish that the complete physical robot controller is released."],"verified":"2026-10-17"},{"id":"rt2","name":"RT-2","title":"RT-2: Vision-Language-Action Models Transfer Web Knowledge to Robotic Control","date":"2023-07-28","year":2023,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Vision-language models co-trained on robot trajectories and web data emit action tokens for real manipulation.","institution":"Google DeepMind","venue":"Preprint","modalities":["RGB","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2307.15818","project":"https://robotics-transformer2.github.io"},"policy":{"family":"VLA","baseModel":"RT-2-PaLI-X and RT-2-PaLM-E, with actions represented as text tokens.","modelSize":"PaLI-X variants: 5B and 55B; PaLM-E variant: 12B. These are alternative models.","inputs":"Robot RGB image and natural-language instruction.","outputs":"Discretized end-effector translation/rotation, gripper and termination actions serialized as text.","trainingData":"RT-1 robot demonstrations co-fine-tuned with original Internet-scale VLM data.","trainingTime":"Not reported","trainingHardware":"Multi-TPU cloud service is explicitly used for inference; exact policy training hardware budget not reported.","controlRate":"55B PaLI-X variant: 1–3 Hz; 5B variant: approximately 5 Hz via cloud inference.","trainingMethod":"Co-fine-tune VLMs on robot action tokens and vision-language tasks.","evaluation":"Approximately 6,000 real robot evaluations of generalization and emergent semantic reasoning."},"release":{"code":"Not released","weights":"Not released","data":"Partial","license":"Not verified","notes":"Official paper/project; links to unrelated language-table code are not presented as an RT-2 policy release."},"evidence":[{"url":"https://arxiv.org/abs/2307.15818","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://robotics-transformer2.github.io","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."}],"caveats":["Different backbone sizes and inference rates must remain variant-specific.","Cloud inference hardware is not a training-cost claim."],"verified":"2026-10-17"},{"id":"f3rm","name":"F3RM","title":"Distilled Feature Fields Enable Few-Shot Language-Guided Manipulation","date":"2023-07-27","year":2023,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Language-guided pose optimization uses 3D feature fields distilled from 2D foundation models.","institution":"MIT CSAIL; IAIFI","venue":"CoRL 2023","modalities":["RGB","Language","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2308.07931","project":"https://f3rm.csail.mit.edu/","code":"https://github.com/f3rm/f3rm"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT for repository code; pretrained models, data and figure rights remain separate","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2308.07931","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://f3rm.csail.mit.edu/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/f3rm/f3rm","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/f3rm/f3rm/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/f3rm/f3rm/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"93 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["The original system reconstructs geometry from calibrated RGB views; a NeRF-derived field is not a measured depth observation.","Scene scanning/fitting is a separate cost from final pose inference."],"verified":"2026-10-17","policy":{"family":"Geometric transfer","baseModel":"NeRF with CLIP/DINO feature distillation and demonstration-conditioned 6-DoF optimization.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Calibrated multi-view RGB scans, few demonstrated poses and optional free-text query.","outputs":"6-DoF grasp/place pose for a physical robot planner.","trainingMethod":"Per-scene feature-field fitting and matching demonstration descriptors; no universal end-to-end action network.","trainingData":"Few grasp/place demonstrations across four object categories; pretrained image/language priors.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Real open-set grasping/placing with unseen objects, descriptions and categories.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"xskill","name":"XSkill","title":"XSkill: Cross Embodiment Skill Discovery","date":"2023-07-19","year":2023,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Discovers shared human–robot skill prototypes from videos, learns a skill-conditioned diffusion controller and composes skills from a new human prompt video.","institution":"Columbia University; J.P. Morgan AI Research; Carnegie Mellon University","venue":"CoRL 2023","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2307.09955","project":"https://xskill.cs.columbia.edu/","code":"https://github.com/real-stanford/xskill","data":"https://xskill.cs.columbia.edu/data/real_kitchen_data.zip"},"policy":{"family":"Diffusion","baseModel":"Cross-embodiment skill-prototype learner and skill-conditioned Diffusion Policy.","modelSize":"Not reported","inputs":"Human prompt video, current robot RGB / proprioception and inferred skill prototypes.","outputs":"Continuous robot actions conditioned on the current skill; alignment selects skills from the demonstrated sequence.","trainingData":"Real-world training uses 175 human videos and 175 robot teleoperation demonstrations; skill discovery uses unsegmented videos and robot action supervision trains the policy. Simulation uses a separate kitchen dataset.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Offline cross-embodiment skill discovery and robot-policy learning; test-time human video supplies a skill composition without target-task policy fine-tuning.","evaluation":"Simulated and real manipulation, including unseen skill compositions and cross-embodiment transfer."},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"MIT (code); model, dependency and dataset terms are separate.","notes":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact. The README links simulation kitchen and real kitchen dataset archives; both endpoints returned HTTP 200. Dataset terms are not inferred from the code license."},"evidence":[{"url":"https://arxiv.org/abs/2307.09955","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/real-stanford/xskill/main/README.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."},{"url":"https://raw.githubusercontent.com/real-stanford/xskill/main/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Code-license text checked directly; this license is not automatically assigned to model weights or third-party data."},{"url":"https://raw.githubusercontent.com/real-stanford/xskill/main/README.md","label":"XSkill data archive links","fields":["links.data","release.data"],"note":"Simulation kitchen.zip and real_kitchen_data.zip are directly author-hosted and reachable; archives were not downloaded in this audit."}],"caveats":["This includes a trained robot-action policy, so it is more than a representation-only method.","Skill discovery and robot-action learning use different supervision; unlabeled videos alone do not train the motor policy."],"verified":"2026-10-17"},{"id":"voxposer","name":"VoxPoser","title":"VoxPoser: Composable 3D Value Maps for Robotic Manipulation with Language Models","date":"2023-07-12","year":2023,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Composes 3D value maps from language-model programs and visual grounding, then optimizes robot motion within those maps.","institution":"Stanford University","venue":"Preprint","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2307.05973","project":"https://voxposer.github.io","code":"https://github.com/huangwl18/VoxPoser"},"policy":{"family":"Robot agent","baseModel":"GPT-4 generates value-map / perception programs; open-vocabulary visual models ground referenced objects.","modelSize":"Not reported","inputs":"Language, calibrated RGB-D observations, robot state and prompt examples for the perception / value-map APIs.","outputs":"3D affordance / constraint / orientation / velocity / gripper value maps used by an optimization-based motion planner.","trainingData":"No task-specific policy training; few-shot code prompts and pretrained perception models are used.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Real camera observations are acquired at 20 Hz; this does not establish a 20 Hz GPT-4 query rate.","trainingMethod":"Frozen language / perception models; programmatic value-map composition and closed-loop optimization-based control.","evaluation":"Simulated and physical tabletop manipulation, including articulated-object and contact-rich interactions."},"release":{"code":"Partial — RLBench demo implementation","weights":"Not applicable","data":"Not verified","license":"MIT code, verified from actual LICENSE; external language/vision models and RLBench assets retain separate terms.","notes":"Actual LMP, voxel interfaces, planner, controller, dynamics and RLBench notebook implementation are public. The author explicitly excludes the real-world object-mask perception pipeline; simulator object masks are used in the demo, which is not a full released physical deployment or evaluation benchmark."},"evidence":[{"url":"https://arxiv.org/abs/2307.05973","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/huangwl18/VoxPoser","label":"Author-linked repository","fields":["links.code"],"note":"Repository identity; code and checkpoint release status are stated separately."},{"url":"https://github.com/huangwl18/VoxPoser/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."},{"url":"https://github.com/huangwl18/VoxPoser","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["Motion planning is a substantive component of the controller.","3D maps are grounded in measured RGB-D rather than inferred monocular depth alone."],"verified":"2026-10-19"},{"id":"knowno","name":"KnowNo","title":"Robots That Ask For Help: Uncertainty Alignment for Large Language Model Planners","date":"2023-07-04","year":2023,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Calibrates a language planner with conformal prediction so it requests human clarification when multiple next actions remain plausible.","institution":"Princeton University; Google DeepMind","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Mobile manipulator","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2307.01928","project":"https://robot-help.github.io","code":"https://github.com/google-research/google-research"},"policy":{"family":"Hybrid","baseModel":"PaLM-2L planner; PaLM-2L-IF and GPT-3.5 text-davinci-003 are separate language-model comparisons, with existing robot skills.","modelSize":"Not reported","inputs":"Task and observation context, skill / action options, a calibration set and human clarifications when requested.","outputs":"A prediction set of next actions; a singleton is executed and a nonsingleton triggers a clarification request.","trainingData":"A held-out calibration dataset, commonly 400 labeled scenarios; no language-model fine-tuning is required.","trainingTime":"Labeling 400 examples takes about four hours for multi-step or 1.5 hours for single-step calibration; this is annotation effort, not model training.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen LLM; conformal calibration aligns uncertainty and controls help requests while existing robot policies execute actions.","evaluation":"Simulated ambiguous tasks and hardware mobile manipulation, tabletop rearrangement and bimanual scenarios."},"release":{"code":"Available","weights":"Not applicable","data":"Not verified","license":"Apache-2.0 (code); model, dependency and dataset terms are separate.","notes":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms."},"evidence":[{"url":"https://arxiv.org/abs/2307.01928","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/google-research/google-research/master/language_model_uncertainty/README.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."},{"url":"https://raw.githubusercontent.com/google-research/google-research/master/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Code-license text checked directly; this license is not automatically assigned to model weights or third-party data."}],"caveats":["Statistical guarantees depend on the stated calibration / sampling assumptions, not a universal physical-success guarantee.","GPT-3.5 and PaLM-2L-IF comparisons do not all include hardware trials."],"verified":"2026-10-17"},{"id":"act3d","name":"Act3D","title":"Act3D: 3D Feature Field Transformers for Multi-Task Robotic Manipulation","date":"2023-06-30","year":2023,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Predicts precise gripper keyposes by querying a continuous 3D feature field with adaptive coarse-to-fine ghost points.","institution":"Carnegie Mellon University","venue":"CoRL 2023","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2306.17817","project":"https://act3d.github.io/","code":"https://github.com/nickgkan/3d_diffuser_actor"},"policy":{"family":"Other","baseModel":"CLIP RGB/text features lifted to 3D + relative-position Transformer","modelSize":"Not reported","inputs":"Calibrated RGB-D views, language and current gripper state","outputs":"6-DoF end-effector keypose, gripper openness and collision flag; low-level planner connects poses","trainingData":"RLBench demonstrations across single-task/multitask experiments; real demos for eight tasks","trainingTime":"Single-task 200k steps /1 day; multitask 600k /5 days; real multitask 3 days","trainingHardware":"Single-task: 1 V100 32 GB; multitask: 8 V100 32 GB; real inference: RTX 4090","controlRate":"RGB-D sensor 30 Hz; network-to-planner closed-loop rate not reported","trainingMethod":"Coarse-to-fine 3D action detection behavior cloning","evaluation":"92 simulated RLBench tasks across reported splits and eight real tasks"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT (linked later 3D Diffuser Actor code); original code/data terms not verified","notes":"Official project originally links chained-diffuser; later author 3D Diffuser Actor code explicitly includes Act3D."},"evidence":[{"url":"https://arxiv.org/abs/2306.17817","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/nickgkan/3d_diffuser_actor","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://act3d.github.io/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."}],"caveats":["Linked author-maintained later code implements Act3D alongside 3D Diffuser Actor.","Sensor 30 Hz is not the keypose/feedback update frequency."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT (linked later 3D Diffuser Actor code); original code/data terms not verified","notes":"Official project originally links chained-diffuser; later author 3D Diffuser Actor code explicitly includes Act3D."}},"relatedIds":[]},{"id":"reflect","name":"REFLECT","title":"REFLECT: Summarizing Robot Experiences for Failure Explanation and Correction","date":"2023-06-27","year":2023,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Summarizes multisensory robot experience into structured events, explains execution failures and supplies those explanations to a correction planner.","institution":"Columbia University","venue":"CoRL 2023","modalities":["RGB","Depth","Audio","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2306.15724","project":"https://robot-reflect.github.io/","code":"https://github.com/columbia-ai-robotics/reflect"},"policy":{"family":"Robot agent","baseModel":"GPT-4 failure explanation / planning; MDETR, CLIP and AudioCLIP perception modules.","modelSize":"Not reported","inputs":"RGB-D, sound, robot states and the original task / plan, converted into hierarchical scene / event summaries.","outputs":"Failure localization and explanation, then revised high-level robot plans for correction.","trainingData":"RoboFail failure experiences and multisensory execution records; language reasoning uses pretrained models.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen models; hierarchical sensory summarization and progressive verification provide context for failure explanation and replanning.","evaluation":"Simulated failures / correction planning and real UR5e recordings across multiple manipulation scenarios."},"release":{"code":"Available","weights":"Not applicable","data":"Not verified","license":"MIT (code); model, dependency and dataset terms are separate.","notes":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact. The frozen agent itself has no separately trained method-specific checkpoint; upstream model access has its own terms."},"evidence":[{"url":"https://arxiv.org/abs/2306.15724","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/columbia-ai-robotics/reflect/main/README.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."},{"url":"https://raw.githubusercontent.com/columbia-ai-robotics/reflect/main/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Code-license text checked directly; this license is not automatically assigned to model weights or third-party data."}],"caveats":["Real UR5e recordings are collected by human teleoperation to mimic policies; recorded failure explanations do not establish autonomous physical execution of every generated recovery.","Sound is a separate observation channel, not tactile sensing."],"verified":"2026-10-17"},{"id":"rvt","name":"RVT","title":"RVT: Robotic View Transformer for 3D Object Manipulation","date":"2023-06-26","year":2023,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Projects RGB-D point clouds into virtual views and predicts 3 D keyposes with a multi-view Transformer.","institution":"NVIDIA","venue":"CoRL 2023","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2306.14896","project":"https://robotic-view-transformer.github.io/","code":"https://github.com/NVlabs/RVT","weights":"https://huggingface.co/ankgoyal/rvt/tree/main/rvt"},"policy":{"family":"Other","baseModel":"Multi-view Transformer + CLIP text encoder","modelSize":"Not reported","inputs":"Calibrated RGB-D, instruction and proprioceptive state","outputs":"End-effector keypose, rotation, gripper and collision flag","trainingData":"1,800 RLBench demos (100 × 18 tasks); 51 real demos over five tasks / 13 variations","trainingTime":"24 h full RLBench training; matches PerAct peak earlier","trainingHardware":"8 NVIDIA V100 GPUs","controlRate":"11.6 FPS network inference on RTX 3090; motion-planning execution is separate","trainingMethod":"Supervised keypose behavior cloning","evaluation":"18 RLBench tasks and real Franka manipulation"},"release":{"code":"Available","weights":"Available","data":"Not verified","license":"NVIDIA custom license (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2306.14896","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/NVlabs/RVT","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://robotic-view-transformer.github.io/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/NVlabs/RVT/master/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["Virtual projections use measured depth and calibrated geometry.","Training and network FPS exclude the physical duration of motion-planner execution."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Not verified","license":"NVIDIA custom license (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"interaction-warping","name":"Interaction Warping","title":"One-shot Imitation Learning via Interaction Warping","date":"2023-06-21","year":2023,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"A category shape-warping model transfers surface interaction keypoints into grasp and placement poses for unseen object instances.","institution":"Northeastern University / Brown University / Microsoft Research / Google DeepMind / University of Amsterdam","venue":"CoRL 2023","modalities":["RGB","Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2306.12392","project":"https://shapewarping.github.io/","code":"https://github.com/ondrejbiza/shapewarping"},"policy":{"family":"Geometric transfer","baseModel":"Generative shape warping for one-shot manipulation. PCA shape deformation prior, joint object shape/pose inference and warped interaction keypoints.","modelSize":"Not reported","inputs":"Instance-segmented point clouds, category-specific canonical shape/warp model and one grasp/placement demonstration.","outputs":"Transferred SE(3) grasp and placement transformations executed with a robot planner.","trainingData":"Category-level shape model requires aligned training shapes; one interaction demonstration defines a target skill.","trainingTime":"PCA/shape-prior training wall time not reported.","trainingHardware":"Shape/pose inference measured on NVIDIA RTX 2080 Ti.","controlRate":"Approximately 25 seconds per object for shape/pose inference; this is setup latency, not servo rate.","trainingMethod":"Learn shape-warping prior, optimize novel object shape/pose, warp demonstrated surface interaction points.","evaluation":"Three simulated and real relational rearrangement tasks and object placement transfer."},"release":{"code":"Available","weights":"Partial","data":"Partial","license":"MIT code; dependency/model/data terms separate","notes":"Author-linked repository and actual README/tree inspected; weights/data are tracked separately. No installation or reproduction attempted."},"evidence":[{"url":"https://arxiv.org/abs/2306.12392","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://shapewarping.github.io/","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://github.com/ondrejbiza/shapewarping","label":"Official code","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://raw.githubusercontent.com/ondrejbiza/shapewarping/main/LICENSE","label":"Actual source license file","fields":["release.license"],"note":"Scope limited to the file/repository subtree carrying these terms, not paper figures or external models/data."}],"caveats":["The simulation protocol selects one demonstration from an available set using a reconstruction-quality heuristic.","Requires accurate segmentation and a category shape prior; it does not learn from execution failures."],"verified":"2026-10-17"},{"id":"robocat","name":"RoboCat","title":"RoboCat: A Self-Improving Generalist Agent for Robotic Manipulation","date":"2023-06-20","year":2023,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A goal-conditioned generalist learns across robot embodiments, then fine-tunes on small demonstration sets and generates additional data for later generalist training.","institution":"Google DeepMind","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Three-finger hand"],"links":{"paper":"https://arxiv.org/abs/2306.11706"},"policy":{"family":"Autoregressive policy","baseModel":"Decoder-only Gato-style sensorimotor transformer with VQ-GAN visual tokens and future-frame prediction.","modelSize":"Main model: 1.18B parameters, 24 transformer layers; selected ablations use 364M models.","inputs":"Goal image, current visual observations and robot proprioception represented as interleaved tokens.","outputs":"Robot control actions tokenized for each embodiment and task-specific action dimension.","trainingData":"Generalist training covers 240 tasks; adaptation adds 13 tasks, using 100–1,000 human demonstrations per target task and additional model-generated successful data.","trainingTime":"Generalist schedules extend to two million optimization steps; elapsed training duration is not reported here.","trainingHardware":"Not reported","controlRate":"Real Panda and KUKA tasks commonly use 10 Hz control; Sawyer task settings use 10 or 20 Hz. Rates are task / embodiment specific.","trainingMethod":"Offline goal-conditioned sequence learning; target-task fine-tuning updates weights, followed by autonomous data generation and retraining of a new generalist.","evaluation":"Panda, Sawyer and KUKA manipulation in simulation and the real world, including lifting, stacking, insertion and shape matching."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2306.11706","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["RoboCat adaptation updates weights; its self-improvement loop is not demonstration-only frozen ICL.","The full RoboCat system is not the generic dm_robotics infrastructure repository."],"verified":"2026-10-17"},{"id":"sgr","name":"Semantic-Geometric Representation (SGR)","title":"A Universal Semantic-Geometric Representation for Robotic Manipulation","date":"2023-06-18","year":2023,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Fuses pretrained semantic image features with 3D point geometry for language-conditioned manipulation.","institution":"Tsinghua University; Shanghai AI Laboratory; Shanghai Qi Zhi Institute","venue":"CoRL 2023","modalities":["RGB","Depth","Point cloud","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2306.10474","project":"https://semantic-geometric-representation.github.io/","code":"https://github.com/TongZhangTHU/sgr"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Apache-2.0 for repository code; pretrained models, data and figure rights remain separate","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2306.10474","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://semantic-geometric-representation.github.io/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/TongZhangTHU/sgr","label":"Official current implementation linked by author project","fields":["release"],"note":"Actual runnable release and model artifacts checked independently."},{"url":"https://github.com/TongZhangTHU/sgr/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/TongZhangTHU/sgr/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"75 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["SGR is both a perception module and a demonstrated action-learning pipeline; it is not a standalone universal controller checkpoint."],"verified":"2026-10-17","policy":{"family":"Transformer / BC","baseModel":"CLIP semantic branch, PointNeXt geometry branch and fused action prediction network.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Calibrated RGB-D point clouds, optional language and robot proprioception.","outputs":"6-DoF keyframe pose, gripper-open and collision-avoidance indicators.","trainingMethod":"Behavior cloning with pretrained2D features and geometric fusion.","trainingData":"Single-/multi-task simulated and real robot demonstrations.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"RLBench and physical manipulation, including novel semantic attributes.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"libero","name":"LIBERO","title":"LIBERO: Benchmarking Knowledge Transfer for Lifelong Robot Learning","date":"2023-06-05","year":2023,"collections":["benchmark"],"categories":["general-manipulation"],"resourceType":"benchmark","summary":"Language-conditioned manipulation suites for controlled knowledge-transfer studies across spatial, object, goal and long-horizon variations.","institution":"University of Texas at Austin","venue":"NeurIPS 2023 Datasets and Benchmarks","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2306.03310","project":"https://libero-project.github.io/","code":"https://github.com/Lifelong-Robot-Learning/LIBERO"},"benchmark":{"simulator":"MuJoCo via robosuite","simulatorVersion":"Original repository pins robosuite==1.4.0; current dependencies/legacy mujoco-py differ from downstream forks.","taskCount":"130 registered task entries across five suites: Spatial 10, Object 10, Goal 10, LIBERO-90 90 and LIBERO-10 10. LIBERO-100 specifically is 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five-suite task-map snapshot from official source, 130 entries; later perturbation benchmarks separate.","capabilities":["Lifelong learning","Spatial transfer","Object transfer","Goal transfer","Long-horizon behavior"],"metrics":"Success rate and lifelong learning / forward/backward transfer statistics; task-order studies.","robots":"Franka Panda parallel-jaw manipulator.","sensors":"Agent-view and wrist RGB, proprioception, language; optional simulator observations must be declared separately. No tactile stream in original suite.","dataCollection":"Human expert teleoperation with a 3Dconnexion SpaceMouse.","data":"50 human demonstrations per task in official suite datasets.","protocol":"Separate 10-task controlled transfer suites and 100-task diverse set; lifelong task sequences, multitask/pretraining evaluations; versioned init-state sets.","baselines":"Sequential / multitask imitation and lifelong-learning methods from original benchmark; later VLA evaluations use distinct recipes."},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"MIT code; assets/data dependencies may retain additional terms","notes":"Official task definitions, HDF5 demonstration downloads and benchmark scripts public."},"evidence":[{"url":"https://arxiv.org/abs/2306.03310","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://libero-project.github.io/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/Lifelong-Robot-Learning/LIBERO","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://github.com/Lifelong-Robot-Learning/LIBERO/blob/master/libero/libero/benchmark/libero_suite_task_map.py","label":"Official implementation / documentation","fields":["benchmark.tasks","benchmark.taskCount"],"note":"Complete official suite map; counts scoped explicitly."},{"url":"https://arxiv.org/abs/2306.03310","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original data-collection section: 3Dconnexion SpaceMouse; not inferred from another benchmark comparison table."}],"caveats":["Common LIBERO VLA mean over Spatial/Object/Goal/10 covers 40 tasks, while LIBERO-100 is another scope.","High success on fixed task resets does not establish robustness to perturbations."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not verified","data":"Available","license":"MIT code; assets/data dependencies may retain additional terms","notes":"Official task definitions, HDF5 demonstration downloads and benchmark scripts public."}},"relatedIds":[]},{"id":"industreal","name":"IndustReal","title":"IndustReal: Transferring Contact-Rich Assembly Tasks from Simulation to Reality","date":"2023-05-26","year":2023,"collections":["benchmark"],"categories":["precision-assembly","supporting-resources"],"resourceType":"evaluation-tool","summary":"RL sim-to-real assembly tools combine simulation-aware updates, signed-distance-field rewards, sampling curricula and a deployment action integrator.","institution":"University of Southern California / NVIDIA","venue":"RSS 2023","modalities":["RGB","Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2305.17110","project":"https://sites.google.com/nvidia.com/industreal","code":"https://github.com/NVlabs/industreallib"},"benchmark":{"simulator":"Isaac Gym with Factory contacts","simulatorVersion":"Exact Isaac Gym preview build Not reported here; original setup distinct from later Isaac Lab ports.","taskCount":"Peg-in-hole insertion and gear-meshing families, with pick / place / insert stages and multiple part geometries","tasks":["Peg insertion","Gear meshing","Pick","Place","Insert"],"taskListScope":"Two assembly families plus skill-stage names; not a count of five independently evaluated semantic tasks.","capabilities":["Tight-clearance assembly","Perception-initialized execution","Sim-to-real transfer","Low-cycle-time insertion"],"metrics":"Real/sim insertion success, assembly time, robustness to pose/contact variation.","robots":"Franka Panda with a parallel gripper and wrist RGB-D camera.","sensors":"RGB-D perception estimates part pose; low-level policy uses proprioception / estimated target state. Raw images and depth are not necessarily direct actor inputs; no tactile image sensor.","dataCollection":"Online RL interaction in parallel simulation with geometry-based rewards and curricula; no common human-demonstration corpus is required.","data":"Algorithms, assembly assets, trained RL workflows and real deployment tools; no unified demonstration count.","protocol":"Train contact-rich policies in simulation and transfer without physical RL fine-tuning; separate pose-estimation and low-level policy components; 600 physical trials in original report.","baselines":"PPO-based IndustReal; curriculum / reward / action-integrator ablations; Franka control comparisons."},"release":{"code":"Available","weights":"Partial","data":"Partial","license":"Not verified","notes":"industreallib physical deployment code and IsaacGymEnvs training environments are separate repositories."},"evidence":[{"url":"https://arxiv.org/abs/2305.17110","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://sites.google.com/nvidia.com/industreal","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/NVlabs/industreallib","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://arxiv.org/abs/2305.17110","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Depth supports perception initialization; do not equate this with an end-to-end RGB-D policy.","Absence of tactile images does not imply absence of contact-force effects in the controller/physics."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Partial","data":"Partial","license":"Not verified","notes":"industreallib physical deployment code and IsaacGymEnvs training environments are separate repositories."}},"relatedIds":[]},{"id":"furniturebench","name":"FurnitureBench","title":"FurnitureBench: Reproducible Real-World Benchmark for Long-Horizon Complex Manipulation","date":"2023-05-22","year":2023,"collections":["benchmark"],"categories":["precision-assembly"],"resourceType":"benchmark","summary":"Physical miniature furniture assembly with reproducible initial-state difficulty and a matching Isaac Gym / Factory simulator.","institution":"University of Southern California / KAIST","venue":"RSS 2023","modalities":["RGB","Depth","Proprioception"],"environments":["Real world","Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2305.12821","project":"https://clvrai.github.io/furniture-bench/","code":"https://github.com/clvrai/furniture-bench","data":"https://clvrai.github.io/furniture-bench/docs/tutorials/dataset.html"},"benchmark":{"simulator":"FurnitureSim built on Isaac Gym and Factory","simulatorVersion":"Isaac Gym Preview 4 in released setup; exact build should be matched to repository.","taskCount":"8 physical furniture assembly tasks; per-skill and complete-assembly evaluations","tasks":["One Leg","Square Table","Desk","Round Table","Chair","Lamp","Drawer","Cabinet"],"taskListScope":"Complete original eight-furniture suite.","capabilities":["Long-horizon assembly","Grasp / place / insertion / screwing","Error accumulation","Reset-level robustness"],"metrics":"Assembly / skill success under low, medium and high randomization; evaluation time and human reset reproducibility.","robots":"Franka Panda arm with parallel-jaw gripper","sensors":"External and wrist RGB-D cameras; robot proprioception; AprilTag-based part tracking used for reproducible setups and labels.","dataCollection":"VR teleoperation with an Oculus Quest 2 controller, supplemented by keyboard control for fine wrist rotations.","data":"200+ hours, 5,000+ demonstrations; RGB-D, robot states and actions.","protocol":"Three standardized initial-state randomization levels; skill and full-assembly evaluation; physical benchmark complemented by FurnitureSim.","baselines":"Behavior cloning and offline reinforcement-learning baselines; exact suite settings in manuscript."},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"MIT code; dataset/third-party terms separate","notes":"CAD files, data, reset/workspace guides and FurnitureSim environment public."},"evidence":[{"url":"https://arxiv.org/abs/2305.12821","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://clvrai.github.io/furniture-bench/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/clvrai/furniture-bench","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://github.com/clvrai/furniture-bench/blob/main/LICENSE","label":"Official source","fields":["release.license"],"note":"License text read directly; code license does not automatically cover models, data or simulator."},{"url":"https://arxiv.org/abs/2305.12821","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Fiducial-based privileged part states and raw image policy observations should be reported separately.","Furniture assembly does not inherently include tactile sensing."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not verified","data":"Available","license":"MIT code; dataset/third-party terms separate","notes":"CAD files, data, reset/workspace guides and FurnitureSim environment public."}},"relatedIds":[]},{"id":"instruct2act","name":"Instruct2Act","title":"Instruct2Act: Mapping Multi-modality Instructions to Robotic Actions with Large Language Model","date":"2023-05-18","year":2023,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Maps multimodal instructions into executable robot programs through a modular visual-perception and action API.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2305.11176","code":"https://github.com/OpenGVLab/Instruct2Act"},"policy":{"family":"Robot agent","baseModel":"GPT-4 program generation with Segment Anything and CLIP-based visual grounding.","modelSize":"Not reported","inputs":"Text / image instructions, current visual observations and configured perception / manipulation APIs.","outputs":"Executable Python action programs that invoke visual segmentation, object selection and robot-control functions.","trainingData":"Prompt examples and pretrained foundation models; no task-specific robot-policy training in the proposed system.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen foundation models compose perception and control APIs through generated programs.","evaluation":"VIMA-Bench multimodal manipulation tasks and physical UR5 manipulation."},"release":{"code":"Partial — VIMABench implementation","weights":"Not applicable","data":"Not verified","license":"No root repository LICENSE found in the inspected tree; upstream SAM/CLIP and simulator terms remain separate.","notes":"Actual perception/tool execution, online/offline program generation, prompts and VIMABench evaluation source are public. This does not include a complete physical-robot perception/deployment stack or new base-model checkpoint; related ManipVQA/A3VLM releases are separate works."},"evidence":[{"url":"https://arxiv.org/abs/2305.11176","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/OpenGVLab/Instruct2Act","label":"Author-linked repository","fields":["links.code"],"note":"Repository identity; code and checkpoint release status are stated separately."},{"url":"https://github.com/OpenGVLab/Instruct2Act","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["Low-level action functions remain hand-designed / provided components.","The architecture is a modular robot agent rather than a fully trained end-to-end VLA."],"verified":"2026-10-19"},{"id":"ramp","name":"RAMP","title":"RAMP: A Benchmark for Evaluating Robotic Assembly Manipulation and Planning","date":"2023-05-16","year":2023,"collections":["benchmark"],"categories":["precision-assembly"],"resourceType":"benchmark","summary":"Accessible beam-and-peg assembly benchmark testing both planning and precise execution, with hardware plans and a digital twin.","institution":"University of Oxford / Manufacturing Technology Centre / University of Birmingham","venue":"IEEE Robotics and Automation Letters 2024","modalities":["RGB","Force/Torque","Proprioception"],"environments":["Real world","Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2305.09644","project":"https://sites.google.com/oxfordrobotics.institute/ramp","code":"https://github.com/applied-ai-lab/ramp-sim"},"benchmark":{"simulator":"NVIDIA Isaac Sim","simulatorVersion":"Released ramp-sim README specifies NVIDIA Isaac Sim 2022.2.1.","taskCount":"Assemblies grouped into Easy, Medium and Hard; paper baseline evaluates 3 Easy assemblies","tasks":["assembly_easy_1.xml","assembly_easy_2.xml","assembly_easy_3.xml"],"taskListScope":"Complete three currently selectable Easy simulation assemblies in released README; paper additionally defines Medium and Hard goal classes.","capabilities":["Task-and-motion planning","Peg insertion","Beam sliding","Regrasping","Diagnostics and fault recovery"],"metrics":"Correct fully seated peg completion percentage over time; completion time and repeats; partial assemblies count only valid connected components.","robots":"Single-arm physical baseline; kit extensible to other robot systems.","sensors":"AprilTags aid state estimation; force-controlled manipulation primitives; sensor choice not fixed by the kit.","dataCollection":"Not applicable — configurable assembly test kit and evaluation designs; no standard training-demonstration corpus or prescribed collector.","data":"Nine base beams, up to 15 pegs, printable joints, A2 reset template, XML goal descriptions and digital twin; no standard large demonstration dataset.","protocol":"Easy requires 3–4 peg insertions; Medium 4–8 with angled insertion/sliding/regrasping; Hard longer free-form designs. Baseline repeats three Easy designs five times.","baselines":"Rudimentary symbolic planner and Cartesian-impedance manipulation primitives."},"release":{"code":"Available","weights":"Not applicable","data":"Available","license":"GPL-3.0 for ramp-sim/ramp-planner; CAD/material terms require separate inspection","notes":"Simulation, planning and assembly instructions provided in separate resources."},"evidence":[{"url":"https://arxiv.org/abs/2305.09644","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://sites.google.com/oxfordrobotics.institute/ramp","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/applied-ai-lab/ramp-sim","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://github.com/applied-ai-lab/ramp-sim","label":"Official source","fields":["benchmark.simulatorVersion","benchmark.tasks"],"note":"README names simulator version and three selectable XML goals."},{"url":"https://arxiv.org/abs/2305.09644","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["The paper baseline covers Easy assemblies; do not present it as a learned tactile benchmark."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not applicable","data":"Available","license":"GPL-3.0 for ramp-sim/ramp-planner; CAD/material terms require separate inspection","notes":"Simulation, planning and assembly instructions provided in separate resources."}},"relatedIds":[]},{"id":"dexart","name":"DexArt","title":"DexArt: Benchmarking Generalizable Dexterous Manipulation with Articulated Objects","date":"2023-05-09","year":2023,"collections":["benchmark"],"categories":["dexterous-benchmarks"],"resourceType":"benchmark","summary":"Dexterous articulated-object manipulation benchmark centered on transfer to unseen object instances with depth-derived point-cloud observations.","institution":"University of California San Diego","venue":"CVPR 2023","modalities":["Depth","Proprioception"],"environments":["Simulation"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2305.05706","project":"https://www.chenbao.tech/dexart/","code":"https://github.com/Kami-code/dexart-release"},"benchmark":{"simulator":"SAPIEN","simulatorVersion":"Repository setup pins sapien==2.1.0; source package version scope only.","taskCount":"4 articulated manipulation task families","tasks":["Laptop","Faucet","Bucket","Toilet"],"taskListScope":"Complete four task families; object-instance splits are separate generalization configurations.","capabilities":["Articulated manipulation","Dexterous contact","Unseen object generalization","3D representation learning"],"metrics":"Task success on seen / unseen articulated objects and representation-pretraining ablations.","robots":"Robot arm with Allegro Hand.","sensors":"Partial point cloud from depth camera; robot proprioception. Color and optical tactile inputs are not the benchmark focus.","dataCollection":"Online PPO interaction in simulation with point-cloud observations; perception pretraining is a separate stage rather than human demonstrations.","data":"Articulated object assets, RL vision pretraining resources and policy checkpoints; not a fixed human-demonstration benchmark.","protocol":"Train RL on selected object instances; evaluate geometric/kinematic generalization to unseen instances within each family.","baselines":"PPO with different 3D representation encoders/pretraining choices."},"release":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 repository code, verified from actual LICENSE; object assets, pretrained vision models and RL checkpoints retain separate terms.","notes":"Code, object assets, vision pretraining and RL checkpoints linked by official repository."},"evidence":[{"url":"https://arxiv.org/abs/2305.05706","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://www.chenbao.tech/dexart/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/Kami-code/dexart-release","label":"Official repository","fields":["benchmark.simulatorVersion"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://arxiv.org/abs/2305.05706","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."},{"url":"https://github.com/Kami-code/dexart-release","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["Four task families contain many object instances; distinguish object diversity from task count."],"verified":"2026-10-19","releaseByCollection":{"benchmark":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 repository code, verified from actual LICENSE; object assets, pretrained vision models and RL checkpoints retain separate terms.","notes":"Code, object assets, vision pretraining and RL checkpoints linked by official repository."}},"relatedIds":[]},{"id":"vision-foundations-for-touch","name":"Vision Foundations for Tactile Representation","title":"Investigating Vision Foundational Models for Tactile Representation Learning","date":"2023-04-30","year":2023,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Transforms heterogeneous tactile readings into image-like representations so a common pretrained visual backbone can learn touch tasks across sensor designs.","institution":"University of Cambridge; Institute for Infocomm Research, A*STAR; Carnegie Mellon University","venue":"Preprint","modalities":["Tactile"],"environments":["Real world"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2305.00596"},"model":{"role":"Tactile representation and perception framework; no executable robot action head","family":"Tactile encoder","baseModel":"ResNet-18 pretrained on MetaDataset; tactile time-series-to-image transformation permits a shared visual architecture","modelSize":"ResNet-18 configuration; complete learned-system parameter count not reported","inputs":"Sensor-specific tactile readouts from RoboSkin, BioTac, GelSight or Contactile, converted to image-like tensors","outputs":"Tactile embeddings, material classes or fabric-composition predictions","trainingMethod":"Supervised fine-tuning of the visual backbone on transformed tactile data; augmentation and continual-learning transfer experiments","trainingData":"Four tactile sensor datasets; material classification and a separately collected Contactile fabric-composition dataset, with task-specific splits","trainingTime":"Material classification configuration: 100 epochs; wall-clock duration not reported","trainingHardware":"Not reported","inference":"Not reported","evaluation":"Material recognition, robustness to sampling/speed/noise, continual tactile learning and fabric-composition detection; no closed-loop manipulation evaluation."},"release":{"code":"Not verified","weights":"Not verified","data":"Partial — external source datasets described; new Contactile release not independently confirmed","license":"Paper CC BY 4.0; code, checkpoint and source dataset terms not verified","notes":"Source-specific data and foundational vision weights are separate from any new tactile model release."},"evidence":[{"url":"https://arxiv.org/abs/2305.00596","label":"Original paper and first-public date","fields":["title","date","summary","institution","model"],"note":"Sections III–V; model is MetaDataset-pretrained ResNet-18, not an ImageNet-only backbone."}],"caveats":["Time-series tactile readings are transformed into images; that transformation does not add an RGB camera or depth sensor.","A common network architecture and sensor transfer experiments do not establish sensor-free calibration or autonomous robot control."],"relatedIds":["unitac-nv","htt"],"verified":"2026-10-17"},{"id":"act","name":"ACT","title":"Learning Fine-Grained Bimanual Manipulation with Low-Cost Hardware","date":"2023-04-23","year":2023,"collections":["policy"],"categories":["visual-precision-rl"],"resourceType":"policy","summary":"Action Chunking with Transformers learns bimanual fine manipulation from small demonstration sets using a conditional variational autoencoder and temporal ensembling.","institution":"Stanford University","venue":"RSS 2023","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Bimanual","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2304.13705","project":"https://tonyzhaozh.github.io/aloha/","code":"https://github.com/tonyzhaozh/act"},"policy":{"family":"ACT","baseModel":"ResNet-18 + Transformer CVAE","modelSize":"Approximately 80M parameters (original paper)","inputs":"Four RGB views and 14 robot joint/gripper positions","outputs":"Chunks of target joint positions; temporal ensembling combines overlapping predictions","trainingData":"50 demos per real task, except Thread Velcro 100; roughly 10–20 min per task","trainingTime":"Approximately 5 h per task","trainingHardware":"Single RTX 2080 Ti, 11 GB","controlRate":"50 Hz action/control; approximately 10 ms model inference","trainingMethod":"Supervised conditional VAE; latent fixed to its prior mean at deployment","evaluation":"Two simulated and six real ALOHA tasks including battery insertion, condiment-cup opening and threading"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2304.13705","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/tonyzhaozh/act","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://tonyzhaozh.github.io/aloha/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/tonyzhaozh/act/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["The ALOHA hardware paper and ACT algorithm share this canonical entry.","Original ACT uses RGB and joint feedback, not measured depth or touch."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"tacgnn","name":"TacGNN","title":"TacGNN:Learning Tactile-based In-hand Manipulation with a Blind Robot","date":"2023-04-03","year":2023,"collections":["policy"],"categories":["tactile-foundation","visual-precision-rl"],"resourceType":"policy","summary":"A graph tactile state estimator enables vision-free PPO in-hand Bao ding manipulation.","institution":"Tencent Robotics X / University of Hong Kong / Southern University of Science and Technology and collaborators","venue":"Preprint","modalities":["Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2304.00736"},"policy":{"family":"RL","baseModel":"Hierarchical graph tactile perception followed by PPO actor.","modelSize":"Not reported","inputs":"Tactile contact features and hand state; no vision.","outputs":"Hand joint commands for rotating two balls.","trainingData":"Simulated contact state data and RL interaction; exact budget Not reported.","trainingTime":"Not reported","trainingHardware":"V100 GPU reported for GNN experiments; full policy training budget Not reported.","controlRate":"Not reported","trainingMethod":"Tactile graph state prediction then PPO policy learning.","evaluation":"Isaac simulation Bao ding 180-degree rotation, disturbance and hold variants; real-hand transfer."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2304.00736","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."}],"caveats":["Object states are estimated from touch at deployment; not externally tracked vision."],"verified":"2026-10-17"},{"id":"t-dex","name":"T-Dex","title":"Dexterity from Touch: Self-Supervised Pre-Training of Tactile Representations with Robotic Play","date":"2023-03-21","year":2023,"collections":["policy"],"categories":["tactile-il","tactile-foundation"],"resourceType":"policy","summary":"Self-supervised tactile play representations support few-demonstration nonparametric dexterous imitation.","institution":"New York University / Meta AI","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2303.12076"},"policy":{"family":"Retrieval","baseModel":"BYOL tactile encoder and non parametric nearest-demonstration imitation.","modelSize":"Not reported","inputs":"Raw distributed 3-axis tactile arrays plus visual/proprioceptive context in configured variants.","outputs":"Arm/hand commands retrieved from demonstrations.","trainingData":"2.5 h task-agnostic play; six demonstrations per downstream task; play filtered 450k frames to 42 k motion-informative frames.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Self-supervised tactile pretraining followed by non parametric imitation.","evaluation":"Kinova Jaco/Allegro; five dexterous tasks including book opening, bottle cap opening and cup un stacking."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2303.12076","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."}],"caveats":["Play frames and task demonstrations are separate counting units.","Distinct from T-Rex and Tactile-VLA."],"verified":"2026-10-17"},{"id":"rotating-without-seeing","name":"Rotating without Seeing","title":"Rotating without Seeing: Towards In-hand Dexterity through Touch","date":"2023-03-20","year":2023,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"Learns an in-hand rotation controller using tactile and proprioceptive signals without external visual feedback.","institution":"HKUST / UC San Diego","venue":"RSS 2023","modalities":["Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2303.10880","project":"https://touchdexterity.github.io/","code":"https://github.com/YingYuan0414/in-hand-rotation"},"policy":{"family":"RL","baseModel":"Deep RL controller with stacked tactile/proprioceptive state","modelSize":"Not reported","inputs":"Tactile signals, joint positions, previous joint targets and target rotation axis","outputs":"Relative joint updates converted to target positions and PD torques","trainingData":"Simulation RL with domain randomization","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Tactile-conditioned sim-to-real reinforcement learning","evaluation":"Real Allegro in-hand object rotation across shapes"},"release":{"code":"Available","weights":"Not verified","data":"Not applicable","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Author project links in-hand rotation implementation."},"evidence":[{"url":"https://arxiv.org/abs/2303.10880","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://github.com/YingYuan0414/in-hand-rotation","label":"Official repository","fields":["release","links"],"note":"Release status is checked separately from paper claims; a project page alone does not imply released implementation or weights."},{"url":"https://arxiv.org/html/2303.10880","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."},{"url":"https://github.com/YingYuan0414/in-hand-rotation/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["Continuous rotation is different from goal-conditioned arbitrary object reorientation."],"verified":"2026-10-19","releaseByCollection":{"policy":{"code":"Available","weights":"Not verified","data":"Not applicable","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Author project links in-hand rotation implementation."}},"relatedIds":[]},{"id":"dlr-modular-reorientation","name":"DLR Modular Reorientation","title":"Dextrous Tactile In-Hand Manipulation Using a Modular Reinforcement Learning Architecture","date":"2023-03-08","year":2023,"collections":["policy"],"categories":["tactile-foundation","visual-precision-rl"],"resourceType":"policy","summary":"A modular policy and differentiable particle filter enable tactile goal-conditioned cube reorientation.","institution":"German Aerospace Center (DLR)","venue":"Preprint","modalities":["Force/Torque","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2303.04705","project":"https://dlr-alr.github.io/dlr-tactile-manipulation"},"policy":{"family":"RL","baseModel":"Small RL control policy plus D2P2 F differentiable particle filter.","modelSize":"Not reported","inputs":"Joint measurements and estimated object state/goal orientation.","outputs":"DLR Hand II target joint commands.","trainingData":"Simulation rollouts for policy/state estimator training; exact budget Not reported.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Staged policy/estimator learning and iterative estimator-in-the-loop fine-tuning.","evaluation":"Palm-down cube reorientation to 24 quarter-turn goals without cameras."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2303.04705","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://dlr-alr.github.io/dlr-tactile-manipulation","label":"Official author project","fields":["links","release"],"note":"Live project artifact links inspected on2026-10-17."}],"caveats":["Object state is estimated from joint/contact feedback; ground truth state used in train simulation is not runtime observation."],"verified":"2026-10-17"},{"id":"dp","name":"Diffusion Policy","title":"Diffusion Policy: Visuomotor Policy Learning via Action Diffusion","date":"2023-03-07","year":2023,"collections":["policy"],"categories":["visual-precision-rl"],"resourceType":"policy","summary":"Generates action sequences by conditional denoising and executes them with receding-horizon control. Includes precision-sensitive real manipulation.","institution":"Columbia University","venue":"RSS 2023; IJRR expanded version","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2303.04137","project":"https://diffusion-policy.cs.columbia.edu/","code":"https://github.com/real-stanford/diffusion_policy","data":"https://diffusion-policy.cs.columbia.edu/data/","weights":"https://diffusion-policy.cs.columbia.edu/data/experiments/"},"policy":{"family":"Diffusion","baseModel":"ResNet visual encoder + temporal U-Net or diffusion Transformer","modelSize":"Task-dependent: image U-Net table reports 256M diffusion + 22M vision parameters; Transformer table 9M + 22M","inputs":"RGB images and robot state; separate state-based benchmark variants","outputs":"Task-specific continuous action chunks; common image U-Net setting predicts 16 and executes 8 steps","trainingData":"12 simulation tasks across four benchmarks; real demonstrations including 200 Push-T episodes","trainingTime":"12 h per method in real Push-T comparison; other settings differ","trainingHardware":"NVIDIA RTX 3080 used for reported real inference; full training hardware not reported","controlRate":"Real Push-T: 10 Hz commands interpolated to 125 Hz robot control; 0.1 s inference","trainingMethod":"Behavior cloning with conditional DDPM; DDIM inference","evaluation":"Push-T, Robomimic, BlockPush, Franka Kitchen; real pouring, spreading and bimanual manipulation"},"release":{"code":"Available","weights":"Available","data":"Available","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2303.04137","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/real-stanford/diffusion_policy","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://diffusion-policy.cs.columbia.edu/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/real-stanford/diffusion_policy/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."}],"caveats":["Model size, horizon and observation type vary by task and implementation.","Haptic feedback used by a demonstrator does not imply tactile input to the learned policy."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Available","license":"MIT (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"mimicplay","name":"MimicPlay","title":"MimicPlay: Long-Horizon Imitation Learning by Watching Human Play","date":"2023-02-24","year":2023,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Learns latent plans from human play videos and uses them to condition a low-level robot imitation policy for long-horizon manipulation.","institution":"Stanford University; NVIDIA; Georgia Tech; University of Texas at Austin; Caltech","venue":"CoRL 2023","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2302.12422","project":"https://mimic-play.github.io/","code":"https://github.com/j96w/MimicPlay"},"policy":{"family":"Hierarchical imitation","baseModel":"Goal-conditioned latent planner with a Gaussian-mixture output, plus a four-layer / four-head GPT-style visuomotor policy.","modelSize":"Not reported","inputs":"One-shot human or robot video prompt, current visual observations and robot proprioception; latent plans guide low-level execution.","outputs":"Latent hand / interaction plans and continuous robot motor actions.","trainingData":"Ten minutes of human play per environment, around 36,000 video frames; twenty robot demonstrations per task, with a separate forty-demonstration sample-efficiency setting.","trainingTime":"Latent planner: 100,000 iterations, about twelve hours. Low-level policy: 100,000 iterations, about twelve hours.","trainingHardware":"A single GPU machine per stated training stage; GPU model is not reported.","controlRate":"Robot arm control / collection at 17–20 Hz; gripper control at 2 Hz.","trainingMethod":"Offline human-play latent-plan learning and robot behavioral cloning; new video prompts condition the trained hierarchy at deployment.","evaluation":"Fourteen real long-horizon tasks in six environments, plus simulated task generalization / multi-environment studies."},"release":{"code":"Partial","weights":"Partial","data":"Partial","license":"MIT (code); model, dependency and dataset terms are separate.","notes":"README releases simulation training / evaluation code, play data and trained checkpoints. Its reproduction uses robot play, and is not a release of every physical human-play experiment."},"evidence":[{"url":"https://arxiv.org/abs/2302.12422","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/j96w/MimicPlay/main/README.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."},{"url":"https://raw.githubusercontent.com/j96w/MimicPlay/main/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Code-license text checked directly; this license is not automatically assigned to model weights or third-party data."}],"caveats":["Human play pretraining and robot action-policy training are separate data requirements.","A human video prompt supplies task guidance but does not replace the learned low-level robot controller."],"verified":"2026-10-17"},{"id":"awda","name":"Attributed Waypoints and Demonstration Augmentation (AWDA)","title":"One-shot Visual Imitation via Attributed Waypoints and Demonstration Augmentation","date":"2023-02-09","year":2023,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Separates demonstration-conditioned task inference from execution by predicting attributed waypoints that hand-crafted motor primitives can realize.","institution":"University of Illinois Urbana-Champaign","venue":"Preprint","modalities":["RGB","Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2302.04856","project":"https://matthewchang.github.io/awda_site/","code":"https://github.com/MatthewChang/osvi-awda"},"policy":{"family":"Hierarchical imitation","baseModel":"ResNet-18 image features and temporal transformer derived from T-OSVI, with attributed-waypoint prediction heads.","modelSize":"Not reported","inputs":"One visual task demonstration and the initial scene image; the BC-Z variant fuses RGB and depth features.","outputs":"A short sequence of 3D waypoints with grasp / interaction attributes, executed by supplied motor primitives.","trainingData":"Successful task demonstrations augmented with asymmetric demonstration mixup and trajectory synthesis; the paper constructs 10,000 samples per task from 100 successful trajectories.","trainingTime":"500,000 model-training iterations; elapsed duration is not reported.","trainingHardware":"Not reported","controlRate":"Waypoint-network forward pass takes approximately 7 ms on an NVIDIA A40; primitive execution is a separate cost.","trainingMethod":"Offline task-inference learning with soft-DTW trajectory supervision and augmentation; new demonstrations condition inference without task-specific fine-tuning.","evaluation":"MetaWorld, T-OSVI and BC-Z one-shot visual imitation settings."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."},"evidence":[{"url":"https://arxiv.org/abs/2302.04856","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/MatthewChang/osvi-awda/main/README.md","label":"Author repository README","fields":["links.code","release.code","release.notes"],"note":"Public author repository includes implementation / execution instructions; this does not certify every physical setup or experiment artifact."}],"caveats":["Measured depth is scoped to the BC-Z feature-fusion variant, not asserted for every benchmark.","Hand-crafted execution primitives address final-contact control; the model does not independently learn every motor action."],"verified":"2026-10-17"},{"id":"rt1","name":"RT-1","title":"RT-1: Robotics Transformer for Real-World Control at Scale","date":"2022-12-13","year":2022,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"A language-conditioned transformer maps camera observations directly to real robot controls at scale.","institution":"Robotics at Google","venue":"Preprint","modalities":["RGB","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2212.06817","project":"https://robotics-transformer1.github.io/","code":"https://github.com/google-research/robotics_transformer","weights":"https://github.com/google-research/robotics_transformer/tree/master/trained_checkpoints"},"policy":{"family":"Transformer policy","baseModel":"FiLM EfficientNet-B3, TokenLearner and transformer action decoder.","modelSize":"35M total parameters; FiLM image/instruction tokenizer is a 16M component.","inputs":"Recent RGB camera observations and natural-language instruction.","outputs":"Discretized arm/gripper/base/mode actions with 256 bins per action dimension.","trainingData":"Approximately 130k episodes across over 700 tasks, collected with 13 robots over 17 months.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"3 Hz closed-loop RT-1 inference.","trainingMethod":"Large-scale multi-task behavior cloning with image-language conditioning.","evaluation":"About 3,000 real-world trials probing task/object/background generalization and diverse robot data."},"release":{"code":"Available","weights":"Available","data":"Partial","license":"Apache-2.0 for repository code; external model, dataset and dependency terms remain separate.","notes":"Original repository includes three trained RT-1 checkpoint variants. RT-1-X is a later, separate paper/model release."},"evidence":[{"url":"https://arxiv.org/abs/2212.06817","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://robotics-transformer1.github.io/","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/google-research/robotics_transformer","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."},{"url":"https://github.com/google-research/robotics_transformer/blob/HEAD/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Actual license file inspected; code license does not automatically license data or weights."}],"caveats":["Seventeen months is data collection duration, not network training time.","Language task conditioning alone is not classified as robot ICL."],"verified":"2026-10-17"},{"id":"touch-and-go","name":"Touch and Go","title":"Touch and Go: Learning from Human-Collected Vision and Touch","date":"2022-11-22","year":2022,"collections":["policy"],"categories":["tactile-representation"],"resourceType":"model","summary":"Human-collected RGB/GelSight data trains cross-modal contrastive encoders evaluated through frozen-feature perception transfer.","institution":"University of Michigan; Carnegie Mellon University","venue":"NeurIPS 2022 Datasets and Benchmarks","modalities":["RGB","Tactile"],"environments":["Real-world recordings"],"embodiments":[],"links":{"paper":"https://arxiv.org/abs/2211.12498","project":"https://touch-and-go.github.io/","code":"https://github.com/fredfyyang/Touch-and-Go","data":"https://drive.google.com/drive/folders/1NDasyshDCL9aaQzxjn_-Q5MBURRT360B"},"release":{"code":"Available","weights":"Not released","data":"Available","license":"BSD-2-Clause-style terms for visuo-tactile CMC code; CC BY 4.0 dataset/project; separate upstream stylization components retain their own licenses","notes":"Actual encoder training/linear-probing code exists. Encoder README still marks pretrained models unreleased; no checkpoint files or download found. Public data directory lists dataset/, category_reference.txt, extract_frame.py and label.txt. Future-prediction implementation was not identified in the released tree."},"model":{"role":"Transferable visual-tactile contrastive encoders with supporting human-touch corpus","family":"Multimodal representation","baseModel":"Separate ResNet-18/ResNet-50 encoders with Contrastive Multiview Coding (CMC).","modelSize":"Not reported; backbone variants are identified without assigning an unreported complete parameter count.","inputs":"Paired RGB and GelSight images during contrastive pretraining; tactile images for frozen-feature downstream classifiers.","outputs":"Normalized visual/tactile embeddings; linear heads predict material properties and grasp outcomes.","trainingMethod":"Bidirectional paired-versus-mismatched contrastive learning; frozen backbones and 60-epoch linear probes.","trainingData":"Touch and Go: approximately 246K paired frames, 13.9K touches, 20 materials and an extrapolated 3,971 objects. The representation experiment separates 5,172 unlabeled pretraining touches from labeled evaluation splits.","trainingTime":"240 pretraining epochs; wall-clock duration not reported.","trainingHardware":"Four NVIDIA RTX 2080 Ti GPUs in the manuscript; released example scripts select two devices.","inference":"No robot action frequency reported; inference is encoder/classifier evaluation.","evaluation":"Offline transfer to Feeling of Success grasp-outcome prediction and material/hardness/roughness classification; separate stylization and future-touch prediction demonstrations."},"evidence":[{"url":"https://arxiv.org/abs/2211.12498","label":"Original manuscript and submission history","fields":["title","date","institution","model","modalities","environments"],"note":"Sections 4.1/5.1 and Appendix E verify contrastive learning, encoder transfer, splits and compute. Grasp-outcome prediction is offline classification, not action execution."},{"url":"https://touch-and-go.github.io/","label":"Official project, corpus interface and licensing","fields":["links","release.data","release.license","model.trainingData"],"note":"Project links the original public corpus; source datasheet distinguishes frames, touches and extrapolated objects."},{"url":"https://github.com/fredfyyang/Touch-and-Go/blob/main/Visuo-tactile%20contrastive%20learning/README.md","label":"Released encoder scope and checkpoint status","fields":["release.code","release.weights","model.baseModel"],"note":"Actual CMC training/evaluation files and ResNet-18/50 scripts verified; pretrained-model checkbox remains unchecked."},{"url":"https://github.com/fredfyyang/Touch-and-Go/blob/main/Visuo-tactile%20contrastive%20learning/LICENSE","label":"Encoder implementation license","fields":["release.license"],"note":"Two redistribution conditions inspected; stylization subdirectory includes multiple upstream licenses."},{"url":"https://drive.google.com/drive/folders/1NDasyshDCL9aaQzxjn_-Q5MBURRT360B","label":"Actual public data file listing","fields":["release.data"],"note":"Anonymous directory listing inspected, not only a download link or author release claim."}],"caveats":["This record describes a pretrained representation and corpus; it does not introduce an executable robot policy.","Grasp-outcome accuracy uses held-out recorded grasps, and does not establish closed-loop grasp control.","Object-instance count is extrapolated from a sample of videos rather than exhaustively annotated."],"verified":"2026-10-17"},{"id":"dexpoint","name":"DexPoint","title":"DexPoint: Generalizable Point Cloud Reinforcement Learning for Sim-to-Real Dexterous Manipulation","date":"2022-11-17","year":2022,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Point-cloud reinforcement learning and imagined hand geometry enable generalizable sim-to-real dexterous grasping.","institution":"University of California San Diego; Hong Kong University of Science and Technology","venue":"CoRL 2022","modalities":["Depth","Point cloud","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2211.09423","project":"https://yzqin.github.io/dexpoint/","code":"https://github.com/yzqin/dexpoint_sim"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT for repository code; pretrained models, data and figure rights remain separate","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2211.09423","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://yzqin.github.io/dexpoint/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/yzqin/dexpoint_sim","label":"Official current implementation linked by author project","fields":["release"],"note":"Actual runnable release and model artifacts checked independently."},{"url":"https://github.com/yzqin/dexpoint_sim/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/yzqin/dexpoint_sim/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"237 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["Contact-based reward is simulated training supervision, not measured tactile input at deployment."],"verified":"2026-10-17","policy":{"family":"RL / sim-to-real","baseModel":"Point-cloud feature extraction with sensed scene, imagined hand cloud and proprioception; actor-critic control.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Observed depth-derived point cloud, robot hand geometry and proprioception.","outputs":"Arm/Allegro hand motor actions.","trainingMethod":"Multi-object RL with contact-based reward and imagined hand points; sim-to-real transfer.","trainingData":"Simulation object categories and randomized physical properties; no physical policy finetuning.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Simulated and physical xArm6/Allegro grasping of novel category instances.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"relational-ndf","name":"Relational NDF","title":"SE(3)-Equivariant Relational Rearrangement with Neural Descriptor Fields","date":"2022-11-17","year":2022,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Equivariant neural descriptor fields identify corresponding interaction frames for rearranging two novel objects.","institution":"Massachusetts Institute of Technology","venue":"CoRL 2022","modalities":["RGB","Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2211.09786","project":"https://anthonysimeonov.github.io/r-ndf/","code":"https://github.com/anthonysimeonov/relational_ndf"},"policy":{"family":"Geometric transfer","baseModel":"SE(3)-equivariant relational geometric policy. Category-specific Neural Descriptor Fields, local-frame matching and optional energy-based relation refinement.","modelSize":"Not reported","inputs":"Two accurately instance-segmented point clouds with known object identities, approximately five to ten relational demonstrations and one annotated 3D interaction keypoint.","outputs":"Relative object transformation, grasp/placement poses and a MoveIt joint trajectory.","trainingData":"Approximately 100,000 synthetic 3D samples per category; five to ten demonstrations specify a relation.","trainingTime":"150,000 NDF iterations, approximately half a day per category.","trainingHardware":"One NVIDIA RTX 3090, batch size 16.","controlRate":"Pose inference optimization uses 650 iterations and multiple starts; universal control Hz not reported.","trainingMethod":"Pretrain implicit category shape descriptors, optimize consistent interaction frames and match them on novel instances.","evaluation":"Three multi-object rearrangement tasks in simulation and on a real robot, with arbitrary initial SE(3) poses."},"release":{"code":"Available","weights":"Available","data":"Partial","license":"MIT code; dependency/model/data terms separate","notes":"Author-linked repository and actual README/tree inspected; weights/data are tracked separately. No installation or reproduction attempted."},"evidence":[{"url":"https://arxiv.org/abs/2211.09786","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://anthonysimeonov.github.io/r-ndf/","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://github.com/anthonysimeonov/relational_ndf","label":"Official code","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://raw.githubusercontent.com/anthonysimeonov/relational_ndf/master/LICENSE.md","label":"Actual source license file","fields":["release.license"],"note":"Scope limited to the file/repository subtree carrying these terms, not paper figures or external models/data."}],"caveats":["Requires known categories and accurately segmented object point clouds.","The first public paper is November 17, 2022; PMLR volume publication in 2023 does not change that first date."],"verified":"2026-10-17"},{"id":"tax-pose","name":"TAX-Pose","title":"TAX-Pose: Task-Specific Cross-Pose Estimation for Robot Manipulation","date":"2022-11-17","year":2022,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Task-specific cross-object correspondence predicts a relative placement pose for a downstream motion planner.","institution":"Carnegie Mellon University","venue":"CoRL 2022","modalities":["Depth","Point cloud"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2211.09325","project":"https://sites.google.com/view/tax-pose/home","code":"https://github.com/r-pad/taxpose"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT for repository code; pretrained models, data and figure rights remain separate","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2211.09325","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://sites.google.com/view/tax-pose/home","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/r-pad/taxpose","label":"Official current implementation linked by author project","fields":["release"],"note":"Actual runnable release and model artifacts checked independently."},{"url":"https://github.com/r-pad/taxpose/blob/main/LICENSE","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/r-pad/taxpose/tree/main","label":"Actual repository content inventory","fields":["release.code"],"note":"705 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["Uses segmented object pairs and a downstream planner; placement prediction alone does not address all contact dynamics."],"verified":"2026-10-17","policy":{"family":"Geometric transfer","baseModel":"DGCNN/transformer cross-object attention, residual correspondences and differentiable weighted SVD.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Segmented action/anchor object point clouds.","outputs":"Task-specific SE(3) cross-pose and planned robot placement trajectory.","trainingMethod":"Supervised cross-pose learning with displacement/correspondence/consistency losses.","trainingData":"Simulation and real demonstrations, including low-data ten-demonstration settings.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Unseen-object relative placement in simulation and physical mug/pan-style manipulation.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"vima","name":"VIMA","title":"VIMA: General Robot Manipulation with Multimodal Prompts","date":"2022-10-06","year":2022,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A transformer policy follows interleaved text-and-image prompts using object-centric scene tokens and prompt cross-attention.","institution":"Not reported","venue":"ICML 2023","modalities":["RGB","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2210.03094","project":"https://vimalabs.github.io","code":"https://github.com/vimalabs/VIMA","benchmark":"https://github.com/vimalabs/VimaBench","data":"https://huggingface.co/datasets/VIMA/VIMA-Data"},"policy":{"family":"In-context policy","baseModel":"Object-centric transformer with T5-Base text encoding and a Mask R-CNN object tokenizer.","modelSize":"Seven policy scales from 2M to 200M parameters; these counts exclude the constant 111M T5-Base encoder.","inputs":"Interleaved language / image prompts and two RGB scene views, represented as object tokens; the observation encoder also receives the end-effector type (suction or spatula), rather than full joint proprioception.","outputs":"Autoregressive pick-and-place action parameters, including discretized position and rotation targets.","trainingData":"Procedurally generated VIMA-Bench expert demonstrations; data-scaling experiments use fractions of the released imitation dataset.","trainingTime":"The largest reported experiment takes approximately one day.","trainingHardware":"Cluster nodes with eight NVIDIA V100 GPUs; distributed data-parallel training.","controlRate":"Not reported","trainingMethod":"Offline behavioral cloning with prompt cross-attention and autoregressive action prediction; new multimodal prompts condition inference without task-specific updates.","evaluation":"VIMA-Bench generalization levels covering object placement, novel combinations, novel objects and novel tasks."},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"MIT code; model and dataset terms separate","notes":"Official VIMA implementation and VIMA-Bench simulator are distinct repositories. Model-card instructions link pretrained weights; dataset access is tracked independently."},"evidence":[{"url":"https://arxiv.org/abs/2210.03094","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/vimalabs/VIMA","label":"Official implementation, README and file/license inspection on 19 October 2026","fields":["links","release","venue"],"note":"Official VIMA implementation and VIMA-Bench simulator are distinct repositories. Model-card instructions link pretrained weights; dataset access is tracked independently. MIT code; model and dataset terms separate."},{"url":"https://huggingface.co/datasets/VIMA/VIMA-Data","label":"Official dataset file inventory","fields":["links","release.data"],"note":"Official Hugging Face dataset API lists vima.zip with ungated access; dataset terms remain separate from MIT implementation licensing."}],"caveats":["The 2M–200M model-size range excludes the text encoder.","Multimodal prompting is broader than full sensorimotor demonstration ICL; not every prompt is a robot trajectory."],"verified":"2026-10-19"},{"id":"vtt","name":"Visuo-Tactile Transformers","title":"Visuo-Tactile Transformers for Manipulation","date":"2022-09-30","year":2022,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Cross-modal transformer representations support latent model-based RL and manipulation planning.","institution":"University of Michigan","venue":"Preprint","modalities":["RGB","Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2210.00121","project":"https://www.mmintlab.com/vtt"},"policy":{"family":"Hybrid","baseModel":"VTT self/cross attention with SLAC-style latent dynamics and policy learning.","modelSize":"Not reported","inputs":"Visual and tactile observations.","outputs":"Latent dynamics/robot actions for planning and control.","trainingData":"Four simulated manipulation tasks and real pushing interaction; exact corpus count Not reported.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Joint modality representation/objective learning within model-based RL.","evaluation":"Four simulation tasks andone real-world pushing task."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2210.00121","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://www.mmintlab.com/vtt","label":"Official author project","fields":["links","release"],"note":"Live artifact status reviewed separately from paper claims."}],"caveats":["Parameter-matched baseline comparisons are an architecture ablation, not cross-task generalist policy training."],"verified":"2026-10-17"},{"id":"progprompt","name":"ProgPrompt","title":"ProgPrompt: Generating Situated Robot Task Plans using Large Language Models","date":"2022-09-22","year":2022,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Prompts an LLM to produce situated robot programs using an allowed action API, object inventory and assertion-style feedback.","institution":"Not reported","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2209.11302","project":"https://progprompt.github.io/","code":"https://github.com/NVlabs/progprompt-vh"},"policy":{"family":"Robot agent","baseModel":"GPT-3-family language-model program generation with executable robot / simulator APIs.","modelSize":"Not reported","inputs":"Language task, code exemplars, available actions and objects, and execution / assertion feedback.","outputs":"Python-like task programs with action calls and conditional checks, executed by the configured robot skills.","trainingData":"Few-shot task-program examples; no target-task language-model parameter training.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen language model synthesizes programs grounded by the action / object inventory and runtime assertions.","evaluation":"VirtualHome household task planning and real Franka tabletop manipulation."},"release":{"code":"Partial — VirtualHome implementation","weights":"Not applicable","data":"Not verified","license":"NVIDIA License — see repository LICENSE for restrictions","notes":"Official release explicitly targets VirtualHome experiments; physical-robot integration is not established by this repository."},"evidence":[{"url":"https://arxiv.org/abs/2209.11302","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/NVlabs/progprompt-vh","label":"Official implementation, README and file/license inspection on 19 October 2026","fields":["links","release"],"note":"Official release explicitly targets VirtualHome experiments; physical-robot integration is not established by this repository. NVIDIA License — see repository LICENSE for restrictions."}],"caveats":["VirtualHome results and physical manipulation results use different environments and action abstractions.","Available skills and object inventories are supplied interfaces rather than learned capabilities of the base LLM."],"verified":"2026-10-19"},{"id":"code-as-policies","name":"Code as Policies (CaP)","title":"Code as Policies: Language Model Programs for Embodied Control","date":"2022-09-16","year":2022,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Generates hierarchical Python programs that compose perception and robot-control APIs from language instructions.","institution":"Google Research; University of California, Berkeley","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2209.07753","project":"https://code-as-policies.github.io","code":"https://github.com/google-research/google-research/tree/master/code_as_policies"},"policy":{"family":"Robot agent","baseModel":"Codex code-generation language models with recursively generated helper functions.","modelSize":"Not reported","inputs":"Language instruction, code examples and outputs from configured perception / robot APIs.","outputs":"Executable language-model programs that compose spatial computations, perception functions and robot-control calls.","trainingData":"Few-shot code prompts; no target-task language-model fine-tuning.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen model; hierarchical program synthesis recursively implements undefined helper functions and executes code through robot APIs.","evaluation":"Real tabletop manipulation, spatial reasoning and drawing, plus simulated language-conditioned manipulation / embodied-control studies."},"release":{"code":"Partial — examples and simulation material","weights":"Not applicable","data":"Not verified","license":"Apache-2.0 for repository code; hosted APIs keep their own terms","notes":"Project-linked code is the code_as_policies subdirectory; provided examples do not establish a complete release of the physical robot system."},"evidence":[{"url":"https://arxiv.org/abs/2209.07753","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/google-research/google-research/tree/master/code_as_policies","label":"Official implementation, README and file/license inspection on 19 October 2026","fields":["links","release"],"note":"Project-linked code is the code_as_policies subdirectory; provided examples do not establish a complete release of the physical robot system. Apache-2.0 for repository code; hosted APIs keep their own terms."}],"caveats":["The generated program depends on the provided perception and action API vocabulary.","An API call can encapsulate a complete motion primitive; it should not be described as direct joint-level language-model control."],"verified":"2026-10-19"},{"id":"peract","name":"PerAct","title":"Perceiver-Actor: A Multi-Task Transformer for Robotic Manipulation","date":"2022-09-12","year":2022,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Language-conditioned behavior cloning detects gripper actions in a voxelized scene using a Perceiver Transformer.","institution":"University of Washington / NVIDIA","venue":"CoRL 2022","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2209.05451","project":"https://peract.github.io/","code":"https://github.com/peract/peract","data":"https://github.com/peract/peract#pre-generated-datasets","weights":"https://github.com/peract/peract/releases/tag/v1.0.0"},"policy":{"family":"Other","baseModel":"CLIP text encoder + 3D voxel embedding + Perceiver Transformer","modelSize":"25.2M Perceiver Transformer parameters (reported by GNFactor comparison); total including CLIP not separately reported","inputs":"Calibrated RGB-D views, language, gripper state and time","outputs":"6-DoF end-effector keypose, gripper open/close and collision flag; motion planner connects keyposes","trainingData":"18 RLBench tasks, 100 demos each; additional 10-demo experiments and real Franka demonstrations","trainingTime":"600k iterations / 16 days for original full multitask model","trainingHardware":"8 NVIDIA V100 GPUs, batch 16","controlRate":"Keypose model; RVT measures 4.9 FPS on RTX 3090, excluding planned motion duration","trainingMethod":"Keyframe behavior cloning with SE(3) augmentation","evaluation":"18 simulated tasks / 249 variations and seven real Franka tasks"},"release":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."},"evidence":[{"url":"https://arxiv.org/abs/2209.05451","label":"Paper / technical report","fields":["date","title","policy"],"note":"Main paper and supplementary implementation sections; v1 date retained."},{"url":"https://github.com/peract/peract","label":"Official repository","fields":["release","policy.baseModel"],"note":"Release and deployment scope checked in README."},{"url":"https://peract.github.io/","label":"Author project page","fields":["summary","imageCandidates"],"note":"Official project, not an independent replication."},{"url":"https://raw.githubusercontent.com/peract/peract/main/LICENSE","label":"Code license","fields":["release.license"],"note":"Code license only; separately named pretrained models may have additional terms."},{"url":"https://arxiv.org/abs/2308.16891","label":"GNFactor parameter comparison","fields":["policy.modelSize"],"note":"25.2M is Perceiver Transformer only, excluding CLIP."}],"caveats":["Keyframe prediction plus motion planning is not a continuous high-rate feedback policy.","100³ voxel discretization limits spatial resolution; current agent has no temporal memory."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Available","weights":"Available","data":"Available","license":"Apache-2.0 (code); model and data terms not verified","notes":"Availability refers to linked author release; no installation or training was performed."}},"relatedIds":[]},{"id":"tactilegym2","name":"Tactile Gym 2.0","title":"Tactile Gym 2.0: Sim-to-real Deep Reinforcement Learning for Comparing Low-cost High-Resolution Robot Touch","date":"2022-07-21","year":2022,"collections":["benchmark"],"categories":["tactile-benchmarks"],"resourceType":"benchmark","summary":"PyBullet-based tactile-control environments supporting comparative sim-to-real studies across marker-based and shading-based optical sensors.","institution":"University of Bristol","venue":"IEEE Robotics and Automation Letters / IROS 2022","modalities":["Tactile","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Robot arm tactile fingertip"],"links":{"paper":"https://arxiv.org/abs/2207.10763","code":"https://github.com/NICESONY/tactile_gym_2"},"benchmark":{"simulator":"PyBullet","simulatorVersion":"Exact PyBullet build Not reported here; repository setup defines dependencies.","taskCount":"3 physically interactive tasks in the paper; repository includes additional environments","tasks":["Object pushing","Edge following","Surface following"],"taskListScope":"Complete three-task physical validation suite; not the full growing repository environment list.","capabilities":["Tactile servoing","Contour following","Surface following","Non-prehensile pushing","Cross-sensor transfer"],"metrics":"Task-specific tracking / pushing reward and physical performance; sim-real qualitative and quantitative evaluation.","robots":"Simulation testing mainly UR5; Panda and Kuka iiwa support is preliminary. Real low-cost 4-DoF arm used for lowered-cost validation.","sensors":"TacTip, DIGIT, DigiTac optical tactile sensors; sensor-specific sim-to-real image adaptation.","dataCollection":"PPO/RAD-PPO simulation interaction; separate sim-to-real image-translation data use randomized robot contacts against edge/surface stimuli.","data":"RL interaction rather than a standardized demonstration corpus; pretrained PPO / RAD-PPO agents downloadable.","protocol":"One sim-to-real approach tested with three sensor designs across three physically interactive tasks.","baselines":"PPO and RAD-PPO via Stable-Baselines3; observation-space comparisons."},"release":{"code":"Available","weights":"Available","data":"Not applicable","license":"GPL-3.0 code; sensor and dependency terms separate","notes":"Environment and SB3 training/evaluation scripts public; pretrained example agents linked."},"evidence":[{"url":"https://arxiv.org/abs/2207.10763","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://github.com/NICESONY/tactile_gym_2","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://github.com/NICESONY/tactile_gym_2/blob/main/LICENSE","label":"Official source","fields":["release.license"],"note":"License text read directly; code license does not automatically cover models, data or simulator."},{"url":"https://arxiv.org/abs/2207.10763","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Single tactile fingertip control does not constitute a complete gripper pick-align-insert benchmark."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Available","data":"Not applicable","license":"GPL-3.0 code; sensor and dependency terms separate","notes":"Environment and SB3 training/evaluation scripts public; pretrained example agents linked."}},"relatedIds":[]},{"id":"inner-monologue","name":"Inner Monologue","title":"Inner Monologue: Embodied Reasoning through Planning with Language Models","date":"2022-07-12","year":2022,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Uses language feedback about observed objects, success and human interaction to replan a sequence of available robot skills.","institution":"Google Research; University of California, Berkeley","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper","Mobile manipulator"],"links":{"paper":"https://arxiv.org/abs/2207.05608","project":"https://innermonologue.github.io"},"policy":{"family":"Hybrid","baseModel":"InstructGPT-1.3B for tabletop conditions; PaLM-540B for real kitchen mobile manipulation. CLIPort / TransporterNets, heuristic or learned low-level policies depend on the setting.","modelSize":"1.3B or 540B language-model component, depending on the experiment; these are not full robot-system counts.","inputs":"Task instruction and language feedback produced by object recognition, success detection, scene description and optional human answers.","outputs":"Replanned low-level skill sequences, executed by the configured tabletop or mobile-manipulation policies.","trainingData":"No target-task LLM weight updates; pretrained skill policies and success / recognition models have separate training.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen language planner integrates closed-loop textual feedback while existing policies perform physical actions.","evaluation":"Simulated tabletop rearrangement, real tabletop rearrangement and real kitchen mobile manipulation."},"release":{"code":"Not verified","weights":"Not applicable","data":"Not verified","license":"Not verified","notes":"The paper is public; implementation and artifact availability require separate verification."},"evidence":[{"url":"https://arxiv.org/abs/2207.05608","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."}],"caveats":["Simulation can provide ground-truth feedback; real settings use learned or heuristic detectors.","Planner and skill backbone differ across the three experimental settings."],"verified":"2026-10-17"},{"id":"prompt-dt","name":"Prompt-DT","title":"Prompting Decision Transformer for Few-Shot Policy Generalization","date":"2022-06-27","year":2022,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A trajectory-prompted decision transformer adapts to held-out control tasks without test-time gradient updates, including Jaco dialing and Sawyer reaching.","institution":"Carnegie Mellon University / Mila / MIT-IBM Watson AI Lab","venue":"ICML 2022","modalities":["Proprioception"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2206.13499","project":"https://mxu34.github.io/PromptDT/","code":"https://github.com/mxu34/prompt-dt","data":"https://drive.google.com/drive/folders/1six767uD8yfdgoGIYW86sJY-fmMdYq7e"},"policy":{"family":"Autoregressive policy","baseModel":"Causal Decision Transformer with return/state/action trajectory prompt tokens.","modelSize":"Not reported","inputs":"Current state, previous actions and returns-to-go plus a short demonstration-trajectory prompt; state-based control.","outputs":"Continuous actions; six joint commands in Jaco Dial and XYZ end-effector control in Meta-World reach-v2.","trainingData":"Offline task trajectories across five control benchmarks; Dial uses 6 training / 4 test target numbers, and reach-v2 uses 15 training / 5 test goals.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Offline autoregressive action regression with a trajectory prompt; no target-task weight update.","evaluation":"Jaco Dial and Meta-World reach-v2, alongside cheetah / ant locomotion benchmarks."},"release":{"code":"Available","weights":"Not released","data":"Partial","license":"Not verified","notes":"Official code and example offline datasets; no released trained policy checkpoint identified."},"evidence":[{"url":"https://arxiv.org/abs/2206.13499","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://mxu34.github.io/PromptDT/","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/mxu34/prompt-dt","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."}],"caveats":["State-based arm experiments justify inclusion; locomotion scores are not manipulation evidence."],"verified":"2026-10-17"},{"id":"bi-dexhands","name":"Bi-DexHands","title":"Towards Human-Level Bimanual Dexterous Manipulation with Reinforcement Learning","date":"2022-06-17","year":2022,"collections":["benchmark"],"categories":["dexterous-benchmarks"],"resourceType":"benchmark","summary":"GPU-parallel bimanual Shadow Hand benchmark for single/multi-agent RL, offline learning, multitask learning and meta-learning.","institution":"Peking University / cooperating institutions","venue":"NeurIPS 2022 Datasets and Benchmarks","modalities":["Proprioception","Force/Torque"],"environments":["Simulation"],"embodiments":["Dexterous hand","Bimanual"],"links":{"paper":"https://arxiv.org/abs/2206.08686","project":"https://pku-marl.github.io/DexterousHands/","code":"https://github.com/PKU-MARL/DexterousHands"},"benchmark":{"simulator":"NVIDIA Isaac Gym","simulatorVersion":"Current official README supports Isaac Gym Preview Release 3/4; exact original experiment binary not further specified.","taskCount":"20 evaluated bimanual task environments; repository may contain additional variants","tasks":["ShadowHandOver","ShadowHandLiftUnderarm","ShadowHandCatchUnderarm","ShadowHandDoorOpenInward","ShadowHandOver2Underarm","ShadowHandDoorOpenOutward","ShadowHandCatchAbreast","ShadowHandDoorCloseInward","ShadowHandTwoCatchUnderarm","ShadowHandDoorCloseOutward","ShadowHandPushBlock","ShadowHandPen","ShadowHandScissors","ShadowHandSwingCup","ShadowHandBlockStack","ShadowHandReOrientation","ShadowHandPourWater","ShadowHandSwitch","ShadowHandGraspAndPlace","ShadowHandBottleCap"],"taskListScope":"Complete twenty task identifiers in original README benchmark results table.","capabilities":["Bimanual coordination","Catching","Handovers","Reorientation","Articulated manipulation","Multi-agent cooperation"],"metrics":"Episodic return / normalized offline score; success diagnostics; training curves over 10 trials.","robots":"Two Shadow Hands","sensors":"Proprioceptive state, object state and fingertip contact forces in state-based baseline observations; these are not optical tactile images.","dataCollection":"RL policy interaction. Offline subsets use random-policy, replay-buffer, PPO-medium and medium/expert rollouts on selected tasks.","data":"Offline random/replay/medium/medium-expert sets for Hand Over and Door Open Outward; many object assets.","protocol":"Original online results: 100M environment steps, 2,048 parallel simulations; PPO/SAC/MAPPO/HAPPO evaluated on 20 tasks. Offline comparisons on two tasks; MT1/4/20 and ML1/4/20 protocols.","baselines":"PPO, SAC, MAPPO, HAPPO; BC, BCQ, TD3+BC, IQL; multitask PPO and ProMP."},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"Apache-2.0 code; simulator and third-party asset terms separate","notes":"Task environments, algorithm implementations and offline data links public."},"evidence":[{"url":"https://arxiv.org/abs/2206.08686","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://pku-marl.github.io/DexterousHands/","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/PKU-MARL/DexterousHands","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://github.com/PKU-MARL/DexterousHands/blob/main/LICENSE","label":"Official source","fields":["release.license"],"note":"License text read directly; code license does not automatically cover models, data or simulator."},{"url":"https://arxiv.org/abs/2206.08686","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"AppendixA.3 offline collection; online RL and selected offline task subsets are distinct."}],"caveats":["Privileged state-based RL setting should not be compared directly with image-only imitation policies."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not verified","data":"Available","license":"Apache-2.0 code; simulator and third-party asset terms separate","notes":"Task environments, algorithm implementations and offline data links public."}},"relatedIds":[]},{"id":"factory","name":"Factory","title":"Factory: Fast Contact for Robotic Assembly","date":"2022-05-07","year":2022,"collections":["benchmark"],"categories":["precision-assembly","supporting-resources"],"resourceType":"simulator","summary":"Contact simulation methods and assembly learning tools with 60 designed part models, three robot assembly environment families and seven controllers.","institution":"NVIDIA","venue":"RSS 2022","modalities":["Proprioception","Force/Torque"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2205.03532","project":"https://sites.google.com/nvidia.com/factory","code":"https://github.com/isaac-sim/IsaacGymEnvs"},"benchmark":{"simulator":"Isaac Gym / PhysX with signed-distance-field contacts","simulatorVersion":"Original paper uses Isaac Gym; exact preview build Not reported. Current Isaac Lab Factory ports are distinct implementations.","taskCount":"3 assembly environment families; 60 part models; 7 robot controllers","tasks":["FrankaNutBoltEnv","FrankaInsertionEnv","FrankaGearsEnv"],"taskListScope":"Complete three original environment classes; demonstrated nut-and-bolt RL subtasks include Pick, Place and Screw.","capabilities":["Dense contact simulation","Threaded fasteners","Peg insertion","Gear meshing","GPU-parallel RL"],"metrics":"Simulation speed/contact stability; RL success, policy training time and control comparisons.","robots":"Franka Panda","sensors":"Robot state and simulated contact information; original policies are not optical-tactile RGBDT policies.","dataCollection":"PPO interaction in parallel contact simulation; the paper trains assembly policies without human demonstration collection or corrections.","data":"60 CAD/physical part models derived from industrial assembly test components; environment/controller assets rather than a common demonstration corpus.","protocol":"GPU-parallel contact-rich scenes; proof-of-concept PPO policies for nut-and-bolt assembly with alternative controllers.","baselines":"PPO with operational-space, joint-space, force and hybrid controllers."},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"BSD-3-Clause IsaacGymEnvs code; NVIDIA simulator and third-party asset terms separate","notes":"Legacy IsaacGymEnvs toolkit; current Isaac Lab implementations should be versioned separately."},"evidence":[{"url":"https://arxiv.org/abs/2205.03532","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://sites.google.com/nvidia.com/factory","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/isaac-sim/IsaacGymEnvs","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://arxiv.org/abs/2205.03532","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["A simulation/learning toolkit rather than a standardized tactile benchmark.","Task ports across Isaac Gym and Isaac Lab do not imply identical physics or policy performance."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not verified","data":"Available","license":"BSD-3-Clause IsaacGymEnvs code; NVIDIA simulator and third-party asset terms separate","notes":"Legacy IsaacGymEnvs toolkit; current Isaac Lab implementations should be versioned separately."}},"relatedIds":[]},{"id":"dlr-pure-torque-rotation","name":"DLR Torque-Only Rotation","title":"Learning Purely Tactile In-Hand Manipulation with a Torque-Controlled Hand","date":"2022-04-07","year":2022,"collections":["policy"],"categories":["tactile-foundation","visual-precision-rl"],"resourceType":"policy","summary":"Torque/proprioceptive feedback supports palm-down continuous in-hand rotation without cameras.","institution":"German Aerospace Center (DLR) / Deggendorf Institute of Technology","venue":"Preprint","modalities":["Force/Torque","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Dexterous hand"],"links":{"paper":"https://arxiv.org/abs/2204.03698","project":"https://dlr-alr.github.io/dlr-tactile-manipulation"},"policy":{"family":"RL","baseModel":"SAC policy trained in identified rigid-body simulation with gravity/filter curriculum.","modelSize":"Not reported","inputs":"Hand joint position/torque measurements; no vision or skin array.","outputs":"Target hand joint angles through impedance control.","trainingData":"Simulated RL interactions; not reported as a demonstration dataset.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"10 Hz policy,1000 Hz real impedance/filtered control;500 Hz simulator loop.","trainingMethod":"Off-policy SAC with domain adapted curriculum andsim-to-real transfer.","evaluation":"DLR Hand II palm-down cube rotation with permanent force closure."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; claimed future releases are not treated as currently available."},"evidence":[{"url":"https://arxiv.org/abs/2204.03698","label":"Original paper and supplementary implementation details","fields":["title","date","summary","institution","policy","caveats"],"note":"Primary full text reviewed; dataset, deployment modalities and training-only privileges are scoped separately."},{"url":"https://dlr-alr.github.io/dlr-tactile-manipulation","label":"Official author project","fields":["links","release"],"note":"Live project artifact links inspected on2026-10-17."}],"caveats":["Paper calls this purely tactile, but observations are joint torque/position, not cutaneous skin measurements."],"verified":"2026-10-17"},{"id":"dome","name":"DOME","title":"Demonstrate Once, Imitate Immediately (DOME): Learning Visual Servoing for One-Shot Imitation Learning","date":"2022-04-06","year":2022,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A learned segmentation and image-servoing system reaches the demonstrated bottleneck pose, then replays end-effector velocities.","institution":"Imperial College London","venue":"IROS 2022","modalities":["RGB","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2204.02863","project":"https://www.robot-learning.uk/dome"},"policy":{"family":"Geometric transfer","baseModel":"Learned visual alignment and trajectory replay. Image-conditioned segmentation network and learned relative-pose visual-servo network.","modelSize":"Not reported","inputs":"Wrist RGB image and bottleneck reference from one demonstration; recorded end-effector velocities.","outputs":"Servo corrections to the bottleneck, then open-loop velocity replay.","trainingData":"Task-independent synthetic Blender/BlenderProc scenes with ShapeNet/ModelNet40 objects and randomized lighting, distractors and appearance.","trainingTime":"No additional training after the target demonstration; offline pretraining duration not reported.","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Supervised synthetic segmentation and pose-regression pretraining; frozen deployment on unseen objects.","evaluation":"Seven everyday physical tasks, 30 predetermined object poses per task, half with distractors."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No author-linked implementation was verified in the inspected primary manuscript/project. This is an evidence gap, not proof that no implementation exists."},"evidence":[{"url":"https://arxiv.org/abs/2204.02863","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://www.robot-learning.uk/dome","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."}],"caveats":["No post-demonstration training does not mean the perception/servoing modules were never pretrained.","Interaction after alignment is open-loop replay and does not provide contact-feedback correction."],"verified":"2026-10-17"},{"id":"saycan","name":"SayCan","title":"Do As I Can, Not As I Say: Grounding Language in Robotic Affordances","date":"2022-04-04","year":2022,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Combines language-model task relevance with learned skill-affordance values to select executable robot behaviors.","institution":"Google Research","venue":"Preprint","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Mobile manipulator","Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2204.01691","project":"https://say-can.github.io","code":"https://github.com/google-research/google-research/tree/master/saycan"},"policy":{"family":"Hybrid","baseModel":"PaLM language model with learned skill policies and associated value functions; model-size variants are compared.","modelSize":"PaLM-540B is the largest reported language-planner variant; skill networks are separate components.","inputs":"Natural-language task instruction and available skill descriptions; learned value functions evaluate current robot observations / state.","outputs":"A sequence of selected skill labels; pretrained visuomotor policies execute each selected behavior.","trainingData":"Pretrained robot skills learned from robotic data and reinforcement learning; no task-specific PaLM fine-tuning for instruction planning.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen language-model skill likelihood is multiplied by learned affordance / success estimates; execution closes the loop between decisions.","evaluation":"101 real-world kitchen robot instructions, including long-horizon and ambiguous commands."},"release":{"code":"Partial — demonstration material","weights":"Not applicable","data":"Not verified","license":"Apache-2.0 for repository code; hosted models and physical skill policies separate","notes":"Project-linked saycan subdirectory is a scoped public example; it is not evidence that the complete physical skill policy stack is released."},"evidence":[{"url":"https://arxiv.org/abs/2204.01691","label":"Primary paper, full text","fields":["title","date","summary","modalities","policy","caveats","imageCandidates"],"note":"Publication date is the first arXiv submission. Policy fields distinguish model calls, controller execution and learning."},{"url":"https://github.com/google-research/google-research/tree/master/saycan","label":"Official implementation, README and file/license inspection on 19 October 2026","fields":["links","release"],"note":"Project-linked saycan subdirectory is a scoped public example; it is not evidence that the complete physical skill policy stack is released. Apache-2.0 for repository code; hosted models and physical skill policies separate."}],"caveats":["Language-model planning performance and end-to-end physical execution success are reported separately.","The language model selects skills rather than emitting every motor action."],"verified":"2026-10-19"},{"id":"bc-z","name":"BC-Z","title":"BC-Z: Zero-Shot Task Generalization with Robotic Imitation Learning","date":"2022-02-04","year":2022,"collections":["policy"],"categories":["generalist-foundation","agent-rsi-icl"],"resourceType":"policy","summary":"A large visual imitation system uses language or human-video task embeddings to generalize to unseen real manipulation tasks.","institution":"Robotics at Google / Everyday Robots","venue":"CoRL 2021","modalities":["RGB","Language"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2202.02005","project":"https://sites.google.com/view/bc-z/home","data":"https://www.kaggle.com/google/bc-z-robot"},"policy":{"family":"Behavior cloning","baseModel":"FiLM-conditioned ResNet visuomotor policy with language/video task embeddings.","modelSize":"Not reported","inputs":"RGB camera observations plus a language or human-demonstration-video task embedding.","outputs":"Seven-degree-of-freedom arm/gripper control commands.","trainingData":"25,877 robot demonstrations totaling 125 robot-hours, plus 18,726 human videos across over 100 tasks.","trainingTime":"Not reported","trainingHardware":"Multi-task policy: TPUv3 pod, batch size 4,096. Video encoder: 18 V100 GPUs, batch size 28 per GPU. These are separate training stages.","controlRate":"Asynchronous closed-loop inference/control at 10 Hz.","trainingMethod":"Behavior cloning from demonstrations and human interventions; contrastive/aligned video task embeddings.","evaluation":"24 unseen real manipulation tasks; 44% average success reported under the paper’s novel-task protocol."},"release":{"code":"Not released","weights":"Not released","data":"Available","license":"Not verified","notes":"Robot demonstrations are publicly linked; video-encoder and policy weights are not established as public releases."},"evidence":[{"url":"https://arxiv.org/abs/2202.02005","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://sites.google.com/view/bc-z/home","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."}],"caveats":["Novel video-task evaluation averages embeddings from ten new human videos, rather than always using a single example."],"verified":"2026-10-17"},{"id":"ndf","name":"Neural Descriptor Fields (NDF)","title":"Neural Descriptor Fields: SE(3)-Equivariant Object Representations for Manipulation","date":"2021-12-09","year":2021,"collections":["policy"],"categories":["rgbd-3d"],"resourceType":"policy","summary":"Equivariant 3D descriptors transfer demonstrated grasp/place poses to unseen object instances.","institution":"MIT; Google Research; University of Toronto","venue":"ICRA 2022","modalities":["Depth","Point cloud","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2112.05124","project":"https://yilundu.github.io/ndf/","code":"https://github.com/anthonysimeonov/ndf_robot"},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT for repository code; pretrained models, data and figure rights remain separate","notes":"Code, data and weights are checked independently; an upstream pretrained model is not a released task policy."},"evidence":[{"url":"https://arxiv.org/abs/2112.05124","label":"Original manuscript and first submission history","fields":["title","date","summary","policy","modalities","environments","embodiments"],"note":"Full author HTML method/evaluation/appendix inspected; depth observations, pseudo-labels and generated outputs retain distinct roles."},{"url":"https://yilundu.github.io/ndf/","label":"Official author project","fields":["institution","release","imageCandidates"],"note":"Public author-linked project; figure and downstream scope verified separately."},{"url":"https://github.com/anthonysimeonov/ndf_robot","label":"Official implementation and release scope","fields":["release"],"note":"Availability of research implementation does not establish public task-policy checkpoints."},{"url":"https://github.com/anthonysimeonov/ndf_robot/blob/master/LICENSE.md","label":"Actual license file in released source","fields":["release.license"],"note":"Only source code covered by this file; not a blanket license for paper figures or upstream weights."},{"url":"https://github.com/anthonysimeonov/ndf_robot/tree/master","label":"Actual repository content inventory","fields":["release.code"],"note":"159 tracked paths inspected; code release is distinguished from a README/project/figure-only repository."}],"caveats":["Category-level shape prior and object segmentation remain assumptions; few demonstration claims exclude prior model training."],"verified":"2026-10-17","policy":{"family":"Geometric transfer","baseModel":"Vector-neuron occupancy autoencoder; continuous point/pose descriptors and test-time pose optimization.","modelSize":"Not reported in the inspected primary-source sections","inputs":"Segmented depth-derived object point clouds and 5–10 demonstration poses.","outputs":"Task-relative 6-DoF grasp/place poses.","trainingMethod":"Self-supervised category-level 3D autoencoding, then demonstration descriptor matching.","trainingData":"Category-shape pretraining and 5–10 task demonstrations; pretraining is not eliminated by few-shot adaptation.","trainingTime":"Not reported in the inspected primary-source sections","trainingHardware":"Not reported in the inspected primary-source sections","evaluation":"Simulation and real robot manipulation across novel category instances and 6-DoF poses.","controlRate":"Not reported in the inspected primary-source sections"}},{"id":"vinn","name":"VINN","title":"The Surprising Effectiveness of Representation Learning for Visual Imitation","date":"2021-12-02","year":2021,"collections":["policy"],"categories":["agent-rsi-icl","visual-precision-rl"],"resourceType":"policy","summary":"A visual nearest-neighbor policy decouples representation learning from action learning and predicts controls by locally weighted retrieval.","institution":"New York University","venue":"RSS 2022","modalities":["RGB","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2112.01511","project":"https://jyopari.github.io/VINN/","code":"https://github.com/jyopari/VINN/tree/main","data":"https://drive.google.com/drive/folders/11-sAN2a-F7G-lvx6qRXnrWjlxNb0PH1m"},"policy":{"family":"Retrieval policy","baseModel":"Pretrained/self-supervised visual encoder with nonparametric locally weighted regression over demonstrated actions.","modelSize":"Not reported","inputs":"Current RGB image and a demonstration retrieval database.","outputs":"Action predicted from nearby demonstration embeddings; robot experiment performs door opening.","trainingData":"Offline visual imitation datasets and real robot door-opening demonstrations; the paper’s human kitchen subset contains 71 train / 21 test demonstrations.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Learn visual representations separately, then retrieve / locally average demonstration actions without fitting a parametric action head.","evaluation":"Offline imitation prediction and real-robot door opening."},"release":{"code":"Available","weights":"Not verified","data":"Available","license":"Not verified","notes":"Executable method code is on the main branch, while the default gh-pages branch hosts the website; official demonstration dataset is linked separately."},"evidence":[{"url":"https://www.roboticsproceedings.org/rss18/p010.pdf","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://jyopari.github.io/VINN/","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/jyopari/VINN","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."},{"url":"https://github.com/jyopari/VINN/tree/main","label":"Verified official code artifact","fields":["links.code","release.code"],"note":"Author project / repository cross-link verified; current release distinguished from original paper experiments."},{"url":"https://drive.google.com/drive/folders/11-sAN2a-F7G-lvx6qRXnrWjlxNb0PH1m","label":"Verified official data artifact","fields":["links.data","release.data"],"note":"Author project / repository cross-link verified; current release distinguished from original paper experiments."}],"caveats":["Nearest-neighbor memory is demonstration retrieval; the IBC baseline compute is not a VINN training-cost claim."],"verified":"2026-10-17"},{"id":"mosaic","name":"MOSAIC","title":"Towards More Generalizable One-shot Visual Imitation Learning","date":"2021-10-26","year":2021,"collections":["policy","benchmark"],"categories":["agent-rsi-icl","general-manipulation"],"resourceType":"policy","summary":"Self-attention and temporal contrastive learning enable one-shot visual imitation across a seven-task manipulation suite.","institution":"UC Berkeley","venue":"ICRA 2022","modalities":["RGB"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2110.13423","project":"https://sites.google.com/berkeley.edu/mosaic","code":"https://github.com/rll-research/mosaic"},"policy":{"family":"Meta-imitation","baseModel":"First three ResNet-18 residual blocks, self-attention demonstration/observation fusion and a discretized-logistic action head.","modelSize":"Not reported","inputs":"Sawyer RGB demonstration video and current Panda RGB observation; four demonstration frames and one evaluation observation frame.","outputs":"Robot action distribution using independently discretized action dimensions; the reported 256 is the number of bins, not action dimensionality.","trainingData":"7 tasks / 61 variations; 100 Sawyer and 100 Panda scripted demonstrations for each variation.","trainingTime":"Single-task model within one GPU-day; multi-task model about four GPU-days.","trainingHardware":"NVIDIA Titan Xp for the reported compute budget.","controlRate":"Not reported","trainingMethod":"Behavior cloning plus temporal contrastive representation learning across paired demonstrations.","evaluation":"Single-task / multi-task one-shot execution, held-out tasks and fine-tuning in the seven-task simulated suite."},"release":{"code":"Available","weights":"Not released","data":"Partial","license":"MIT for repository code; external model, dataset and dependency terms remain separate.","notes":"Official code includes expert-data generation, model training and evaluation; checkpoint-input commands do not establish pretrained weights."},"evidence":[{"url":"https://arxiv.org/abs/2110.13423","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats","benchmark"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://sites.google.com/berkeley.edu/mosaic","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/rll-research/mosaic/blob/HEAD/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Actual license file inspected; code license does not automatically license data or weights."},{"url":"https://github.com/rll-research/mosaic","label":"Verified official code artifact","fields":["links.code","release.code"],"note":"Author project / repository cross-link verified; current release distinguished from original paper experiments."},{"url":"https://arxiv.org/abs/2110.13423","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Cross-morphology protocol uses Sawyer videos to condition a Panda policy; images are RGB."],"verified":"2026-10-17","benchmark":{"simulator":"robosuite / MuJoCo, with additional Meta-World assets","simulatorVersion":"Paper: robosuite v1.1, MuJoCo version not reported. Official reproduction README pins mujoco210 / MuJoCo 2.1.0.","taskCount":"7 task families, 61 variations in the published suite","tasks":["Door","Drawer","Press Button","Pick & Place","Stack Block","Basketball","Nut Assembly"],"taskListScope":"Complete seven-family published suite; individual variation names are not expanded here.","capabilities":["One-shot visual imitation","Cross-morphology transfer","Multi-task generalization"],"metrics":"Mean and standard deviation of success rate per task.","robots":"Sawyer demonstrator; Panda policy robot.","sensors":"RGB rendered images.","dataCollection":"Scripted expert policies generate Sawyer and Panda demonstrations for randomized instances of each task variation.","data":"100 Sawyer and 100 Panda scripted demonstrations per variation.","protocol":"Single / multi-task training; three converged checkpoints evaluated on 10 random-seed episodes per variation; separate held-out-task and fine-tuning settings.","baselines":"DAML, T-OSIL, LSTM, MLP and MOSAIC."},"benchmarkName":"MOSAIC Task Suite","policyName":"MOSAIC"},{"id":"taxim","name":"Taxim","title":"Taxim: An Example-based Simulation Model for GelSight Tactile Sensors","date":"2021-09-09","year":2021,"collections":["benchmark"],"categories":["supporting-resources"],"resourceType":"simulator","summary":"Calibration-driven optical and marker-motion simulation for GelSight-style tactile sensors, used as a component in later visuotactile simulators.","institution":"Carnegie Mellon University","venue":"IEEE Robotics and Automation Letters 2022","modalities":["Tactile"],"environments":["Simulation"],"embodiments":["Sensor-level resource"],"links":{"paper":"https://arxiv.org/abs/2109.04027","code":"https://github.com/Robo-Touch/Taxim"},"benchmark":{"simulator":"Standalone example-based tactile simulator","simulatorVersion":"No mandatory robot physics engine; repository tested configurations include macOS 10.15.7 / Python 3.8.5 and Ubuntu 18.04.1 / Python 3.6.13.","taskCount":"Not applicable — sensor-image / marker-motion simulator","tasks":[],"taskListScope":"No closed-loop manipulation task suite.","capabilities":["GelSight optical simulation","Marker displacement","Sensor calibration"],"metrics":"Image/marker fidelity and computational performance.","robots":"Not prescribed","sensors":"GelSight and sensor-specific variations calibrated with example contacts.","dataCollection":"Instrumented real contact calibration fits optical/deformation models; simulated tactile samples are then rendered. No uniform action-policy demonstration corpus.","data":"Calibration files, object meshes and optical/marker-motion examples.","protocol":"Collect calibration contacts, fit optical table and simulate deformation / marker fields under configured contact.","baselines":"Sensor-model comparisons; no manipulation-policy baseline."},"release":{"code":"Available","weights":"Not applicable","data":"Available","license":"MIT code","notes":"Calibration and example sensor simulation code available; upstream URL may redirect from CMURoboTouch to Robo-Touch."},"evidence":[{"url":"https://arxiv.org/abs/2109.04027","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://github.com/Robo-Touch/Taxim","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://arxiv.org/abs/2109.04027","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Optical/marker simulation is a component rather than a complete contact physics or manipulation benchmark."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not applicable","data":"Available","license":"MIT code","notes":"Calibration and example sensor simulation code available; upstream URL may redirect from CMURoboTouch to Robo-Touch."}},"relatedIds":[]},{"id":"tacto","name":"TACTO","title":"TACTO: A Fast, Flexible, and Open-source Simulator for High-Resolution Vision-based Tactile Sensors","date":"2020-12-15","year":2020,"collections":["benchmark"],"categories":["supporting-resources"],"resourceType":"simulator","summary":"Configurable optical tactile rendering for sensors such as DIGIT/OmniTact, with grasp-stability and marble-control demonstrations.","institution":"Facebook AI Research","venue":"IEEE Robotics and Automation Letters 2022","modalities":["Tactile"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper","Robot arm tactile fingertip"],"links":{"paper":"https://arxiv.org/abs/2012.08456","code":"https://github.com/facebookresearch/tacto"},"benchmark":{"simulator":"TACTO renderer; PyBullet example integration","simulatorVersion":"Exact package / PyBullet version Not reported here; archived repository dependencies define compatibility.","taskCount":"2 application demonstrations, not a common multi-task manipulation benchmark","tasks":["Grasp stability prediction","Marble manipulation"],"taskListScope":"Complete two application types in abstract; sensor-rendering examples additional.","capabilities":["Optical tactile rendering","Sensor configuration","Grasp-stability learning","Local tactile control"],"metrics":"Rendering throughput; grasp prediction and control outcomes; proof-of-concept sim-to-real.","robots":"Task-specific gripper / tactile configurations.","sensors":"DIGIT and OmniTact optical tactile designs.","dataCollection":"Procedural physics/grasps supply tactile-rendering samples for application-specific datasets; not a common closed-loop demonstration collector.","data":"One million simulated grasps used in paper grasp-stability application; not one million closed-loop task demonstrations.","protocol":"Separate tactile rendering from external rigid-body dynamics; application-specific perception and control evaluation.","baselines":"Application-specific neural predictors/controllers; not a universal policy benchmark."},"release":{"code":"Available","weights":"Not applicable","data":"Partial","license":"MIT code","notes":"Open-source simulator with examples; repository maintenance/dependency age should be considered."},"evidence":[{"url":"https://arxiv.org/abs/2012.08456","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://github.com/facebookresearch/tacto","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://arxiv.org/abs/2012.08456","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["A realistic-looking tactile render does not establish accurate soft-body mechanics or transfer for every sensor."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not applicable","data":"Partial","license":"MIT code","notes":"Open-source simulator with examples; repository maintenance/dependency age should be considered."}},"relatedIds":[]},{"id":"petnet","name":"PETNet","title":"Modeling Task Uncertainty for Safe Meta-Imitation Learning","date":"2020-11-27","year":2020,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A probabilistic task embedding estimates demonstration uncertainty for safer visual meta-imitation.","institution":"Not reported","venue":"Frontiers in Robotics and AI 2020","modalities":["RGB","Proprioception"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://doi.org/10.3389/frobt.2020.606361"},"policy":{"family":"Meta-imitation","baseModel":"Probabilistic task-embedding network and demonstration-conditioned control policy.","modelSize":"Not reported","inputs":"RGB demonstration cue, current visual/robot observations and inferred task-embedding distribution.","outputs":"Manipulation controls conditioned on uncertain task embeddings; uncertainty supports decisions about ambiguous instructions.","trainingData":"MIL simulated-pushing dataset; 12 demonstrations per task used under the paper’s protocol.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Learn a probability distribution in task-embedding space and a visual imitation policy with task uncertainty.","evaluation":"Simulated arm pushing with held-out objects/tasks and uncertainty calibration / ambiguity tests."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://doi.org/10.3389/frobt.2020.606361","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."}],"caveats":["A manipulation policy and uncertainty method, distinct from generic meta-learning classification.","The linked MIL implementation is a baseline dependency, not automatically a released PETNet implementation."],"verified":"2026-10-17"},{"id":"t-osil","name":"Transformers for One-Shot Visual Imitation (T-OSIL)","title":"Transformers for One-Shot Visual Imitation","date":"2020-11-11","year":2020,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A video-conditioned attention policy learns context-driven and dynamics-aware features for cross-embodiment visual imitation.","institution":"Carnegie Mellon University","venue":"CoRL 2020","modalities":["RGB","Proprioception"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2011.05970","project":"https://oneshotfeatures.github.io/","code":"https://github.com/SudeepDasari/one_shot_transformers"},"policy":{"family":"Transformer policy","baseModel":"Convolutional image encoder, spatiotemporal non-local attention and discretized action head; inverse-dynamics auxiliary objective.","modelSize":"Not reported","inputs":"Demonstration video and current robot visual/state observations.","outputs":"End-effector target position, axis-angle rotation and gripper command, discretized into 256 bins per dimension and executed through IK/PD control.","trainingData":"Paired demonstrations in simulated cross-embodiment manipulation tasks.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Behavior cloning with attention-based demonstration conditioning and dynamics-aware auxiliary learning.","evaluation":"Novel visual task instances and cross-morphology simulated manipulation."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Author implementation is public; do not infer action dimension from its 256 discretization bins."},"evidence":[{"url":"https://arxiv.org/abs/2011.05970","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://oneshotfeatures.github.io/","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/SudeepDasari/one_shot_transformers","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."}],"caveats":["IK and low-level control execute predicted poses; the network does not directly predict joint torques."],"verified":"2026-10-17"},{"id":"flowcontrol","name":"FlowControl","title":"FlowControl: Optical Flow Based Visual Servoing","date":"2020-07-01","year":2020,"collections":["policy"],"categories":["agent-rsi-icl","rgbd-3d"],"resourceType":"policy","summary":"Optical flow establishes RGB-D correspondences for continuously servoing a robot along one demonstrated interaction.","institution":"University of Freiburg / Symbio Robotics","venue":"IROS 2020","modalities":["RGB","Depth","Proprioception"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2007.00291"},"policy":{"family":"Geometric transfer","baseModel":"One-shot optical-flow visual servoing. Pretrained FlowNet2, depth back-projection, SVD rigid alignment and position controller.","modelSize":"Not reported","inputs":"Live and demonstrated 640×480 RGB-D images, demonstration foreground mask and gripper height/angle/aperture state.","outputs":"Relative rigid alignment corrections and sequential progression through a demonstrated interaction.","trainingData":"One demonstration per new task; FlowNet2 is pretrained on FlyingThings3D.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Frozen visual correspondence model plus repeated geometric alignment; no target-task policy training.","evaluation":"Real grasping, pick-and-stow, shape-sorter insertion and wheel interaction, including appearance/geometry changes."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No author-linked implementation was verified in the inspected primary manuscript/project. This is an evidence gap, not proof that no implementation exists."},"evidence":[{"url":"https://arxiv.org/abs/2007.00291","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://ieeexplore.ieee.org/document/9340942/","label":"Publisher conference record","fields":["venue"],"note":"Published in 2020 IEEE/RSJ IROS, not ICRA 2021."}],"caveats":["A foreground mask is supplied for demonstration images.","Reported approximately 1 mm alignment is a five-repeat robot-state measurement, not a universal manipulation accuracy guarantee."],"verified":"2026-10-17"},{"id":"metacure","name":"MetaCURE","title":"MetaCURE: Meta Reinforcement Learning with Empowerment-Driven Exploration","date":"2020-06-15","year":2020,"collections":["policy"],"categories":["agent-rsi-icl","visual-precision-rl"],"resourceType":"policy","summary":"Separate exploration and exploitation policies infer new tasks efficiently from interaction context.","institution":"Tsinghua University","venue":"ICML 2021","modalities":["Proprioception"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/2006.08170","code":"https://github.com/NagisaZj/MetaCURE-Public"},"policy":{"family":"RL","baseModel":"Off-policy context encoder with temporal convolution/attention and separate exploration/exploitation actor-critics.","modelSize":"Not reported","inputs":"State/action/reward context and current state; sparse task success provides feedback.","outputs":"Continuous arm actions in the evaluated Meta-World Reach and Reach-Wall settings.","trainingData":"Agent-generated replay experience in meta-training task distributions.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Empowerment / task-information-gain intrinsic reward for exploration and shared task inference for exploitation.","evaluation":"Sparse-reward Meta-World Reach / Reach-Wall, alongside MuJoCo locomotion tasks."},"release":{"code":"Available","weights":"Not verified","data":"Not applicable","license":"Not verified","notes":"Official implementation is public; online training generates its own experience."},"evidence":[{"url":"https://arxiv.org/abs/2006.08170","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://github.com/NagisaZj/MetaCURE-Public","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."}],"caveats":["Included for evaluated robotic arm tasks; locomotion sample-efficiency plots are not manipulation training budgets."],"verified":"2026-10-17"},{"id":"tecnet-humans","name":"TecNets: Humans without Humans","title":"Learning One-Shot Imitation from Humans without Humans","date":"2019-11-04","year":2019,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Simulation-trained task embeddings let a real robot imitate one human video without real human demonstrations during training.","institution":"Imperial College London","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/1911.01103","project":"https://sites.google.com/view/tecnets-humans"},"policy":{"family":"Meta-imitation","baseModel":"Task-Embedded Control Networks with randomized simulated human-like demonstrators.","modelSize":"Not reported","inputs":"One human demonstration video and the robot’s current visual/state observations.","outputs":"Robot manipulation controls conditioned on the demonstration task embedding.","trainingData":"Domain-randomized simulated demonstrations; no real human arm demonstrations in training.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Simulation-only task-embedding/control learning with human appearance and domain randomization.","evaluation":"Pushing and placing in simulation and real robots."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/1911.01103","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://sites.google.com/view/tecnets-humans","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."}],"caveats":["A distinct follow-up to the original TecNet paper; simulator-only training is the key experimental setting."],"verified":"2026-10-17"},{"id":"rlbench","name":"RLBench","title":"RLBench: The Robot Learning Benchmark & Learning Environment","date":"2019-09-26","year":2019,"collections":["benchmark"],"categories":["general-manipulation","precision-assembly"],"resourceType":"benchmark","summary":"Programmatic manipulation environment and demonstration generation benchmark with broad everyday tasks and common observation/action interfaces.","institution":"Imperial College London","venue":"IEEE Robotics and Automation Letters 2020","modalities":["RGB","Depth","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/1909.12271","project":"https://sites.google.com/view/rlbench","code":"https://github.com/stepjam/RLBench"},"benchmark":{"simulator":"CoppeliaSim via PyRep","simulatorVersion":"Official installation specifies CoppeliaSim 4.1.0; use PyRep-compatible simulator build.","taskCount":"Original paper: 100 tasks; current master source contains 106 task modules.","tasks":["basketball_in_hoop","beat_the_buzz","block_pyramid","change_channel","change_clock","close_box","close_door","close_drawer","close_fridge","close_grill","close_jar","close_laptop_lid","close_microwave","empty_container","empty_dishwasher","get_ice_from_fridge","hang_frame_on_hanger","hit_ball_with_queue","hockey","insert_onto_square_peg","insert_usb_in_computer","lamp_off","lamp_on","lift_numbered_block","light_bulb_in","light_bulb_out","meat_off_grill","meat_on_grill","move_hanger","open_box","open_door","open_drawer","open_fridge","open_grill","open_jar","open_microwave","open_oven","open_washing_machine","open_window","open_wine_bottle","phone_on_base","pick_and_lift","pick_and_lift_small","pick_up_cup","place_cups","place_hanger_on_rack","place_shape_in_shape_sorter","play_jenga","plug_charger_in_power_supply","pour_from_cup_to_cup","press_switch","push_button","push_buttons","put_all_groceries_in_cupboard","put_books_on_bookshelf","put_bottle_in_fridge","put_groceries_in_cupboard","put_item_in_drawer","put_knife_in_knife_block","put_knife_on_chopping_board","put_money_in_safe","put_plate_in_colored_dish_rack","put_rubbish_in_bin","put_shoes_in_box","put_toilet_roll_on_stand","put_tray_in_oven","put_umbrella_in_umbrella_stand","reach_and_drag","reach_target","remove_cups","scoop_with_spatula","screw_nail","set_the_table","setup_checkers","setup_chess","slide_block_to_target","slide_cabinet_open","slide_cabinet_open_and_place_cups","solve_puzzle","stack_blocks","stack_chairs","stack_cups","stack_wine","straighten_rope","sweep_to_dustpan","take_cup_out_from_cabinet","take_frame_off_hanger","take_item_out_of_drawer","take_lid_off_saucepan","take_money_out_safe","take_off_weighing_scales","take_plate_off_colored_dish_rack","take_shoes_out_of_box","take_toilet_roll_off_stand","take_tray_out_of_oven","take_umbrella_out_of_umbrella_stand","take_usb_out_of_computer","toilet_seat_down","toilet_seat_up","turn_oven_on","turn_tap","tv_on","unplug_charger","water_plants","weighing_scales","wipe_desk"],"taskListScope":"Complete 106 current-master task-module identifiers; not asserted to match exactly the original 100-task paper snapshot.","capabilities":["Multi-task learning","Few-shot learning","Imitation learning","Reinforcement learning","Insertion / articulated manipulation"],"metrics":"Task-specific binary success and episode return; few-shot/multi-task splits.","robots":"Default Franka Panda; configurable additional robot arms.","sensors":"Multiple RGB-D cameras, masks and proprioception; privileged object states optional; no optical tactile observations in original suite.","dataCollection":"Scripted task experts and motion planning through manually designed waypoints generate demonstrations on demand.","data":"On-demand waypoint/script-generated expert demonstrations; number is user-selected.","protocol":"Predefined task splits and variations; programmatic success/failure criteria; task-specific waypoints generate observations/actions.","baselines":"Original benchmark behavior-cloning / RL examples; later 3D and VLA policies evaluated through downstream recipes."},"release":{"code":"Available","weights":"Not applicable","data":"Available","license":"MIT code; CoppeliaSim and asset terms separate","notes":"Simulation tasks and demonstration-generation scripts public."},"evidence":[{"url":"https://arxiv.org/abs/1909.12271","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://sites.google.com/view/rlbench","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://github.com/stepjam/RLBench","label":"Official repository","fields":["release","benchmark.simulatorVersion"],"note":"Current public repository; implementation scope may differ from paper."},{"url":"https://arxiv.org/abs/1909.12271","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Task variants and registered task modules are different counting units.","Current task catalogue differs from the original publication snapshot."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Available","weights":"Not applicable","data":"Available","license":"MIT code; CoppeliaSim and asset terms separate","notes":"Simulation tasks and demonstration-generation scripts public."}},"relatedIds":[]},{"id":"watch-try-learn","name":"Watch, Try, Learn (WTL)","title":"Watch, Try, Learn: Meta-Learning from Demonstrations and Reward","date":"2019-06-07","year":2019,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A visual meta-policy combines a demonstration with trial outcomes and sparse reward feedback to resolve task ambiguity.","institution":"Google Brain / X","venue":"ICLR 2020","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/1906.03352","project":"https://sites.google.com/view/watch-try-learn-project"},"policy":{"family":"Meta-imitation","baseModel":"Convolutional video encoders with demonstration/trial context and a learned action policy.","modelSize":"Not reported","inputs":"Demonstration video, current RGB/robot state and previous task trial with its sparse success reward.","outputs":"Continuous manipulation actions from a demonstration-conditioned trial policy and an improved policy.","trainingData":"Kitchenware experiments collect 1,536 human-teleoperated demonstrations for 768 tasks over 96 object sets; toy reaching uses separate scripted expert data.","trainingTime":"Not reported","trainingHardware":"NVIDIA Tesla P100 GPUs; worker counts vary by experiment.","controlRate":"Not reported","trainingMethod":"Two-phase meta-learning from demonstrations and trial experience, using sparse reward feedback during adaptation.","evaluation":"Ambiguous reaching and visual kitchenware manipulation; held-out object/task settings."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/1906.03352","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://sites.google.com/view/watch-try-learn-project","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."}],"caveats":["Test-time adaptation uses demonstration and observed outcomes; its training procedure is distinct from training-free language-model reflection."],"verified":"2026-10-17"},{"id":"making-sense-vision-touch","name":"Making Sense of Vision and Touch","title":"Making Sense of Vision and Touch: Self-Supervised Learning of Multimodal Representations for Contact-Rich Tasks","date":"2018-10-24","year":2018,"collections":["policy"],"categories":["visual-precision-rl","tactile-foundation"],"resourceType":"policy","summary":"Self-supervised RGB/wrist-force fusion supports a complete learned peg-insertion controller, connecting low-rate Cartesian policy actions to compliant torque control.","institution":"Stanford University; NVIDIA","venue":"ICRA 2019","modalities":["RGB","Force/Torque","Proprioception"],"environments":["Simulation","Real robot"],"embodiments":["Arm-mounted peg"],"links":{"paper":"https://arxiv.org/abs/1810.10191","project":"https://sites.google.com/view/visionandtouch","code":"https://github.com/stanford-iprl-lab/multimodal_representation","data":"https://downloads.cs.stanford.edu/juno/triangle_real_data.zip","extended":"https://arxiv.org/abs/1907.13098"},"release":{"code":"Partial","weights":"Not verified","data":"Available","license":"MIT for repository code; data and figure rights remain separate","notes":"Actual source release covers representation training and provides a public dataset download script; no full TRPO/real-robot controller package or trained policy checkpoint was verified. Dataset archive endpoint responds successfully; archive was not downloaded."},"evidence":[{"url":"https://arxiv.org/abs/1810.10191","label":"Original manuscript and first public submission history","fields":["title","date","institution","model","policy","modalities","environments","caveats"],"note":"Full author method, evaluation and appendix reviewed; downstream controller probes are distinguished from the reusable model."},{"url":"https://sites.google.com/view/visionandtouch","label":"Official author project and current artifact destinations","fields":["release","venue","imageCandidates"],"note":"A project button or advertised artifact does not establish a downloadable release; paper-version differences are retained."},{"url":"https://github.com/stanford-iprl-lab/multimodal_representation/tree/master","label":"Actual public repository content inventory","fields":["release.code","release.weights","release.license"],"note":"36 tracked paths reviewed; representation code, action-policy code and README placeholders are distinguished."},{"url":"https://github.com/stanford-iprl-lab/multimodal_representation/blob/master/LICENSE","label":"Actual MIT license file","fields":["release.license"],"note":"Applies to released repository code, not a blanket license for paper figures or upstream assets."},{"url":"https://github.com/stanford-iprl-lab/multimodal_representation/blob/master/multimodal/dataset/download_data.sh","label":"Original data download script and checked public archive endpoint","fields":["release.data","links.data"],"note":"Script names the triangle_real_data.zip archive; its HTTPS endpoint returned 200 on review, without downloading the large archive."}],"caveats":["Wrist F/T feedback is distinct from fingertip/skin tactile sensing; no measured scene-depth input is used by this policy.","The 2019 extended manuscript is linked separately; quantitative fields here follow the original ICRA manuscript, whose first public submission was in 2018."],"verified":"2026-10-17","policy":{"family":"RL","baseModel":"Six-layer visual CNN, causal convolutional wrist-F/T encoder and proprioceptive MLP fused into a 128D representation; two-layer TRPO policy with an interpolating impedance/operational-space controller.","modelSize":"Modality encoders: approximately 0.5M learnable parameters; total controller count not reported.","inputs":"Fixed-camera RGB, 32-sample six-axis wrist F/T history, end-effector position/velocity. Kinect depth is not a deployed input in this manuscript.","outputs":"3D Cartesian end-effector displacement from policy; compliant hierarchy converts interpolated trajectories to joint torques.","trainingMethod":"Action-conditioned optical-flow/contact prediction and temporal-alignment pretraining; freeze representation, then learn task policy with TRPO.","trainingData":"Representation: 100K sampled states collected in 90–120 minutes. Simulation policies: 1,200 episodes × 500 steps. Real policies: 300 episodes × 1,000 steps.","trainingTime":"Representation: 20 epochs; elapsed time not reported. Real robot policy training: approximately five hours.","trainingHardware":"NVIDIA Titan V for representation training; real KUKA LBR iiwa for interaction. Other compute not reported.","controlRate":"20 Hz Cartesian policy; 200 Hz interpolation/compliant joint-torque controller.","evaluation":"Peg insertion in SAI 2.0/CHAI3D simulation and on KUKA iiwa; shape/clearance transfer and external perturbations. Real haptics use an OptoForce wrist sensor, not skin tactile."}},{"id":"tecnet","name":"Task-Embedded Control Networks (TecNets)","title":"Task-Embedded Control Networks for Few-Shot Imitation Learning","date":"2018-10-08","year":2018,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A metric-learned demonstration embedding conditions a visuomotor policy on new tasks from one or a few examples.","institution":"Imperial College London","venue":"CoRL 2018","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/1810.03237","project":"https://sites.google.com/view/task-embedded-control"},"policy":{"family":"Meta-imitation","baseModel":"Task-embedding video network and image-conditioned control network trained jointly.","modelSize":"Not reported","inputs":"One or more RGB demonstration videos and the robot’s current visual/state observation.","outputs":"Continuous robot control; task-specific settings include simulated 7-DoF arm control.","trainingData":"Simulation task demonstrations with domain randomization; real-world embedding analysis uses 164 tasks × five demonstrations, not necessarily policy training data.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Metric-learning task embeddings plus behavior cloning; simulation-to-real transfer without extra policy training.","evaluation":"Simulated visually guided manipulation and real robot few-shot task acquisition."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/1810.03237","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://sites.google.com/view/task-embedded-control","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."}],"caveats":["The 164-task real demonstration set is used for embedding visualization; it must not be relabeled as the entire policy-training corpus."],"verified":"2026-10-17"},{"id":"visual-mpc-retrying","name":"Visual MPC with Retrying","title":"Robustness via Retrying: Closed-Loop Robotic Manipulation with Self-Supervised Learning","date":"2018-10-06","year":2018,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Video prediction and self-supervised image registration let a robot repeatedly replan toward a visual manipulation goal.","institution":"University of California, Berkeley","venue":"CoRL 2018","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/1810.03043","project":"https://sites.google.com/view/robustness-via-retrying","code":"https://github.com/febert/robustness_via_retrying"},"policy":{"family":"Model-based planning","baseModel":"Self-supervised visual model predictive control. Action-conditioned video predictor, image-to-image registration model and CEM trajectory optimization.","modelSize":"Not reported","inputs":"Current RGB image and desired goal image with user-designated object pixels; robot control state used by the action-conditioned predictor.","outputs":"Cartesian x/y/z displacement and gripper azimuth, with a grasp primitive; replanning maintains visual goal correspondence.","trainingData":"20,000 autonomous pushing trajectories and 15,000 trajectories with gripper control; approximately 160 robot-hours, 150 training objects.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Self-supervised video and image registration learning on robot interaction data; goal-directed CEM planning with retries at execution.","evaluation":"Two Sawyer arms on unseen-object prehensile/nonprehensile relocation; additional MuJoCo L-block pushing experiments."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"No repository-wide license found in inspected tree","notes":"Author-linked repository and actual README/tree inspected; weights/data are tracked separately. No installation or reproduction attempted."},"evidence":[{"url":"https://arxiv.org/abs/1810.03043","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."},{"url":"https://sites.google.com/view/robustness-via-retrying","label":"Official project","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."},{"url":"https://github.com/febert/robustness_via_retrying","label":"Official code","fields":["release","imageCandidates"],"note":"Author-linked source. Component and artifact release terms may differ."}],"caveats":["This is the 2018 closed-loop retrying paper, rather than the earlier visual-foresight paper.","A user supplies goal/object pixel annotations; the objective is not inferred from arbitrary language."],"verified":"2026-10-17"},{"id":"more-than-a-feeling","name":"More Than a Feeling","title":"More Than a Feeling: Learning to Grasp and Regrasp using Vision and Touch","date":"2018-05-28","year":2018,"collections":["policy"],"categories":["tactile-il"],"resourceType":"policy","summary":"Predicts the success of candidate grasp adjustments from raw vision/touch and repeatedly chooses promising regrasp actions.","institution":"UC Berkeley / MIT","venue":"IEEE RA-L 2018","modalities":["RGB","Tactile"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/1805.11085"},"policy":{"family":"Other","baseModel":"Action-conditional multimodal CNN","modelSize":"Not reported","inputs":"RGB, paired GelSight tactile images and candidate adjustment action","outputs":"Predicted grasp-success probability used to choose regrasp actions","trainingData":"≈6,450 real grasping trials","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Self-supervised action-conditional outcome prediction and iterative action search","evaluation":"Sawyer/Weiss gripper grasp adjustment, grasp success and low-force grasping"},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."},"evidence":[{"url":"https://arxiv.org/abs/1805.11085","label":"Author manuscript and submission history","fields":["title","date","policy","modalities","environments","embodiments"],"note":"Policy facts refer to the full manuscript available on the verification date; date is first arXiv submission."},{"url":"https://arxiv.org/html/1805.11085","label":"Full paper: method and implementation details","fields":["policy.baseModel","policy.modelSize","policy.inputs","policy.outputs","policy.trainingData","policy.trainingTime","policy.trainingHardware","policy.controlRate","policy.trainingMethod","policy.evaluation","imageCandidates"],"note":"Unreported specifications remain explicitly unknown; hardware/rates are scoped to training, inference or low-level control."}],"caveats":["A learned outcome model plus action search, rather than a modern direct chunked generative policy."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No verified public download found in the inspected primary sources."}},"relatedIds":[]},{"id":"zero-shot-visual-imitation","name":"Zero-Shot Visual Imitation / GSP","title":"Zero-Shot Visual Imitation","date":"2018-04-23","year":2018,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A goal-conditioned skill policy learns from self-supervised exploration, then imitates a sequence of visual goals without expert action labels.","institution":"UC Berkeley","venue":"ICLR 2018","modalities":["RGB"],"environments":["Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/1804.08606","project":"https://pathak22.github.io/zeroshot-imitation/","code":"https://github.com/pathak22/zeroshot-imitation"},"policy":{"family":"Goal-conditioned policy","baseModel":"Rope manipulation uses pretrained AlexNet features and a goal-conditioned skill network with forward-consistency training.","modelSize":"Not reported","inputs":"Current RGB observation and demonstration goal-image sequence.","outputs":"Grasp location, displacement direction and displacement magnitude for rope manipulation.","trainingData":"Approximately 60k rope interaction pairs collected through self-supervised exploration; no expert actions for demonstration imitation.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Self-supervised exploration and forward-consistent goal-conditioned skill prediction.","evaluation":"Real Baxter rope manipulation; separate navigation / VizDoom experiments test the broader approach."},"release":{"code":"Available","weights":"Partial","data":"Not verified","license":"Not verified","notes":"Official project provides code/models; modalities and architectures differ between rope and navigation domains."},"evidence":[{"url":"https://arxiv.org/abs/1804.08606","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://pathak22.github.io/zeroshot-imitation/","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/pathak22/zeroshot-imitation","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."}],"caveats":["Not excluded as navigation-only: the original paper includes a real Baxter rope-manipulation experiment.","Navigation ResNet-50 architecture and frame counts are not substituted for the rope-policy setting."],"verified":"2026-10-17"},{"id":"universal-planning-networks","name":"Universal Planning Networks","title":"Universal Planning Networks","date":"2018-04-02","year":2018,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Differentiable latent-space planning is trained through an inner trajectory optimizer and transfers goal representations to new robot control problems.","institution":"University of California, Berkeley","venue":"ICML 2018","modalities":["RGB","Proprioception"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/1804.00645"},"policy":{"family":"Model-based planning","baseModel":"Gradient-based latent world-model planning. Convolutional image encoder, latent forward dynamics and unrolled gradient-descent planner.","modelSize":"Not reported","inputs":"Current and goal RGB images; embodiment sensorimotor state for dynamics/planning. Transfer policies may use the learned metric as a shaped reward.","outputs":"Planned continuous controls; transfer experiments additionally train RL controllers using the learned latent reward.","trainingData":"20,000 demonstrations each for three-/four-link reachers; manipulation experiment uses 20,000 pokes to learn representations for pushing.","trainingTime":"One million minibatch updates, approximately 1.5 days.","trainingHardware":"One NVIDIA Titan X.","controlRate":"Not reported","trainingMethod":"Imitation loss differentiates through trajectory optimization; representation transfers through goal-metric reward to RL policies.","evaluation":"MuJoCo reacher and morphology transfer, plus object poking-to-pushing skill transfer; navigation/locomotion experiments are separate."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"No author-linked implementation was verified in the inspected primary manuscript/project. This is an evidence gap, not proof that no implementation exists."},"evidence":[{"url":"https://arxiv.org/abs/1804.00645","label":"Original manuscript and first submission history","fields":["title","date","policy","modalities","environments"],"note":"Original method, evaluation and available implementation/appendix sections inspected. The survey is discovery evidence only."}],"caveats":["Included for its explicit object-manipulation transfer experiment. Several other experiments are navigation or reaching.","Transferred RL policies require training; gradient-based action planning alone is not deployment-time policy-weight adaptation."],"verified":"2026-10-17"},{"id":"daml","name":"DAML","title":"One-Shot Imitation from Observing Humans via Domain-Adaptive Meta-Learning","date":"2018-02-05","year":2018,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Domain-adaptive meta-learning lets a robot imitate one human demonstration despite viewpoint and embodiment differences.","institution":"UC Berkeley","venue":"ICRA 2018","modalities":["RGB","Depth","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/1802.01557","project":"https://sites.google.com/view/daml","code":"https://github.com/tianheyu927/mil"},"policy":{"family":"Meta-imitation","baseModel":"Convolutional visuomotor policy with a learned temporal adaptation objective and gradient-based meta-learning.","modelSize":"Not reported","inputs":"One human demonstration video, current robot image and robot state; measured RGB-D in pick-and-place, RGB only in placing / pushing.","outputs":"Continuous arm motion with a discrete gripper command; auxiliary target-contact gripper-pose prediction is training supervision.","trainingData":"1,293 placing, 640 pushing and 1,008 pick-and-place robot demonstrations, with an equal number of human demonstrations per setting.","trainingTime":"Appendix: 60k iterations for real placing / pushing / pick-and-place; separate simulated experiments use 30k iterations. Wall-clock duration not reported.","trainingHardware":"Not reported","controlRate":"Real robot policy and demonstration collection at 10 Hz.","trainingMethod":"Meta-learn a human-video adaptation loss using paired human / robot tasks, then adapt a robot policy from a new human video.","evaluation":"PR2 and Sawyer placing, pushing and pick-and-place; simulated pushing and viewpoint-shift experiments."},"release":{"code":"Available","weights":"Not verified","data":"Partial","license":"MIT for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"The shared original MIL repository explicitly implements both papers and documents the July 2018 learned-temporal-loss DAML experiment. No separate tianheyu927/daml repository is assumed."},"evidence":[{"url":"https://arxiv.org/abs/1802.01557","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://sites.google.com/view/daml","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/tianheyu927/mil","label":"Verified official code artifact","fields":["links.code","release.code"],"note":"Author project / repository cross-link verified; current release distinguished from original paper experiments."},{"url":"https://github.com/tianheyu927/mil/blob/master/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["Measured depth is specific to the pick-and-place setting. Other experiments use RGB.","One-shot adaptation updates model parameters."],"verified":"2026-10-19"},{"id":"nist-assembly-task-boards","name":"NIST Assembly Task Boards","title":"Assembly Performance Metrics and Test Methods: NIST Assembly Task Boards","date":"2018","year":2018,"collections":["benchmark"],"categories":["precision-assembly"],"resourceType":"benchmark","summary":"Standardized physical assembly/disassembly boards with low-cost components, reset protocols, time-based performance metrics and statistical comparison procedures.","institution":"National Institute of Standards and Technology","venue":"NIST benchmark and test protocols","modalities":[],"environments":["Real world"],"embodiments":["Embodiment-agnostic"],"links":{"paper":"https://www.nist.gov/system/files/documents/2018/01/11/test_method-metrics-analysis_v3.pdf","project":"https://www.nist.gov/el/intelligent-systems-division-73500/robotic-grasping-and-manipulation-assembly/assembly"},"benchmark":{"simulator":"Not applicable — original physical test boards","simulatorVersion":"Not applicable","taskCount":"Several board designs; board/component operations are the counting unit, not a fixed robot-learning task suite.","tasks":["Peg insertions","Gear meshing","Electrical connector insertions","Nut threading","Flexible parts handling and insertion","Actuating tensioners","Cable threading and routing","Wire bundling"],"taskListScope":"Published competencies across board designs; exact component repetitions vary by board and revision.","capabilities":["Small-parts assembly","Threading","Meshing","Routing","Flexible component handling"],"metrics":"Assembly/disassembly completion and elapsed time; benchmark templates and statistical tools; manual interference invalidates autonomous trials.","robots":"Robot-agnostic integrated-system evaluation.","sensors":"Not prescribed; perception, control and sensors are system design choices.","dataCollection":"Not applicable — physical task boards, test procedures and performance metrics; no prescribed training-demonstration collection method.","data":"Board designs, replication instructions, connector-housing STL files, benchmark protocol templates.","protocol":"Randomize board and bin pose in robot workspace; autonomous assembly or disassembly; repeated controlled tests and documented component configuration.","baselines":"Reference systems appear in companion studies such as Benchmarking Off-The-Shelf Solutions to Robotic Assembly Tasks (2021)."},"release":{"code":"Not applicable","weights":"Not applicable","data":"Available","license":"NIST public-resource terms; third-party part designs may have separate rights","notes":"Official board drawings, replication instructions and test templates publicly downloadable."},"evidence":[{"url":"https://www.nist.gov/system/files/documents/2018/01/11/test_method-metrics-analysis_v3.pdf","label":"Paper","fields":["title","date","benchmark"],"note":"Published paper / arXiv record and manuscript."},{"url":"https://www.nist.gov/el/intelligent-systems-division-73500/robotic-grasping-and-manipulation-assembly/assembly","label":"Official project","fields":["summary","benchmark","imageCandidates"],"note":"Public project page."},{"url":"https://www.nist.gov/system/files/documents/2018/01/11/test_method-metrics-analysis_v3.pdf","label":"Data collection method and scope","fields":["benchmark.dataCollection"],"note":"Original method/data protocol reviewed for the collection mechanism. Data volume, evaluation rollouts and downstream training remain separate; undisclosed or unresolved interfaces are labeled explicitly."}],"caveats":["Original test boards do not prescribe a learning algorithm, robot or sensor package.","Simulation adaptations such as Factory are separate resources and may cover only a subset of boards."],"verified":"2026-10-17","releaseByCollection":{"benchmark":{"code":"Not applicable","weights":"Not applicable","data":"Available","license":"NIST public-resource terms; third-party part designs may have separate rights","notes":"Official board drawings, replication instructions and test templates publicly downloadable."}},"relatedIds":[]},{"id":"neural-task-programming","name":"Neural Task Programming (NTP)","title":"Neural Task Programming: Learning to Generalize Across Hierarchical Tasks","date":"2017-10-04","year":2017,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"Recursively decomposes a demonstrated task into subtasks and callable neural programs, with information scoping to support compositional generalization.","institution":"Stanford University; California Institute of Technology","venue":"Preprint","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/1710.01813","project":"https://stanfordvl.github.io/ntp/","code":"https://github.com/StanfordVL/NTP-vat-release"},"policy":{"family":"Hierarchical imitation","baseModel":"Task-specification encoder, neural program interpreter and callable bottom-level action subroutines.","modelSize":"Not reported","inputs":"A demonstration represented by object-location trajectories or third-person video, current environment state and the active program.","outputs":"Next subprogram, scoped subtask specification and arguments; bottom-level programs invoke robot actions such as move_to and pick_and_drop.","trainingData":"Expert-generated program execution traces with hierarchical / scoping supervision. Object Sorting trains on 500 trajectories; Block Stacking uses up to 1,000 training task instances.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Supervised neural program induction from expert traces; a novel demonstration specifies the test task without new-task parameter updates.","evaluation":"Object Sorting, Block Stacking and other hierarchical manipulation in Bullet, with PR2 gripper / Sawyer simulation and selected real Sawyer experiments."},"release":{"code":"Partial — Bullet/PR2-gripper simulation framework","weights":"Not verified","data":"Not verified","license":"MIT for the linked VAT environment and original project material, verified from their actual LICENSE files; model/data and external dependency terms remain separate.","notes":"The original ntp repository contains paper/implementation documentation. Its author-linked NTP-vat-release contains actual vat environment code, expert demo.py and the PR2-gripper block-stacking environment, pinned to PyBullet 1.2.9. The full Sawyer environment is marked forthcoming; this is not a verified learned NTP model/training/checkpoint release."},"evidence":[{"url":"https://arxiv.org/abs/1710.01813","label":"Original primary paper and appendix","fields":["date","summary","policy","modalities","caveats","imageCandidates","institution"],"note":"Survey citation is used only for discovery. Method and deployment facts come from the original paper; the date is its first submission where available. Affiliations are transcribed from the PDF author block / footnotes; LMPC affiliation is confirmed by its official project page."},{"url":"https://raw.githubusercontent.com/StanfordVL/ntp/master/README.md","label":"Author repository README","fields":["links.code"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://raw.githubusercontent.com/StanfordVL/ntp/master/LICENSE","label":"Repository LICENSE","fields":["links"],"note":"Research/project provenance; current artifact availability and licensing are superseded by the separately cited 2026-10-19 file inventories."},{"url":"https://github.com/StanfordVL/NTP-vat-release","label":"Current artifact inventory and release scope","fields":["links","release"],"note":"2026-10-19 current file-tree/implementation/model-data inventory audit; release scope and licensing are independent. Not a runtime reproduction."}],"caveats":["Real robot experiments use object-location observations; video-conditioned and state-conditioned settings are separate.","Hand-coded expert programs supply structured training traces and bottom-level execution primitives."],"verified":"2026-10-19"},{"id":"mil","name":"Meta-Imitation Learning (MIL)","title":"One-Shot Visual Imitation Learning via Meta-Learning","date":"2017-09-14","year":2017,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A meta-learned visual policy acquires a new manipulation task from one demonstration through a small gradient update.","institution":"UC Berkeley","venue":"CoRL 2017","modalities":["RGB","Proprioception"],"environments":["Simulation","Real world"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/1709.04905","project":"https://sites.google.com/view/one-shot-imitation","code":"https://github.com/tianheyu927/mil"},"policy":{"family":"Meta-imitation","baseModel":"MAML-style convolutional visuomotor policy with spatial softmax and learned adaptation loss.","modelSize":"Not reported","inputs":"RGB image and robot state; one robot demonstration for task adaptation, including a video-only demonstration variant.","outputs":"Continuous arm controls; real PR2 setup predicts end-effector linear/angular velocity.","trainingData":"Simulated reaching / pushing demonstrations; real placing data contains 1,293 demonstrations, including 96 validation demonstrations.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Behavior-cloning meta-training with gradient-based one-shot adaptation; demonstrations without actions supported using a learned loss.","evaluation":"Simulated reaching and object pushing; real PR2 placing into a demonstrated target container."},"release":{"code":"Available","weights":"Not verified","data":"Not verified","license":"MIT for repository code; external model, dataset and dependency terms remain separate.","notes":"Author project links the original MIL implementation; artifact availability is independent of the later DAML work."},"evidence":[{"url":"https://arxiv.org/abs/1709.04905","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."},{"url":"https://sites.google.com/view/one-shot-imitation","label":"Official project","fields":["links","release"],"note":"Author-linked project and current release checked on the catalog cutoff."},{"url":"https://github.com/tianheyu927/mil","label":"Official code / release documentation","fields":["links","release"],"note":"Current repository documentation; paper settings and subsequently released artifacts are distinguished."},{"url":"https://github.com/tianheyu927/mil/blob/HEAD/LICENSE","label":"Repository LICENSE","fields":["release.license"],"note":"Actual license file inspected; code license does not automatically license data or weights."}],"caveats":["Adaptation changes network weights; it is not frozen-weight ICL."],"verified":"2026-10-17"},{"id":"one-shot-imitation","name":"One-Shot Imitation Learning","title":"One-Shot Imitation Learning","date":"2017-03-21","year":2017,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A demonstration-conditioned attention policy learns new block-stacking layouts from one example without a task-specific parameter update.","institution":"UC Berkeley / OpenAI","venue":"NeurIPS 2017","modalities":["Proprioception"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/1703.07326"},"policy":{"family":"Meta-imitation","baseModel":"Temporal convolution, recurrent soft attention and a manipulation network; trained from scratch.","modelSize":"Not reported","inputs":"One state/action demonstration and current planar gripper and block coordinates.","outputs":"Current gripper control distribution for block stacking.","trainingData":"140 training layouts with 1,000 scripted trajectories per task; 43 held-out task layouts.","trainingTime":"Up to 1,000 epochs; appendix reports up to one hour per policy.","trainingHardware":"One NVIDIA Titan X Pascal GPU for the reported training timing.","controlRate":"Not reported","trainingMethod":"Behavior cloning or DAGGER across paired demonstrations; temporal dropout and attention.","evaluation":"Simulated block stacking with 2–10 blocks and novel stacking layouts; particle-reaching diagnostic experiments."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/1703.07326","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."}],"caveats":["The policy receives object coordinates at deployment; it is not an RGB or measured-depth policy.","Historical contextual-policy foundation, with simplified planar stacking rather than a real-robot precision benchmark."],"verified":"2026-10-17"},{"id":"up-osi","name":"UP-OSI","title":"Preparing for the Unknown: Learning a Universal Policy with Online System Identification","date":"2017-02-08","year":2017,"collections":["policy"],"categories":["agent-rsi-icl","visual-precision-rl"],"resourceType":"policy","summary":"A universal policy uses online identification from recent state/action history to adapt to unknown dynamics, including object-mass-dependent block throwing.","institution":"Georgia Institute of Technology / Google Brain","venue":"Preprint","modalities":["Proprioception"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper"],"links":{"paper":"https://arxiv.org/abs/1702.02453"},"policy":{"family":"RL","baseModel":"Feed-forward universal control policy and an online-system-identification neural network.","modelSize":"Not reported","inputs":"Current arm joint positions/velocities and simulated block position p_block, plus recent state/action history; inferred dynamics parameters replace privileged true parameters at deployment.","outputs":"Continuous controls; manipulation experiment commands a simulated arm grasping and throwing a block.","trainingData":"Simulator-generated experience with randomized model parameters; true physical parameters supervise the identification model.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Train the universal policy over a dynamics distribution, then train online parameter identification from interaction histories.","evaluation":"Four simulated dynamics tasks, including an arm throwing a block to a target height under unknown block mass."},"release":{"code":"Not verified","weights":"Not verified","data":"Not verified","license":"Not verified","notes":"Original paper reviewed; no public artifact is implied by a citation alone."},"evidence":[{"url":"https://arxiv.org/abs/1702.02453","label":"Original paper / author report","fields":["title","date","summary","institution","policy","caveats"],"note":"Original full paper and supplementary experimental / implementation details; deployment inputs are separated from supervision and benchmark state."}],"caveats":["The arm experiment uses state observations, not RGB/depth.","True dynamics parameters are training supervision, while deployment uses history-based estimates.","The manipulation policy observes privileged simulator block position, not only robot proprioception."],"verified":"2026-10-17"},{"id":"liber0-preview","name":"Liber-0 Preview","title":"Liber-0 Preview hierarchical RoboDojo policy","date":"","year":0,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Adds a learned visual-language subtask controller and episodic visual cues to the released Liber inference backend for bimanual manipulation.","institution":"LiberAI","venue":"Official project / technical release","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"project":"https://github.com/XPolicyLab/XPolicyLab/tree/main/policy/Liber_0_preview","code":"https://github.com/XPolicyLab/XPolicyLab/tree/main/policy/Liber_0_preview","weights":"https://huggingface.co/LiberAI/Liber0-Preview-Robodojo"},"policy":{"family":"VLA","baseModel":"Qwen3-VL context model plus a Qwen3-VL-derived shared visual/action flow backend loaded through the Liber-0 Lite runtime; HiDream-O1-Image base assets.","modelSize":"Complete parameter count Not reported; checkpoint byte sizes are not parameter counts.","inputs":"Head and two wrist RGB views, language and 14-dimensional ARX X5 state; maintains subtask hierarchy, episode context and a visual cue image.","outputs":"32-step chunks of 14-dimensional dual-arm joint/gripper actions; default executes 16 steps before replanning.","trainingData":"Not disclosed; this is an evaluation-only release.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Default ten flow-integration steps and 16 executed actions per replan; physical control rate Not reported.","trainingMethod":"Released inference runs a learned context controller to select a current subtask and visual cue, then a conditioned flow action backend. Training code is pending.","evaluation":"RoboDojo ARX X5 joint-control adapter and a separately released Preview checkpoint; batched evaluation is explicitly unsupported."},"release":{"code":"Partial","weights":"Available","data":"Not verified","license":"XPolicyLab adapter under Apache-2.0; shared Liber vendor components retain MIT/Apache notices. Preview weight license Not reported in the inspected model card/repository.","notes":"Public checkpoint revision 2773a04e3b8f09096ec7beb6105bd6ed80c91da8 includes model.pt, configuration/statistics and a separate context/ model. It reuses the Lite runtime but requires its own Preview checkpoint and context model. Training is not released."},"evidence":[{"url":"https://github.com/XPolicyLab/XPolicyLab/tree/94dbf9505f2f87cf59c3ee8ba5912cd236781771/policy/Liber_0_preview","label":"Author-contributed Preview adapter and source","fields":["title","institution","policy","modalities","release"],"note":"Inspected README, model.py, runtime.py, generation.py, deploy.yml and downloader at a fixed XPolicyLab commit. No manuscript or explicit first public policy-release date is supplied."},{"url":"https://huggingface.co/LiberAI/Liber0-Preview-Robodojo/tree/2773a04e3b8f09096ec7beb6105bd6ed80c91da8","label":"Actual released Preview artifact inventory and configs","fields":["links.weights","release.weights","policy.inputs","policy.outputs","policy.baseModel"],"note":"Verified public ungated repository file inventory, manifest, config.yaml and context/config.json. No weights downloaded or executed; no total model size inferred from storage bytes."},{"url":"https://github.com/XPolicyLab/XPolicyLab/blob/94dbf9505f2f87cf59c3ee8ba5912cd236781771/policy/Liber_0_lite/NOTICE.md","label":"Shared runtime component notices","fields":["release.license"],"note":"Preview README explicitly reuses the Lite runtime; root Apache-2.0 and third-party component notices do not establish a license for Preview weights."}],"caveats":["Preview and Lite are separate scored assets and separate checkpoints; shared inference code does not make the methods identical.","Full training procedure, corpus and compute are not disclosed.","An original method diagram was not located in the reviewed sources; the cover is an explicitly labeled excerpt from the released implementation."],"verified":"2026-10-17","relatedIds":["liber0-lite"]},{"id":"liber0-lite","name":"Liber0-Lite","title":"Liber0-Lite RoboDojo policy","date":"","year":0,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Releases an author-contributed checkpoint and self-contained inference backend for bimanual RoboDojo evaluation.","institution":"LiberAI","venue":"Official project / technical release","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"project":"https://github.com/XPolicyLab/XPolicyLab/tree/main/policy/Liber_0_lite","code":"https://github.com/XPolicyLab/XPolicyLab/tree/main/policy/Liber_0_lite","weights":"https://huggingface.co/LiberAI/Liber0-Lite-Robodojo"},"policy":{"family":"VLA","baseModel":"Vision encoder and denoising action decoder; precise foundation/backbone identity not disclosed","modelSize":"Not disclosed; 53 GB download footprint is not a model parameter count","inputs":"RGB views, language and ARX X5 joint-state context; maintains first-frame cue and per-environment episode state","outputs":"Dual-arm joint-action chunks under ARX X5 contract; checkpoint configuration governs horizon/normalization","trainingData":"Not disclosed; training code pending","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported","trainingMethod":"Inference implementation and checkpoint only; paper/training release announced","evaluation":"Official RoboDojo Liber-0 Lite submission; batched inference support separate from quality equivalence"},"release":{"code":"Partial","weights":"Available","data":"Not verified","license":"Apache-2.0 for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Public project and model release; undisclosed training details remain explicitly unknown. A manuscript is not required to identify the released policy."},"evidence":[{"url":"https://github.com/XPolicyLab/XPolicyLab/tree/main/policy/Liber_0_lite","label":"Official model project / technical release","fields":["title","date","policy","release"],"note":"Research-report facts and current executable release are scoped separately; undated releases retain an unknown publication date."},{"url":"https://github.com/XPolicyLab/XPolicyLab/tree/main/policy/Liber_0_lite","label":"Official implementation / author-contributed adapter","fields":["policy.inputs","policy.outputs","release.code"],"note":"Actual deployment contract is checked; adapter presence alone is not evidence of benchmark participation."},{"url":"https://github.com/XPolicyLab/XPolicyLab/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["Liber-0 Preview and Liber-0 Lite are separately scored assets; identity/architecture equivalence not established.","No manuscript or original architecture figure verified."],"verified":"2026-10-19","relatedIds":["liber0-preview"]},{"id":"mm-abc","name":"MM-ABC","title":"MM-ABC mobile-manipulation policy","date":"","year":0,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Uses subsystem-aware flow actions for a mobile dual-arm dexterous robot with a preserved embodiment/schema contract.","institution":"Not reported","venue":"Official project / technical release","modalities":["RGB","Proprioception","Language"],"environments":["Real world"],"embodiments":["Mobile manipulator","Bimanual","Dexterous hand"],"links":{"project":"https://github.com/XPolicyLab/XPolicyLab/tree/main/policy/MM_ABC","code":"https://github.com/XPolicyLab/XPolicyLab/tree/main/policy/MM_ABC"},"policy":{"family":"VLA","baseModel":"Qwen3-VL-4B-Instruct with dual-stream flow action expert; frozen VGGT-Omega teacher in optional training recipe","modelSize":"4B VLM backbone; full expert-inclusive total not disclosed","inputs":"Head, left-wrist and right-wrist RGB, language and mobile proprioception; teacher is not loaded for inference","outputs":"32-step chunks, execute 16; 75D default absolute model layout, 80D pretrain canonical layout; base/waist/arms/hands","trainingData":"Not disclosed; adapter converts custom mobile HDF5 to LeRobotV3.0 with policy-specific packing","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"10Euler integration steps; no measured feedback/servo rate disclosed","trainingMethod":"Clean-action prediction with velocity-space loss; optional future-alignment teacher; from-backbone and pretrained recipes","evaluation":"Integrated m92uw mobile embodiment interface; no official RoboTwin/RoboDojo scored participation verified"},"release":{"code":"Partial","weights":"Not verified","data":"Not verified","license":"Apache-2.0 for repository code, verified from LICENSE; pretrained models, datasets, dependencies and figure rights retain separate terms.","notes":"Public project and model release; undisclosed training details remain explicitly unknown. A manuscript is not required to identify the released policy."},"evidence":[{"url":"https://github.com/XPolicyLab/XPolicyLab/tree/main/policy/MM_ABC","label":"Official model project / technical release","fields":["title","date","policy","release"],"note":"Research-report facts and current executable release are scoped separately; undated releases retain an unknown publication date."},{"url":"https://github.com/XPolicyLab/XPolicyLab/tree/main/policy/MM_ABC","label":"Official implementation / author-contributed adapter","fields":["policy.inputs","policy.outputs","release.code"],"note":"Actual deployment contract is checked; adapter presence alone is not evidence of benchmark participation."},{"url":"https://github.com/XPolicyLab/XPolicyLab/blob/main/LICENSE","label":"Actual repository code license","fields":["release.license"],"note":"2026-10-19 actual file inspection; applies to repository code, not a blanket grant over models/data/third-party assets."}],"caveats":["Integration is not benchmark participation; mock hold-state debug mode is not learned-policy performance.","Required mmabc_pretrain_200k checkpoint is not distributed by adapter; no independent weights URL or architecture figure verified."],"verified":"2026-10-19"},{"id":"physicalrsi","name":"PhysicalRSI 1.0","title":"PhysicalRSI 1.0: Native System 1–2 Co-evolution","date":"","year":0,"collections":["policy"],"categories":["agent-rsi-icl"],"resourceType":"policy","summary":"A multimodal agent revises an executable harness that selects and composes code-policy and VLA skills. Rollout evidence guides successive harness versions.","institution":"Not reported","venue":"Official project report","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"project":"https://yanming03.github.io/PhysicalRSI/v2/"},"policy":{"family":"Hybrid","baseModel":"A multimodal System 2 agent with a System 1 skill pool including code policies, π0.5 and π0.5 with sparse memory; the public page does not identify one universal agent checkpoint.","modelSize":"Not reported","inputs":"Task goal, RGB observations, proprioception, episode state, execution feedback and retained skill / control-code artifacts.","outputs":"Harness-selected code or VLA skill actions through a physical API; the agent can revise routing, tools, control code and skills between improvement rounds.","trainingData":"Rollout success / failure evidence revises the harness; underlying VLA skill training is separate.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"The website labels 0.1–1 Hz planning and 20–50 Hz control as illustrative estimates, not measured policy throughput.","trainingMethod":"Frozen agent model in the illustrated harness-evolution loop: vary candidate programs, evaluate, select and inherit. This is distinct from training a new VLA checkpoint.","evaluation":"RoboDojo with published task / layout result sheets; skill inheritance, composition and memory case studies."},"release":{"code":"Partial","weights":"Not verified","data":"Partial","license":"Not verified; the author project and the contributed RoboDojo adapter have separate release scopes.","notes":"Public project and an author-contributed evaluation adapter exist. A standalone upstream repository at the previously suggested URL could not be verified."},"evidence":[{"url":"https://yanming03.github.io/PhysicalRSI/v2/","label":"Official project / author report","fields":["title","date","summary","policy","release","caveats"],"note":"Project-only evidence; any reported date is an explicit public report or code-release event, not a manuscript submission."}],"caveats":["No paper submission or explicit initial publication date was established; this record is undated.","The contributed benchmark adapter selects a registered skill composition at episode start and keeps it fixed until reset; it does not by itself release the entire research evolution loop.","Distinct from the unrelated PhysicalRSI / PhysicalRSI liquid-handling infrastructure repository."],"verified":"2026-10-17"},{"id":"rex-m1-preview","name":"Rex-M1 Preview","title":"Rex-M1 Preview long-context RoboDojo policy","date":"","year":0,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Extends Xiaomi-Robotics-1 inference with sampled head-camera history and fixed-budget history tokens for bimanual RoboDojo evaluation.","institution":"Futian Lab / VisIncept","venue":"Official project / technical release","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"project":"https://github.com/XPolicyLab/XPolicyLab/tree/main/policy/Rex_M1_preview","code":"https://github.com/XPolicyLab/XPolicyLab/tree/main/policy/Rex_M1_preview","weights":"https://modelscope.cn/models/gzx2019/Rex-M1-preview"},"policy":{"family":"VLA","baseModel":"Xiaomi-Robotics-1-5B derivative with Qwen3-VL-4B-Instruct and its action model; modified constant-budget history-token pooling.","modelSize":"Xiaomi-Robotics-1-5B is the declared base checkpoint; complete Rex-M1 Preview parameter count Not reported.","inputs":"Current head and two wrist RGB views, language and robot state; released config samples up to 20 head-camera history frames at stride 25 and pools each to 16 tokens.","outputs":"30-step action chunks; default adapter executes 15 before replanning. The internal packed 60-dimensional layout contains end-effector slots and is decoded to dual-ARX5 EE/gripper commands, not 60 independently controlled joints.","trainingData":"Not disclosed for the released Rex-M1 Preview checkpoint; upstream Xiaomi pretraining corpus is not asserted to be this derivative’s training dataset.","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"30-step chunk / default 15-step replan; physical feedback/control Hz Not reported.","trainingMethod":"Released derivative uses visual history at inference. Upstream training entry points remain vendored, but the required history sampling/collation is absent; checkpoint ptp_length=0 disables the retained past-reconstruction auxiliary path.","evaluation":"Author-contributed RoboDojo simulation adapter: dual ARX5 with action_type=ee. One policy server per evaluation to avoid mixing environment history."},"release":{"code":"Partial","weights":"Available","data":"Not verified","license":"Apache-2.0 for vendored Xiaomi source and Rex modifications, verified from LICENSE/NOTICE. The ModelScope checkpoint card declares Apache-2.0; no separate weight-license file was present in its inspected inventory.","notes":"ModelScope revision aeb65fc1944b6871bc1c17c353085fa84e9a84bb publicly lists config.py and last.ckpt/checkpoint/mp_rank_00_model_states.pt with integrity hashes. This release supplies inference; vendored upstream training scripts do not reproduce history-policy training."},"evidence":[{"url":"https://github.com/XPolicyLab/XPolicyLab/tree/94dbf9505f2f87cf59c3ee8ba5912cd236781771/policy/Rex_M1_preview","label":"Author-contributed adapter and source","fields":["title","institution","policy","modalities","release"],"note":"Inspected README, INSTALLATION.md, model.py, deploy.yml and NOTICE at a fixed commit. Explicitly distinguishes usable history inference from the incomplete derivative-training path. Public policy release date is unspecified."},{"url":"https://modelscope.cn/models/gzx2019/Rex-M1-preview","label":"Released checkpoint model card, inventory and config","fields":["release.weights","release.license","policy.baseModel","policy.inputs","policy.outputs"],"note":"Public ModelScope API returned README, config.py and actual 10,226,684,560-byte tensor file with SHA-256. Config specifies 20 history frames, stride 25, 16-token pooling and action length 30. No tensors downloaded or executed."},{"url":"https://github.com/XPolicyLab/XPolicyLab/blob/94dbf9505f2f87cf59c3ee8ba5912cd236781771/policy/Rex_M1_preview/NOTICE","label":"Actual derivative notice and vendored LICENSE","fields":["release.license"],"note":"NOTICE explicitly licenses Rex modifications under Apache-2.0 and enumerates the modified Xiaomi/Qwen files; source licenses are not silently generalized to all third-party assets."}],"caveats":["No paper is released and the derivative’s training data/compute are undisclosed.","Long-context observations do not by themselves establish recursive self-improvement or weight updates.","The released checkpoint has ptp_length=0; the retained auxiliary code path should not be described as active at inference.","An original method diagram was not located in the reviewed sources; the cover is a labeled source-code interface preview."],"verified":"2026-10-17","relatedIds":["xiaomi1"]},{"id":"simate-beta","name":"Simate-beta","title":"Simate-beta AE memory policy","date":"","year":0,"collections":["policy"],"categories":["generalist-foundation"],"resourceType":"policy","summary":"Provides an author-contributed inference-only memory policy with compressed history tokens and a released RoboDojo checkpoint.","institution":"SIPAILab","venue":"Official project / technical release","modalities":["RGB","Proprioception","Language"],"environments":["Simulation"],"embodiments":["Parallel-jaw gripper","Bimanual"],"links":{"project":"https://github.com/XPolicyLab/XPolicyLab/tree/main/policy/Simate_beta","code":"https://github.com/XPolicyLab/XPolicyLab/tree/main/policy/Simate_beta","weights":"https://huggingface.co/SIPAILab/sipai-robodojo-eval-ae"},"policy":{"family":"VLA","baseModel":"SigLIP vision encoder, AE history compressor and flow action model; tokenization uses PaliGemma SentencePiece","modelSize":"Not disclosed; no parameter count inferred from checkpoint byte size","inputs":"Three current RGB views, language, current joint state and 25 primary-camera history frames at 20 action-step intervals","outputs":"Relative dual-arm joint commands plus absolute gripper openness; default executes 10 of maximum 50-step chunks","trainingData":"Not disclosed; public release is evaluation-only","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Not reported; history sampling interval and 10 flow integration steps are not feedback rate","trainingMethod":"Training procedure not released; AE compresses history 16 × 16 tokens to 4 × 4 at inference","evaluation":"RoboDojo ARX X5 joint-action submission; adapter debug checks are not policy-quality evaluation"},"release":{"code":"Partial","weights":"Available","data":"Not verified","license":"Apache-2.0 weights/model card; PaliGemma tokenizer access terms apply separately","notes":"Public project and model release; undisclosed training details remain explicitly unknown. A manuscript is not required to identify the released policy."},"evidence":[{"url":"https://github.com/XPolicyLab/XPolicyLab/tree/main/policy/Simate_beta","label":"Official model project / technical release","fields":["title","date","policy","release"],"note":"Research-report facts and current executable release are scoped separately; undated releases retain an unknown publication date."},{"url":"https://github.com/XPolicyLab/XPolicyLab/tree/main/policy/Simate_beta","label":"Official implementation / author-contributed adapter","fields":["policy.inputs","policy.outputs","release.code"],"note":"Actual deployment contract is checked; adapter presence alone is not evidence of benchmark participation."}],"caveats":["No paper supplied and no verified architecture image; training data, cost and complete backbone remain unknown."],"verified":"2026-10-17"},{"id":"t-wla","name":"T-WLA","title":"T-WLA: Bridging Slow Vision and Fast Touch","date":"","year":0,"collections":["policy"],"categories":["tactile-foundation"],"resourceType":"policy","summary":"Caches history-aware visual context for action planning and reuses it for fast tactile residual editing between visual updates.","institution":"Not reported","venue":"Anonymous project","modalities":["RGB","Tactile","Proprioception","Language"],"environments":["Real world"],"embodiments":["Dexterous hand"],"links":{"project":"https://anonymous-submission-20.github.io/T-WLA2.github.io/"},"policy":{"family":"World-action model","baseModel":"Vision-language/action branch with visual context bank and tactile residual editor; exact backbone not reported","modelSize":"Not reported","inputs":"Current/past RGB, tactile history, instruction and current action chunk","outputs":"Base action chunks and residual edits to unexecuted actions","trainingData":"30 demonstrations per real task; EgoTouch and Tachin hand-motion prediction","trainingTime":"Not reported","trainingHardware":"Not reported","controlRate":"Project reports visual39.7 Hz and tactile119.1 Hz update rates; hardware/measurement scope not disclosed","trainingMethod":"Visual-context caching plus tactile editing; future-image/pressure auxiliary supervision during training","evaluation":"EgoTouch/Tachin future hand motion; egg handling, single-sheet pickup, paper-cup nesting, paper-bag opening"},"release":{"code":"Not released","weights":"Not released","data":"Not verified","license":"Not verified","notes":"Anonymous project page accessible; no public paper, implementation or checkpoint links exposed."},"evidence":[{"url":"https://anonymous-submission-20.github.io/T-WLA2.github.io/","label":"Anonymous author project page","fields":["title","summary","policy","imageCandidates","release"],"note":"Page accessed directly via urllib; supports reported data/rates/results but supplies no publication date, paper, code or hardware details."}],"caveats":["Anonymous author project page does not expose a publication date; catalogued as undated.","Hand-motion prediction errors and robot task success are distinct evaluations.","Future image/pressure heads are removed at deployment."],"verified":"2026-10-17","releaseByCollection":{"policy":{"code":"Not released","weights":"Not released","data":"Not verified","license":"Not verified","notes":"Anonymous project page accessible; no public paper, implementation or checkpoint links exposed."}},"relatedIds":[]}],"figures":{"pace":{"policy":{"path":"/images/catalog/pace-policy.webp","original":"https://arxiv.org/html/2610.16308v1/figs/Figure1.png","page":"https://arxiv.org/html/2610.16308","credit":"PACE authors","alt":"PACE — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"5553e5acb172872cdfadb722780505c903674815a040aa4153edcdab4486d7e6","width":1500,"height":923,"note":"Author method diagram showing the observation, representation and action-generation pathways."}},"vista":{"policy":{"path":"/images/catalog/vista-policy.webp","original":"https://arxiv.org/html/2610.03333v1/method.png","page":"https://arxiv.org/html/2610.03333","credit":"VISTA authors","alt":"VISTA — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"e5fc3b1b09792fc2b521ea0d4f154a5e30c9c040f8fa7d3297483ccf26d3bb02","width":1500,"height":616,"note":"Author method diagram showing the observation, representation and action-generation pathways."}},"simpletouch":{"policy":{"path":"/images/catalog/simpletouch-policy.webp","original":"https://arxiv.org/html/2610.02784v1/simpletouch_overview.png","page":"https://arxiv.org/html/2610.02784","credit":"SimpleTouch authors","alt":"SimpleTouch — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"6b6dd157f1b5dea912f1747cf741f9849e95f40229c392e17589521a6246a131","width":810,"height":298,"note":"Author method diagram showing the observation, representation and action-generation pathways."}},"tacdyn-wam":{"policy":{"path":"/images/catalog/tacdyn-wam-policy.webp","original":"https://arxiv.org/html/2610.00638v1/figures/main_framework.png","page":"https://arxiv.org/html/2610.00638","credit":"TacDyn-WAM authors","alt":"TacDyn-WAM — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"79a79676eb243b515a414a25c958d776ed4ff5495a4cf7eb42edf9eef2c3585b","width":942,"height":482,"note":"Author method diagram showing sensory encoding,multimodal computation and action/control pathways."}},"vistacalign":{"policy":{"path":"/images/catalog/vistacalign-policy.webp","original":"https://arxiv.org/html/2609.30959v2/pipeline.png","page":"https://arxiv.org/html/2609.30959","credit":"VisTacAlign authors","alt":"VisTacAlign — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"e72b0bf5c27fc72b37d0152af0d459357d383930c3c23301161767e74b421438","width":1500,"height":1395,"note":"Author method diagram showing the observation, representation and action-generation pathways."}},"tactic-encoder-study":{"policy":{"path":"/images/catalog/tactic-encoder-study-policy.webp","original":"https://arxiv.org/html/2609.30969v1/tactic_eyecandy.png","page":"https://arxiv.org/html/2609.30969","credit":"TACTIC — encoder study authors","alt":"TACTIC — encoder study — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"177c54882839bc920f058704c511e5dffce0b828c552aac0e9529ec6f325b38f","width":468,"height":479,"note":"Author method diagram showing sensory encoding,multimodal computation and action/control pathways."},"benchmark":{"path":"/images/catalog/tactic-encoder-study-benchmark.webp","original":"https://arxiv.org/pdf/2609.30969#page=3","page":"https://arxiv.org/abs/2609.30969","credit":"TACTIC — encoder study authors","alt":"TACTIC — encoder study — Four real-world tasks with five annotated execution stages.","kind":"task-overview","reviewed":"2026-10-17","width":1601,"height":1483,"note":"Figure 2. Four real-world tasks with five annotated execution stages. Rendered from the primary paper PDF (page 3), cropped to the figure, and visually reviewed.","sourceSha256":"e5dad2933df410f9a6796408223b8bae0b6ed117868769b0c62dae400820e5cf"}},"me-dex-1":{"policy":{"path":"/images/catalog/me-dex-1-policy.webp","original":"https://arxiv.org/html/2609.21449v2/mainpipeline.png","page":"https://arxiv.org/html/2609.21449","credit":"ME-Dex 1.0 authors","alt":"ME-Dex 1.0 — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"1f8c44bddaa19434829ada15793931fe034e2571db10d0e18ca46d84013a41f3","width":1500,"height":1055,"note":"Author method diagram: observation processing, multimodal learning and action/control pathways."}},"foretac-vla":{"policy":{"path":"/images/catalog/foretac-vla-policy.webp","original":"https://foretac-vla.github.io/assets/Fig1.png","page":"https://foretac-vla.github.io/","credit":"ForeTac-VLA authors","alt":"ForeTac-VLA — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"f9759f46cf9421fdfd165f36b277f4ff5ff49b1373802af9490e037b5102db21","width":1500,"height":927,"note":"Author framework diagram showing tactile forecasting and action conditioning."}},"unidex-vitac":{"policy":{"path":"/images/catalog/unidex-vitac-policy.webp","original":"https://arxiv.org/html/2609.16504v1/figure_1.png","page":"https://arxiv.org/html/2609.16504","credit":"UniDex-ViTac authors","alt":"UniDex-ViTac — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"d52b58e16b2935df9ee39e0854b1962c3762ba80d75cda3a05646dbaad6950e2","width":837,"height":391,"note":"Author method diagram showing the observation, representation and action-generation pathways."}},"visible-touch":{"policy":{"path":"/images/catalog/visible-touch-policy.webp","original":"https://arxiv.org/html/2609.14156v2/figures/overview_postrebuttal_v3.png","page":"https://arxiv.org/html/2609.14156","credit":"Visible Touch 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architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"dcb5a312f1d4ec76afef78c7100f7da1e154fe83f4dd45300da90b35fb08ef05","width":1500,"height":532,"note":"Figure 3 : Action-conditioned visuo-tactile model network architecture."}},"openvitac":{"benchmark":{"path":"/images/catalog/openvitac-benchmark.webp","original":"https://fvl-repo.github.io/OpenViTac/assets/capability_taxonomy.webp","page":"https://fvl-repo.github.io/OpenViTac/","credit":"OpenViTac authors","alt":"OpenViTac — taxonomy","kind":"taxonomy","reviewed":"2026-10-17","sourceSha256":"7480d71d2ea851c5177e33aa580bde33fe2908a6bd1981fa940b4cbe69f26e17","width":1500,"height":1496,"note":"Four capability groups and the associated tasks; more informative for benchmark browsing than a policy pipeline."},"policy":{"path":"/images/catalog/openvtla-policy.webp","original":"https://arxiv.org/html/2610.10384v1/tactile_injection.png","page":"https://arxiv.org/html/2610.10384","credit":"OpenVTLA authors","alt":"OpenVTLA — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"f950a7c5645e07588fc569b4392c504957cc3abceb5ac816a73f6443b19e912f","width":1500,"height":656,"note":"Author method diagram: observation processing, multimodal learning and action/control pathways."}},"univtac":{"benchmark":{"path":"/images/catalog/univtac-benchmark.webp","original":"https://univtac.github.io/tasks.svg","page":"https://univtac.github.io/","credit":"UniVTAC authors","alt":"UniVTAC — task overview","kind":"task-overview","reviewed":"2026-10-17","sourceSha256":"8daf8a6eda4ff2e26fb146a3d0048a380d139082a355547e0d25ebd06439f5ba","width":1400,"height":518,"note":"Official complete eight-task gallery."}},"tac-bench":{"benchmark":{"path":"/images/catalog/tac-bench-benchmark.webp","original":"https://peilinwu.site/tac-bench.github.io/static/assets/figures/tasks.png","page":"https://peilinwu.site/tac-bench.github.io/","credit":"Tac-Bench authors","alt":"Tac-Bench — task overview","kind":"task-overview","reviewed":"2026-10-17","sourceSha256":"4138ee51b26f68e5c786c1ef8898bce218484090561bc357858fdeed35b11798","width":1500,"height":813,"note":"Full 14-task gallery from official page."}},"bench2dex":{"benchmark":{"path":"/images/catalog/bench2dex-benchmark.webp","original":"https://bench2dex.github.io/assets/img/figures/overview.png","page":"https://bench2dex.github.io/","credit":"Bench2Dex authors","alt":"Bench2Dex — task overview","kind":"task-overview","reviewed":"2026-10-17","sourceSha256":"f7c21b13bb8a2ec066589ada0ddcbc2211e0ffcfecdb1be5195807f0cd4d1901","width":1500,"height":846,"note":"Embodiments, task catalogue and multimodal benchmark overview."}},"softvtbench":{"benchmark":{"path":"/images/catalog/softvtbench-benchmark.webp","original":"https://softvtbench.github.io/assets/fig1_overview.png","page":"https://softvtbench.github.io/","credit":"SoftVTBench authors","alt":"SoftVTBench — task overview","kind":"task-overview","reviewed":"2026-10-17","sourceSha256":"d50803c4de239f08e6eba3983ac03f5c2121b6e2bdf538c99ef5d565c9fe7c95","width":1500,"height":858,"note":"Four diagnostic suites and matched rigid/deformable assets."}},"fmb":{"benchmark":{"path":"/images/catalog/fmb-benchmark.webp","original":"https://functional-manipulation-benchmark.github.io/static/images/FMB%20Objects.jpg","page":"https://functional-manipulation-benchmark.github.io/","credit":"FMB authors","alt":"FMB — task overview","kind":"task-overview","reviewed":"2026-10-17","sourceSha256":"c25d05ba5181d9e64272f67d3510af4f42fb439a63da2ab71a1abbc61c1db5eb","width":1500,"height":444,"note":"Full object and assembly-board diversity."}},"furniturebench":{"benchmark":{"path":"/images/catalog/furniturebench-benchmark.webp","original":"https://clvrai.github.io/furniture-bench/website_img/all_furniture.png","page":"https://clvrai.github.io/furniture-bench/","credit":"FurnitureBench authors","alt":"FurnitureBench — task overview","kind":"task-overview","reviewed":"2026-10-17","sourceSha256":"6ebc1bf339fa205caf01ee2a1c51e7a0493074fbe475cb86b5527c4118300f7d","width":1500,"height":848,"note":"All eight miniature furniture assemblies."}},"roco-aaai-2026":{"benchmark":{"path":"/images/catalog/roco-aaai-2026-benchmark.webp","original":"https://rocochallenge.github.io/RoCo-AAAI2026/assets/images/hero_new.png","page":"https://rocochallenge.github.io/RoCo-AAAI2026/","credit":"RoCo AAAI 2026 authors","alt":"RoCo AAAI 2026 — task overview","kind":"task-overview","reviewed":"2026-10-17","sourceSha256":"b55476075728d3a8612f60baa133867570f8b62f407e5c70fb34c0278849ba81","width":1500,"height":844,"note":"Official gearbox assembly scene and challenge setup."}},"tactidex":{"benchmark":{"path":"/images/catalog/tactidex-benchmark.webp","original":"https://tactidex.github.io/static/images/f1_dataset.png","page":"https://tactidex.github.io/","credit":"TactiDex authors","alt":"TactiDex — dataset and capture overview","kind":"dataset-overview","reviewed":"2026-10-17","sourceSha256":"5fef4ba08c36b5883d2ce6eb7f7413ca01505e910a8163a83bd94c5afe6e27e3","width":1500,"height":821,"note":"Official dataset breadth / task overview."}},"robotwin2":{"benchmark":{"path":"/images/catalog/robotwin2-benchmark.webp","original":"https://robotwin-platform.github.io/doc/tasks/50-tasks.gif","page":"https://robotwin-platform.github.io/doc/tasks/index.html","credit":"RoboTwin 2.0 authors","alt":"RoboTwin 2.0 — task overview","kind":"task-overview","reviewed":"2026-10-17","sourceSha256":"4340a690563ba686aae0a931c2bd41fedeacdf30b07cd18644d84275be82c171","width":640,"height":240,"note":"Official complete 50-task montage."}},"dp":{"policy":{"path":"/images/catalog/dp-policy.webp","original":"https://arxiv.org/html/2303.04137v5/policy_input_output.png","page":"https://arxiv.org/abs/2303.04137","credit":"Diffusion Policy authors","alt":"Diffusion Policy — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"e2ddc6995b3597e704e6b8e8cebac544bc0c7769b4336c638bcaf60ce3f088c0","width":1500,"height":409,"note":"Figure 2 : Diffusion Policy Overview a) General formulation. At time step t t , the policy takes the latest T o T_{o} steps of observation data O t O_{t} as input and outputs T a T"}},"act":{"policy":{"path":"/images/catalog/act-policy.webp","original":"https://tonyzhaozh.github.io/aloha/resources/algo.png","page":"https://tonyzhaozh.github.io/aloha/","credit":"Zhao et al.","alt":"ACT — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"a6f4fd28d77244ee0b6fb9b24abdab17a909cc3e3dabebd6adc8d0d25aed77e5","width":1500,"height":414,"note":"ACT CVAE training and action-chunk prediction diagram."}},"dp3":{"policy":{"path":"/images/catalog/dp3-policy.webp","original":"https://arxiv.org/html/2403.03954v7/method_v3.png","page":"https://arxiv.org/abs/2403.03954","credit":"3D Diffusion Policy (DP3) authors","alt":"3D Diffusion Policy (DP3) — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"4bb40a36dbc70c7637b156ff37ddde9105f3b9f8df103628a7e91f883a3dcd9c","width":1500,"height":557,"note":"Fig. 2: Overview of 3D Diffusion Policy (DP3). Above: In the training phase, DP3 simultaneously trains its perception module and decision-making process in an end-to-end manner usi"}},"idp3":{"policy":{"path":"/images/catalog/idp3-policy.webp","original":"https://arxiv.org/html/2410.10803v3/system_v2.png","page":"https://arxiv.org/abs/2410.10803","credit":"Improved 3D Diffusion Policy (iDP3) authors","alt":"Improved 3D Diffusion Policy (iDP3) — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"b1b2f65c9e303212342ac98dddb3b4e553074d426bb43403f38c842f200915f7","width":1500,"height":509,"note":"Fig. 2: Overview of our system. Our system mainly consists of four parts: the humanoid robot platform, the data collection system, the visuomotor policy learning method, and the re"}},"peract":{"policy":{"path":"/images/catalog/peract-policy.webp","original":"https://arxiv.org/html/2209.05451v2/peract_main_fig_v6.png","page":"https://arxiv.org/abs/2209.05451","credit":"PerAct authors","alt":"PerAct — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"7eab08ef809b94ced00273a463092d8eb74f2900dd619c7c500a403160f54e17","width":973,"height":237,"note":"Figure 2: PerAct Overview. PerAct is a language-conditioned behavior-cloning agent trained with supervised learning to detect actions . PerAct takes as input a language goal and a "}},"rvt":{"policy":{"path":"/images/catalog/rvt-policy.webp","original":"https://arxiv.org/html/2306.14896v1/overview_v2.png","page":"https://arxiv.org/abs/2306.14896","credit":"RVT authors","alt":"RVT — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"c43aa93811c240535b296aabecfc606044317f46af46f43909165a10bde32066","width":1500,"height":494,"note":"Figure 2: Overview of RVT. Given RGB-D from sensor(s), we first construct a point cloud of the scene. The point cloud is then used to produce virtual images around the robot worksp"}},"rvt2":{"policy":{"path":"/images/catalog/rvt2-policy.webp","original":"https://arxiv.org/html/2406.08545v1/rvt2-figure2-v4_crop.png","page":"https://arxiv.org/abs/2406.08545","credit":"RVT-2 authors","alt":"RVT-2 — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"470d0edab00a4776d72e73d2ea578075152372db887585a9801039406eb04cc5","width":1500,"height":840,"note":"Fig. 2 : RVT-2 Architecture. Given the current scene and a task instruction, RVT-2 predicts the next key-frame pose. It consists of two stages. The first stage uses fixed virtual v"}},"3dda":{"policy":{"path":"/images/catalog/3dda-policy.webp","original":"https://3d-diffuser-actor.github.io/static/images/arch.jpg","page":"https://arxiv.org/abs/2402.10885","credit":"3D Diffuser Actor authors","alt":"3D Diffuser Actor — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"fae0f87f4eada7c3b5b2e4165498d485d6cae398a17383f02b362c4b4ed5a7c9","width":1500,"height":746,"note":"Author architecture / method figure showing observation encoding and action generation."}},"polarnet":{"policy":{"path":"/images/catalog/polarnet-policy.webp","original":"https://arxiv.org/html/2309.15596v1/figures/method_detailed.png","page":"https://arxiv.org/abs/2309.15596","credit":"PolarNet authors","alt":"PolarNet — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"c1f99ca37ff4c9a53c659652b2dd2367570993b1f01f1910bbe592e87272bdb8","width":1500,"height":507,"note":"Figure 6: Detailed architecture of PolarNet for language guided robotic manipulation. The model takes the input of point cloud { p 1 , … , p N v } \\{p_{1},...,p_{N_{v}}\\} and instr"}},"gnfactor":{"policy":{"path":"/images/catalog/gnfactor-policy.webp","original":"https://raw.githubusercontent.com/YanjieZe/GNFactor/main/docs/gnfactor_method.png","page":"https://github.com/YanjieZe/GNFactor","credit":"Ze et al.","alt":"GNFactor — pipeline","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"03a4bb1ff408b2e722ee3c995316afc1eab2526dfc3e5687114e923c26228dab","width":1500,"height":616,"note":"Joint feature-field reconstruction and behavior-cloning pipeline."}},"rise":{"policy":{"path":"/images/catalog/rise-policy.webp","original":"https://arxiv.org/html/2404.12281v3/overview.png","page":"https://arxiv.org/abs/2404.12281","credit":"RISE authors","alt":"RISE — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"54ed9d74d02e3c1ad6ba51d81894d625a2533353afdf1a5d5e558271d5db9a31","width":1500,"height":572,"note":"Fig. 1 : Overview of RISE architecture. The input of RISE is a noisy point cloud captured from the real world. A 3D encoder built with sparse convolution is employed to compress th"}},"equibot":{"policy":{"path":"/images/catalog/equibot-policy.webp","original":"https://equi-bot.github.io/images/model_architecture.png","page":"https://arxiv.org/abs/2407.01479","credit":"EquiBot authors","alt":"EquiBot — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"ab4159c7ce7cc47d8c026acbe1af596696cca65ddd9494ea0aeaa091b3cd8402","width":1500,"height":809,"note":"Author architecture / method figure showing observation encoding and action generation."}},"pointvla":{"policy":{"path":"/images/catalog/pointvla-policy.webp","original":"https://arxiv.org/html/2503.07511v1/pointvla_framework.png","page":"https://arxiv.org/abs/2503.07511","credit":"PointVLA authors","alt":"PointVLA — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"c5c111ab296f66204860a1e29a7b37fe73870309f0e293ab43c6fee356a19b3b","width":1500,"height":804,"note":"Figure 2 : Overview of PointVLA framework. Left: The 2D image observation and instruction are processed by the vision-language model. The vanilla action expert remains frozen, whil"}},"geovla":{"policy":{"path":"/images/catalog/geovla-policy.webp","original":"https://linsun449.github.io/GeoVLA/static/images/03framework.png","page":"https://arxiv.org/abs/2508.09071","credit":"GeoVLA authors","alt":"GeoVLA — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"8a8c84060b4cb99e7c9f2981e7e539b503639185e56ecf5d4ac0c7e662e2f8b4","width":1500,"height":520,"note":"Author architecture / method figure showing observation encoding and action generation."}},"bridgevla":{"policy":{"path":"/images/catalog/bridgevla-policy.webp","original":"https://bridgevla.github.io/static/images/network.png","page":"https://arxiv.org/abs/2506.07961","credit":"BridgeVLA authors","alt":"BridgeVLA — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"7cb30080e258902aff11022c0c71341802beecf22e90a2294744816a0dfd741a","width":1500,"height":581,"note":"Author architecture / method figure showing observation encoding and action generation."}},"spatialvla":{"policy":{"path":"/images/catalog/spatialvla-policy.webp","original":"https://arxiv.org/html/2501.15830v5/pipeline.png","page":"https://arxiv.org/abs/2501.15830","credit":"SpatialVLA authors","alt":"SpatialVLA — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"9488b124c07dd070e5766acde9df35071fc9c4d4d1a8fbcb581a30fe7d9457f2","width":1210,"height":345,"note":"Fig. 2 : Overview of SpatialVLA . Given an image observation 𝐨 t \\mathbf{o}_{t} and a task instruction 𝐋 \\mathbf{L} , the model processes the image using Ego3D Position Encoding an"}},"r3d":{"policy":{"path":"/images/catalog/r3d-policy.webp","original":"https://arxiv.org/html/2604.15281v2/pipeline-3_cropped.png","page":"https://arxiv.org/abs/2604.15281","credit":"R3D authors","alt":"R3D — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"07269e83febbe2f5c59f15f0cc0e9f388908941c843a2023c1a21cd7a2b3191b","width":1447,"height":831,"note":"Figure 1: Pipeline architecture : a point cloud encoder encodes the input point clouds and a diffusion transformer decodes actions. The encoder can be pre-trained on 3D segmentatio"}},"maniflow":{"policy":{"path":"/images/catalog/maniflow-policy.webp","original":"https://maniflow-policy.github.io/figures/maniflow_pipeline.jpg","page":"https://arxiv.org/abs/2509.01819","credit":"ManiFlow authors","alt":"ManiFlow — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"53917803dbf34bb4ff2c589e5c5052c5307e19d013c5157e9e292e94d1849104","width":1500,"height":392,"note":"Author architecture / method figure showing observation encoding and action generation."}},"consisvla4d":{"policy":{"path":"/images/catalog/consisvla4d-policy.webp","original":"https://arxiv.org/html/2605.05126v1/fig/pipline.png","page":"https://arxiv.org/abs/2605.05126","credit":"ConsisVLA-4D authors","alt":"ConsisVLA-4D — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"7adaa30e578500e6b579d270ae7124bc5ea76acaa6089d2825b3245964ae2023","width":1500,"height":461,"note":"Author architecture / method figure showing observation encoding and action generation."}},"vga":{"policy":{"path":"/images/catalog/vga-policy.webp","original":"https://arxiv.org/html/2604.12908v2/vggtvla2.png","page":"https://arxiv.org/abs/2604.12908","credit":"Vision-Geometry-Action (VGA) authors","alt":"Vision-Geometry-Action (VGA) — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"c90f0f64e188b18d9ee94b0b1691a21a450c661417466587c162ece362c76c3d","width":1487,"height":813,"note":"Figure 2 . Overview of our VGA model. (a) The left column compares our VGA framework with representative robot learning paradigms.\nVGA differs from them by leveraging a pretrained "}},"serl":{"policy":{"path":"/images/catalog/serl-policy.webp","original":"https://arxiv.org/html/2401.16013v4/images/software.png","page":"https://arxiv.org/abs/2401.16013","credit":"SERL authors","alt":"SERL — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"43106a6b534b79ed1ddc6a666e695cd8e8321c28a4eb38b0a958a87b0f0831e2","width":1500,"height":765,"note":"Figure 2 : Software architecture and real-world robot training example code. SERL  runs three parallel processes, consisting of the actor, which chooses actions, and the learner no"}},"hilserl":{"policy":{"path":"/images/catalog/hilserl-policy.webp","original":"https://arxiv.org/html/2410.21845v3/fig2_1.png","page":"https://arxiv.org/abs/2410.21845","credit":"HIL-SERL authors","alt":"HIL-SERL — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"ff0dc7596322e4ef871c4c8e32215854bf94fd4a64da43d2ce780d5d19e77887","width":1264,"height":770,"note":"Figure 2 : Overview of HIL-SERL. This figure illustrates the architecture of HIL-SERL, which comprises three primary components: the actor process, the learner process, and replay "}},"dppo":{"policy":{"path":"/images/catalog/dppo-policy.webp","original":"https://arxiv.org/html/2409.00588v3/overview-v1.png","page":"https://arxiv.org/abs/2409.00588","credit":"DPPO authors","alt":"DPPO — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"a41832663e9e09aa001f84deb57365165fcb28c468aac078b59ad79421198ce4","width":1480,"height":379,"note":"Author architecture / method figure showing observation encoding and action generation."}},"dsrl":{"policy":{"path":"/images/catalog/dsrl-policy.webp","original":"https://arxiv.org/html/2506.15799v2/paper_figure_pi0.png","page":"https://arxiv.org/abs/2506.15799","credit":"DSRL authors","alt":"DSRL — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"56d12dce7d82569d6a06899762e2a853f39cc92489788f59fa75646c48491a7a","width":1500,"height":501,"note":"Figure 1: Overview of our proposed approach, D iffusion S teering via R einforcement L earning ( Dsrl ). Standard deployment of a BC-trained diffusion policy π dp \\pi_{\\mathrm{dp}}"}},"octo":{"policy":{"path":"/images/catalog/octo-policy.webp","original":"https://arxiv.org/html/2405.12213v2/architecture.png","page":"https://arxiv.org/abs/2405.12213","credit":"Octo authors","alt":"Octo — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"03f34d47fe8965fe9e9340c86ef90f3a3d3359a0b10baf2be4fd12c9533798ca","width":1500,"height":726,"note":"Fig. 1: Model architecture. Left : Octo tokenizes task descriptions (green) and input observations (blue) using a pretrained language model and a lightweight CNN, respectively. Top"}},"openvla":{"policy":{"path":"/images/catalog/openvla-policy.webp","original":"https://arxiv.org/html/2406.09246v3/openvla_model.png","page":"https://arxiv.org/abs/2406.09246","credit":"OpenVLA authors","alt":"OpenVLA — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"037ac1c22b7793d0df916620b065bb13dfdbb357c39c3e7da2f203e33f5bf0ae","width":1500,"height":557,"note":"Figure 2: OpenVLA model architecture. Given an image observation and a language instruction, the model predicts 7-dimensional robot control actions. The architecture consists of th"}},"pi0":{"policy":{"path":"/images/catalog/pi0-policy.webp","original":"https://arxiv.org/html/2410.24164v4/overview.png","page":"https://arxiv.org/abs/2410.24164","credit":"π0 authors","alt":"π0 — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"480cd004bd0aaebb731ead815e0375bda88eb79ef4b54efa5c1875fd2094b2e5","width":1500,"height":419,"note":"Fig. 3: Overview of our framework. We start with a pre-training mixture, which consists of both our own dexterous manipulation datasets and open-source data. We use this mixture to"}},"pi05":{"policy":{"path":"/images/catalog/pi05-policy.webp","original":"https://arxiv.org/html/2504.16054v1/Figure_3.png","page":"https://arxiv.org/abs/2504.16054","credit":"π0.5 authors","alt":"π0.5 — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"7100dfa67a7626ec259857484efb613c3bc4502d32e553ed0017df8127b8ab15","width":1500,"height":624,"note":"Fig. 3 : Model overview. π 0.5 \\pi_{0.5} is trained in two stages. First, a pre-training stage combines all of the different data sources to produce an initial VLA with discrete to"}},"grootn1":{"policy":{"path":"/images/catalog/grootn1-policy.webp","original":"https://arxiv.org/html/2503.14734v2/groot_model_v6.png","page":"https://arxiv.org/abs/2503.14734","credit":"GR00T N1 authors","alt":"GR00T N1 — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"dff8fe9996013c8ea08a28d3d3d4acae503d97ff2c0f9358906b4a8c53040fa0","width":1500,"height":778,"note":"Figure 3 : GR00T N1 Model Architecture. GR00T N1 is trained on a diverse set of embodiments ranging from single-arm robot arms to bimanual humanoid dexterous hands.\nTo deal with di"}},"grootn16":{"policy":{"path":"/images/catalog/grootn16-policy.webp","original":"https://raw.githubusercontent.com/NVIDIA/Isaac-GR00T/n1.6-release/media/model-architecture.png","page":"https://huggingface.co/nvidia/GR00T-N1.6-3B","credit":"NVIDIA","alt":"GR00T N1.6 — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"bbf161c66daac88799b5bc7b5c110429f48d7f96ae315113e2b2e6d1f11c9e54","width":1500,"height":782,"note":"Version-pinned GR00T N1.6 model architecture."}},"grootn17":{"policy":{"path":"/images/catalog/grootn17-policy.webp","original":"https://huggingface.co/nvidia/GR00T-N1.7-3B/resolve/main/model-architecture.png","page":"https://huggingface.co/nvidia/GR00T-N1.7-3B","credit":"NVIDIA","alt":"GR00T N1.7 — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"bbf161c66daac88799b5bc7b5c110429f48d7f96ae315113e2b2e6d1f11c9e54","width":1500,"height":782,"note":"N1.7-specific official model-card architecture."}},"rdt":{"policy":{"path":"/images/catalog/rdt-policy.webp","original":"https://arxiv.org/html/2410.07864v2/framework.png","page":"https://arxiv.org/abs/2410.07864","credit":"RDT-1B authors","alt":"RDT-1B — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"d231a61a3190bc6686f43ad980cdc604f2d43650fae400f085d303eb6f8b1b59","width":1500,"height":740,"note":"Figure 3: RDT framework. Heterogeneous action spaces of various robots are embedded into a unified action space for multi-robot training. Inputs: proprioception 𝒛 t {\\bm{z}}_{t} , "}},"rdt2":{"policy":{"path":"/images/catalog/rdt2-policy.webp","original":"https://arxiv.org/html/2602.03310v1/pipeline.png","page":"https://arxiv.org/abs/2602.03310","credit":"RDT2 authors","alt":"RDT2 — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"f50161b3836828aa3e61c56b60361286715e20e184e49f913f679d5a3b81c0c3","width":1500,"height":284,"note":"Figure 2 : A three-stage pipeline for training RDT2. In Stage 1, we pre-train a 7B VLM backbone with discretized action data for vision-language reasoning capabilities. Then, in St"}},"smolvla":{"policy":{"path":"/images/catalog/smolvla-policy.webp","original":"https://arxiv.org/html/2506.01844v1/SmolVLA.png","page":"https://arxiv.org/abs/2506.01844","credit":"SmolVLA authors","alt":"SmolVLA — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"89c2504c2cec53f3769c8c54c0be3d0599a562c5e482d36d15228aa1dccf5947","width":1200,"height":675,"note":"Author architecture / method figure showing observation encoding and action generation."}},"cosmospolicy":{"policy":{"path":"/images/catalog/cosmospolicy-policy.webp","original":"https://arxiv.org/html/2601.16163v1/fig/cosmos_policy_diffusion_sequence_v2_main_version.001.jpeg","page":"https://arxiv.org/abs/2601.16163","credit":"Cosmos Policy authors","alt":"Cosmos Policy — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"f02ed92c1e6ef9197d511cb680c26a2235046439831f5a52ef9c919dc7a4675e","width":1500,"height":598,"note":"Figure 2: The latent diffusion sequence of Cosmos Policy. We illustrate latent frame injection —the primary mechanism for adapting the pretrained Cosmos-Predict2 into a policy that"}},"dreamzero":{"policy":{"path":"/images/catalog/dreamzero-policy.webp","original":"https://arxiv.org/html/2602.15922v1/dreamzero_model.png","page":"https://arxiv.org/abs/2602.15922","credit":"DreamZero authors","alt":"DreamZero — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"4483f06722a68bd5eb8909549900c7104e9ce36d4f5efeae64f53f8061c3c80b","width":1500,"height":494,"note":"Figure 4 : Model Architecture of DreamZero . The model takes three inputs: visual context (encoded via a VAE), language instructions (via a text encoder), and proprioceptive state "}},"act3d":{"policy":{"path":"/images/catalog/act3d-policy.webp","original":"https://arxiv.org/html/2306.17817v2/final_figures/main.png","page":"https://arxiv.org/abs/2306.17817","credit":"Act3D authors","alt":"Act3D — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"8def8d732cbcba3fce373d40af64d17e1bffefc77ef56f862a5ce491f4e76252","width":1500,"height":456,"note":"Author architecture / method figure showing observation encoding and action generation."}},"gendp":{"policy":{"path":"/images/catalog/gendp-policy.webp","original":"https://robopil.github.io/GenDP/media/figures/method.png","page":"https://robopil.github.io/GenDP/","credit":"Wang et al.","alt":"GenDP — pipeline","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"8dea7259e49d1669a4d719f91bc56c06999315a93722216fcdc335f9cdf3e387","width":1500,"height":1025,"note":"Measured RGB-D → descriptor/semantic fields → diffusion actions, Figure2."}},"rise2":{"policy":{"path":"/images/catalog/rise2-policy.webp","original":"https://arxiv.org/html/2503.03081v3/policy.png","page":"https://arxiv.org/abs/2503.03081","credit":"Fang et al.","alt":"RISE-2 — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"48a87c715647724d999a3f9305d28f4632ba3f2553b6f5c047f3e4ac99ef273c","width":1500,"height":629,"note":"Figure3 RISE-2 policy architecture, distinct from AirExo hardware diagram."}},"tabero":{"benchmark":{"path":"/images/catalog/tabero-benchmark.webp","original":"https://arxiv.org/html/2605.27886v1/figs/mul_task_new.png","page":"https://arxiv.org/html/2605.27886","credit":"Tabero authors","alt":"Tabero / Tabero-VTLA — task overview","kind":"task-overview","reviewed":"2026-10-17","sourceSha256":"e24259a468257647181a3b493bd5ef2a3a8a07d4ea0aa34e19d158e3e409e868","width":1294,"height":440,"note":"Migrated LIBERO environment families;broader than the nine-task policy subset."},"policy":{"path":"/images/catalog/tabero-policy.webp","original":"https://arxiv.org/html/2605.27886v1/part2.png","page":"https://arxiv.org/html/2605.27886","credit":"Tabero authors","alt":"Tabero / Tabero-VTLA — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"fa380d3456f5bb0235c0af3277c5ba50eb03a28a52aea2bb7b8246062c1ba673","width":1145,"height":1059,"note":"Policy force/position architecture and hybrid feedback controller."}},"vtdexmanip":{"benchmark":{"path":"/images/catalog/vtdexmanip-benchmark.webp","original":"https://lqts.github.io/VTDexManip/resources/benchmark1.jpg","page":"https://lqts.github.io/VTDexManip/","credit":"VTDexManip authors","alt":"VTDexManip — task overview","kind":"task-overview","reviewed":"2026-10-17","sourceSha256":"871306dea4239725e886811deca6a1a82acf209c35a8ff8c738c5e1c5196949f","width":1500,"height":670,"note":"Author composite of all six benchmark tasks,18 representation variants and PPO policy pipeline."},"policy":{"path":"/images/catalog/vtdexmanip-policy.webp","original":"https://proceedings.iclr.cc/paper_files/paper/2025/file/e19b6f65791e350347bcff8a3955cb5b-Paper-Conference.pdf#page=20","page":"https://proceedings.iclr.cc/paper_files/paper/2025/file/e19b6f65791e350347bcff8a3955cb5b-Paper-Conference.pdf","credit":"VTDexManip authors","alt":"VTDexManip — VT-JointPretrain architecture with joint vision-tactile masking, Transformer encoder/decoder and modality reconstruction heads","kind":"architecture","reviewed":"2026-10-17","width":1801,"height":872,"note":"Actual Figure9, PDF page20. Cropped to the complete VT-JointPretrain method architecture, including its token legend; crop rectangle[124,77,490,254]PDFpoints. This is the benchmark's joint vision-tactile representation pretraining architecture, not the downstream PPO action head. The benchmark card retains its separate full task/platform overview image. Rendered from the primary ICLR2025 proceedings PDF and visually inspected.","sourceSha256":"2b7cfb1052430c62260782a8be21ee1366e8983924a649d6361f619bf06161ad"}},"colosseum-v2":{"benchmark":{"path":"/images/catalog/colosseum-v2-benchmark.webp","original":"https://colosseum-v2.github.io/content/ColosseumV2_hero.jpg","page":"https://colosseum-v2.github.io/","credit":"Colosseum V2 authors","alt":"Colosseum V2 — task overview","kind":"task-overview","reviewed":"2026-10-17","sourceSha256":"2abf9387174f513346689c05fe650b895147309221fbc3b8c9b195d16fd0ad67","width":1500,"height":672,"note":"Official 28-task and perturbation overview."}},"vla-arena":{"benchmark":{"path":"/images/catalog/vla-arena-benchmark.webp","original":"https://vla-arena.github.io/figures/structure.png","page":"https://vla-arena.github.io/","credit":"VLA-Arena authors","alt":"VLA-Arena — taxonomy","kind":"taxonomy","reviewed":"2026-10-17","sourceSha256":"0668e369454639ecd30d44edde68ae8a4746ab5a6cf33a64a22408c4e8c6eab7","width":1500,"height":920,"note":"Four capability dimensions, task levels and independent perturbation structure."}},"prism":{"benchmark":{"path":"/images/catalog/prism-benchmark.webp","original":"https://tengbo-yu.github.io/PRISM/assets/images/overview_v2.png","page":"https://tengbo-yu.github.io/PRISM/","credit":"PRISM authors","alt":"PRISM — dataset and capture overview","kind":"dataset-overview","reviewed":"2026-10-17","sourceSha256":"af609879228677d8c59b5ac5cb293c156ad3988a8e9bb31ab536a04d34ccae51","width":1500,"height":666,"note":"Industrial tasks, robot platforms and sensing breadth."}},"reassemble":{"benchmark":{"path":"/images/catalog/reassemble-benchmark.webp","original":"https://tuwien-asl.github.io/REASSEMBLE_page/figures/teaser.png","page":"https://tuwien-asl.github.io/REASSEMBLE_page/","credit":"REASSEMBLE authors","alt":"REASSEMBLE — dataset and capture overview","kind":"dataset-overview","reviewed":"2026-10-17","sourceSha256":"4767920c7c5cffebc67cba064701164fe37555487edf8141b51ee291c885ed03","width":1500,"height":844,"note":"Task board, action types, sensing and dataset overview."}},"tacverse":{"benchmark":{"path":"/images/catalog/tacverse-benchmark.webp","original":"https://arxiv.org/pdf/2606.25877#page=4","page":"https://arxiv.org/abs/2606.25877","credit":"TacVerse authors","alt":"TacVerse — dataset and capture overview","kind":"dataset-overview","reviewed":"2026-10-17","width":1601,"height":722,"note":"Figure 2. Shape classification, grating classification and force regression, with example tactile observations and the force-measurement apparatus. Rendered from the primary paper PDF (page 4), cropped to the figure, and visually reviewed.","sourceSha256":"86f99234f83187a13a5849d57b4fbc1934724f554065a199e25ec051cd0c28b3"}},"dexvla":{"policy":{"path":"/images/catalog/dexvla-policy.webp","original":"https://arxiv.org/html/2502.05855v3/framework2.png","page":"https://arxiv.org/abs/2502.05855","credit":"Wen et al.","alt":"DexVLA — architecture","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"a4f6464405586ae14ed45096ff17aad6eb9fb2cadc18759dddde61591493a86e","width":1500,"height":472,"note":"Figure2: VLM/diffusion expert and three-stage embodiment curriculum."}},"univla":{"policy":{"path":"/images/catalog/univla-policy.webp","original":"https://arxiv.org/pdf/2505.06111#page=5","page":"https://arxiv.org/abs/2505.06111","credit":"Bu et al. / OpenDriveLab","alt":"Figure 3: Generalist policy architecture with latent action tokens, autoregressive VLM and task-specific action decoder.","kind":"architecture","reviewed":"2026-10-17","width":1548,"height":840,"note":"Re-rendered actual paper vector figure at6x for legible zoom; replaced lower-resolution web image. Surrounding paper text/caption excluded; visually reviewed.","sourceSha256":"1b8551db322a0e31514601d9d84879f85a5c391e4911e40752ea9df7567196f5"}},"pifast":{"policy":{"path":"/images/catalog/pifast-policy.webp","original":"https://arxiv.org/html/2501.09747v1/teaser.png","page":"https://arxiv.org/abs/2501.09747","credit":"Pertsch et al.","alt":"π0-FAST / FAST — pipeline","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"be38f8035d6319c7cda945bf9a140c0a4b4d972254ca941218845d11289889d4","width":1067,"height":1005,"note":"Figure2: autoregressive robot control with FAST action compression."}},"xvla":{"policy":{"path":"/images/catalog/xvla-policy.webp","original":"https://arxiv.org/pdf/2510.10274#page=22","page":"https://arxiv.org/abs/2510.10274","credit":"Zheng et al.","alt":"Figure 10: Shared Transformer, embodiment-specific soft prompts and input/output projection libraries.","kind":"architecture","reviewed":"2026-10-17","width":1926,"height":1830,"note":"Re-rendered actual paper vector figure at6x for legible zoom; replaced lower-resolution web image. Surrounding paper text/caption excluded; visually reviewed.","sourceSha256":"483c47a651b44456f7f5a71b8d0e586df2e32c034f188dfb0653f3f6cc2d8aa7"}},"sparsh":{"benchmark":{"path":"/images/catalog/sparsh-benchmark.webp","original":"https://sparsh-ssl.github.io/media/figures/teaser2.png","page":"https://sparsh-ssl.github.io/","credit":"TacBench authors","alt":"TacBench — task overview","kind":"task-overview","reviewed":"2026-10-17","sourceSha256":"22cfa233b206f546faf6935e781590ddefcd10d0f3867e8dc28931c6ab3bf280","width":1500,"height":619,"note":"Overview of all six benchmark problems and touch representations."},"policy":{"path":"/assets/figures/sensing-sparsh.webp","original":"https://arxiv.org/html/2410.24090v1/ssl2.png","page":"https://arxiv.org/html/2410.24090#S3.F2","credit":"sparsh authors; original paper figure","alt":"Sparsh compares masked reconstruction, self-distillation and predictive embedding pretraining for tactile encoders.","kind":"architecture","reviewed":"2026-10-17","width":1320,"height":631,"note":"Original S3.F2 from the primary paper; scientific content retained.","sourceSha256":"81160674bf2bf7323bf86312b5fbd57aa5a0bd09483f45eb1960a64184c7bcd1"}},"bridgevla-plus":{"policy":{"path":"/images/catalog/bridgevla-plus-policy.webp","original":"https://bridgevla-plus.github.io/static/images/paper/architecture.png","page":"https://bridgevla-plus.github.io/","credit":"BridgeVLA++ authors","alt":"BridgeVLA++ — Fig2:grounding/action pipeline with temporal and spatial memory injection.","kind":"architecture","reviewed":"2026-10-17","width":1500,"height":814,"note":"Officialdirectarchitecturefigure;optimizedlocalcopy. Visually reviewed."}},"spatialvam":{"policy":{"path":"/images/catalog/spatialvam-policy.webp","original":"https://spatialvam.github.io/static/images/Method.png","page":"https://spatialvam.github.io/home_page.html","credit":"SpatialVAM authors","alt":"SpatialVAM — Fig2:pipeline,view-awarevideoTransformer andaction decoder.","kind":"architecture","reviewed":"2026-10-17","width":1500,"height":979,"note":"Officialdirectarchitecturefigure;optimizedlocalcopy. Visually reviewed."}},"sa-wam":{"policy":{"path":"/images/catalog/sa-wam-policy.webp","original":"https://jlopetegui98.github.io/assets/img/sawam/method.png","page":"https://jlopetegui98.github.io/projects/sa_wam.html","credit":"SA-WAM authors","alt":"SA-WAM — Fig2:observedRGB-D/state/languageconditioning andjointlatentaction/futuregeneration.","kind":"architecture","reviewed":"2026-10-17","width":1500,"height":626,"note":"Officialdirectarchitecturefigure;optimizedlocalcopy. Visually reviewed."}},"manifeel":{"benchmark":{"path":"/images/catalog/manifeel-benchmark.webp","original":"https://arxiv.org/pdf/2505.18472#page=3","page":"https://arxiv.org/abs/2505.18472","credit":"ManiFeel authors","alt":"ManiFeel — simulation and real-world task overview","kind":"task-overview","reviewed":"2026-10-17","width":1601,"height":1605,"note":"Figure1, PDF page3. Eight named simulation panels and four real-world panels are shown. The paper text counts nine simulation setups because separate PegInsertion is described in text but has no separate panel here. Rendered from the primary paper PDF, cropped to the original figure, and visually reviewed.","sourceSha256":"f9bbb98529baf333276c989b912a5f887a3ec4ba6156d6debd2601882664cc12"}},"contactworld":{"benchmark":{"path":"/images/catalog/contactworld-benchmark.webp","original":"https://arxiv.org/pdf/2606.13877#page=3","page":"https://arxiv.org/abs/2606.13877","credit":"ContactWorld authors","alt":"ContactWorld — Twelve task variants grouped into insertion, screwing, disassembly, and exploration.","kind":"task-overview","reviewed":"2026-10-17","width":1601,"height":877,"note":"Figure 2. Twelve task variants grouped into insertion, screwing, disassembly, and exploration. Rendered from the primary paper PDF (page 3), cropped to the figure, and visually reviewed.","sourceSha256":"a57be63a903b68d2cee9f11245d6ab7a0ec38731a7940e16c4d2d61ff1b6df8c"},"policy":{"path":"/images/catalog/contactworld-policy.webp","original":"https://contact-world.github.io/assets/architecture.svg","page":"https://contact-world.github.io/","credit":"ContactWorld authors","alt":"ContactWorld — pipeline","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"848f9c0943ec241f504255468066510adf0c8cf3d491ac6720631ad7de863319","width":1400,"height":736,"note":"Official training and planning architecture; distinct from the benchmark task overview."}},"taco-sensor-benchmark":{"benchmark":{"path":"/images/catalog/taco-sensor-benchmark-benchmark.webp","original":"https://arxiv.org/pdf/2605.21976#page=6","page":"https://arxiv.org/abs/2605.21976","credit":"TacO authors","alt":"TacO — Reorientation, unknown-mass pick-and-place and insertion tasks, plus repeatability test apparatus.","kind":"task-overview","reviewed":"2026-10-17","width":1601,"height":607,"note":"Figure 3. Reorientation, unknown-mass pick-and-place and insertion tasks, plus repeatability test apparatus. Rendered from the primary paper PDF (page 6), cropped to the figure, and visually reviewed.","sourceSha256":"d7077a4591adc2ead90b9616833afaac6f4fb2c42db58fbe699e041ad1ba0aac"}},"tactilegym2":{"benchmark":{"path":"/images/catalog/tactilegym2-benchmark.webp","original":"https://arxiv.org/pdf/2207.10763#page=5","page":"https://arxiv.org/abs/2207.10763","credit":"Tactile Gym 2.0 authors","alt":"Tactile Gym 2.0 — Physical stimuli for edge-following, surface-following and object-pushing; stimuli rather than robot scenes.","kind":"task-overview","reviewed":"2026-10-17","width":1601,"height":228,"note":"Figure 3. Physical stimuli for edge-following, surface-following and object-pushing; stimuli rather than robot scenes. Rendered from the primary paper PDF (page 5), cropped to the figure, and visually reviewed.","sourceSha256":"c92c7b5719692529c420dfe73714a1638a0ebc0cd27e4ab0fb0a1e5e745dee3c"}},"maniskill-vitac-2025":{"benchmark":{"path":"/images/catalog/maniskill-vitac-2025-benchmark.webp","original":"https://arxiv.org/pdf/2411.12503#page=3","page":"https://arxiv.org/abs/2411.12503","credit":"ManiSkill-ViTac 2025 authors","alt":"ManiSkill-ViTac 2025 — The real and simulated challenge insertion platform; this is not an all-track taxonomy.","kind":"task-overview","reviewed":"2026-10-17","width":1601,"height":928,"note":"Figure 1. The real and simulated challenge insertion platform; this is not an all-track taxonomy. Rendered from the primary paper PDF (page 3), cropped to the figure, and visually reviewed.","sourceSha256":"227c618dc588b8179f16cac10ca75f655a2a17fec04b1a99c9de9f42159cabc7"}},"peg-in-bench":{"benchmark":{"path":"/images/catalog/peg-in-bench-benchmark.webp","original":"https://arxiv.org/pdf/2609.00906#page=4","page":"https://arxiv.org/abs/2609.00906","credit":"Peg-in-Bench authors","alt":"Peg-in-Bench — Printed benchmark bases, five peg shapes, multiple clearances, fixing parts and peg holder.","kind":"task-overview","reviewed":"2026-10-17","width":1601,"height":805,"note":"Figure 2. Printed benchmark bases, five peg shapes, multiple clearances, fixing parts and peg holder. Rendered from the primary paper PDF (page 4), cropped to the figure, and visually reviewed.","sourceSha256":"74a2c98dcf4e8953e9a39bd8dc49416325b0b3266b94cf356642bca7ba9ae16a"}},"labit":{"benchmark":{"path":"/images/catalog/labit-benchmark.webp","original":"https://arxiv.org/pdf/2610.16398#page=4","page":"https://arxiv.org/abs/2610.16398","credit":"LABIT authors","alt":"LABIT — All fifteen assembly operations in the published task sequence.","kind":"task-overview","reviewed":"2026-10-17","width":1601,"height":708,"note":"Figure 4. All fifteen assembly operations in the published task sequence. Rendered from the primary paper PDF (page 4), cropped to the figure, and visually reviewed.","sourceSha256":"15e953dbf9c36a4f1ca4ddd6532fb7ffd8e7cecac7289c05cc848a54509d50fe"}},"ramp":{"benchmark":{"path":"/images/catalog/ramp-benchmark.webp","original":"https://arxiv.org/pdf/2305.09644#page=4","page":"https://arxiv.org/abs/2305.09644","credit":"RAMP authors","alt":"RAMP — Assembly configurations grouped by easy, medium and hard difficulty.","kind":"task-overview","reviewed":"2026-10-17","width":1601,"height":837,"note":"Figure 3. Assembly configurations grouped by easy, medium and hard difficulty. Rendered from the primary paper PDF (page 4), cropped to the figure, and visually reviewed.","sourceSha256":"8d44e6ec660a7de604c6c5a85857c07562d4685da8f673d37715e3b037e9f4b2"}},"manipulationnet":{"benchmark":{"path":"/images/catalog/manipulationnet-benchmark.webp","original":"https://arxiv.org/pdf/2603.04363#page=17","page":"https://arxiv.org/abs/2603.04363","credit":"ManipulationNet authors","alt":"ManipulationNet — Peg-in-hole tolerance board and cable management board; two benchmark families, not the full platform scope.","kind":"task-overview","reviewed":"2026-10-17","width":1601,"height":843,"note":"Figure 4. Peg-in-hole tolerance board and cable management board; two benchmark families, not the full platform scope. Rendered from the primary paper PDF (page 17), cropped to the figure, and visually reviewed.","sourceSha256":"90b885dae1d53df7e6c242281dd54904dac40a01dc09af3a839d437e30abd647"}},"industreal":{"benchmark":{"path":"/images/catalog/industreal-benchmark.webp","original":"https://arxiv.org/pdf/2305.17110#page=3","page":"https://arxiv.org/abs/2305.17110","credit":"IndustReal authors","alt":"IndustReal — Three assembly types and pick, place, insert phases.","kind":"task-overview","reviewed":"2026-10-17","width":1601,"height":1104,"note":"Figure 2. Three assembly types and pick, place, insert phases. 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video/action towers, and temporal alignment.","reviewed":"2026-10-17"}},"wla":{"policy":{"path":"/images/catalog/wla-policy.webp","original":"https://arxiv.org/html/2606.05979v1/architecture.png","page":"https://arxiv.org/html/2606.05979v1","credit":"Yang et al. / WLA authors","alt":"WLA-0 / WLA policy architecture","kind":"architecture","reviewed":"2026-10-17","width":2039,"height":767,"note":"Figure 2: AR backbone, world/action experts and optional value-ranked test-time scaling."}},"vpp2":{"policy":{"path":"/images/catalog/vpp2-policy.webp","original":"https://arxiv.org/html/2610.10270v3/fig4_3.png","page":"https://arxiv.org/html/2610.10270v3","credit":"Guo et al. / VPP2 authors","alt":"Video Prediction Policy 2 (VPP2) policy architecture","kind":"pipeline","reviewed":"2026-10-17","width":1756,"height":614,"note":"Figure 4: three-stage video/action training pipeline; architecture rather than leaderboard plot."}},"a1":{"policy":{"path":"/images/catalog/a1-policy.webp","original":"https://arxiv.org/html/2604.05672v3/framework.png","page":"https://arxiv.org/html/2604.05672v3","credit":"A1 authors","alt":"A1 policy architecture","kind":"architecture","reviewed":"2026-10-17","width":2038,"height":723,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"being-h05":{"policy":{"path":"/images/catalog/being-h05-policy.webp","original":"https://arxiv.org/html/2601.12993v1/beingh05_arch_nar.png","page":"https://arxiv.org/html/2601.12993v1","credit":"Being-H0.5 authors","alt":"Being-H0.5 policy architecture","kind":"architecture","reviewed":"2026-10-17","width":1979,"height":1099,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"dm0":{"policy":{"path":"/images/catalog/dm0-policy.webp","original":"https://arxiv.org/html/2602.14974v1/figs/dm0_arch_v2.png","page":"https://arxiv.org/html/2602.14974v1","credit":"DM0 authors","alt":"DM0 policy architecture","kind":"architecture","reviewed":"2026-10-17","width":2200,"height":726,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"eventvla":{"policy":{"path":"/images/catalog/eventvla-policy.webp","original":"https://arxiv.org/html/2606.20092v3/framework.png","page":"https://arxiv.org/html/2606.20092v3","credit":"EventVLA authors","alt":"EventVLA policy architecture","kind":"architecture","reviewed":"2026-10-17","width":1299,"height":993,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"galaxea-g0":{"policy":{"path":"/images/catalog/galaxea-g0-policy.webp","original":"https://arxiv.org/html/2509.00576v1/vla_model.png","page":"https://arxiv.org/html/2509.00576v1","credit":"Galaxea G0 authors","alt":"Galaxea G0 policy architecture","kind":"architecture","reviewed":"2026-10-17","width":2040,"height":1134,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"go1":{"policy":{"path":"/images/catalog/go1-policy.webp","original":"https://arxiv.org/html/2503.06669v4/policy_architecture_v5.png","page":"https://arxiv.org/html/2503.06669v4","credit":"GO-1 / AgiBot World authors","alt":"GO-1 / AgiBot World policy architecture","kind":"architecture","reviewed":"2026-10-17","width":964,"height":335,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"internvla-a1":{"policy":{"path":"/images/catalog/internvla-a1-policy.webp","original":"https://arxiv.org/html/2601.02456v2/figure2_method_InternVLA-A1.png","page":"https://arxiv.org/html/2601.02456v2","credit":"InternVLA-A1 authors","alt":"InternVLA-A1 policy architecture","kind":"architecture","reviewed":"2026-10-17","width":1549,"height":894,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"internvla-a15":{"policy":{"path":"/images/catalog/internvla-a15-policy.webp","original":"https://arxiv.org/html/2607.04988v1/Model-A1.5.png","page":"https://arxiv.org/html/2607.04988v1","credit":"InternVLA-A1.5 authors","alt":"InternVLA-A1.5 policy architecture","kind":"architecture","reviewed":"2026-10-17","width":1066,"height":339,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"lda1b":{"policy":{"path":"/images/catalog/lda1b-policy.webp","original":"https://arxiv.org/html/2602.12215v2/LDA_pipeline.png","page":"https://arxiv.org/html/2602.12215v2","credit":"LDA-1B authors","alt":"LDA-1B policy architecture","kind":"architecture","reviewed":"2026-10-17","width":1772,"height":685,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"lingbot-vla":{"policy":{"path":"/images/catalog/lingbot-vla-policy.webp","original":"https://arxiv.org/html/2601.18692v4/Teaser.png","page":"https://arxiv.org/html/2601.18692v4","credit":"LingBot-VLA authors","alt":"LingBot-VLA policy architecture","kind":"architecture","reviewed":"2026-10-17","width":671,"height":325,"note":"Cropped central architecture panel from Figure1:RGB/text/depth queries and flow-action expert; excludes deployment gallery and score plots."}},"abot-m0":{"policy":{"path":"/images/catalog/abot-m0-policy.webp","original":"https://arxiv.org/html/2602.11236v2/model.png","page":"https://arxiv.org/html/2602.11236v2","credit":"ABot-M0 authors","alt":"ABot-M0 policy architecture","kind":"architecture","reviewed":"2026-10-17","width":1844,"height":709,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"dexora":{"policy":{"path":"/images/catalog/dexora-policy.webp","original":"https://arxiv.org/html/2605.18722v1/method.png","page":"https://arxiv.org/html/2605.18722v1","credit":"Dexora authors","alt":"Dexora policy architecture","kind":"architecture","reviewed":"2026-10-17","width":1273,"height":486,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"h-rdt":{"policy":{"path":"/images/catalog/h-rdt-policy.webp","original":"https://arxiv.org/html/2507.23523v2/figure2.png","page":"https://arxiv.org/html/2507.23523v2","credit":"H-RDT authors","alt":"H-RDT policy architecture","kind":"architecture","reviewed":"2026-10-17","width":724,"height":509,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"hy-embodied05":{"policy":{"path":"/images/catalog/hy-embodied05-policy.webp","original":"https://arxiv.org/html/2606.14409v2/pipeline.png","page":"https://arxiv.org/html/2606.14409v2","credit":"Hy-Embodied-0.5-VLA authors","alt":"Hy-Embodied-0.5-VLA policy architecture","kind":"architecture","reviewed":"2026-10-17","width":2033,"height":659,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"molmoact2":{"policy":{"path":"/images/catalog/molmoact2-policy.webp","original":"https://arxiv.org/html/2605.02881v2/MAF3.png","page":"https://arxiv.org/html/2605.02881v2","credit":"MolmoAct2 authors","alt":"MolmoAct2 policy architecture","kind":"architecture","reviewed":"2026-10-17","width":2024,"height":843,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"tinyvla":{"policy":{"path":"/images/catalog/tinyvla-policy.webp","original":"https://arxiv.org/html/2409.12514v5/tinyvla_framework.png","page":"https://arxiv.org/html/2409.12514v5","credit":"TinyVLA authors","alt":"TinyVLA policy architecture","kind":"architecture","reviewed":"2026-10-17","width":985,"height":524,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"xiaomi0":{"policy":{"path":"/images/catalog/xiaomi0-policy.webp","original":"https://arxiv.org/html/2602.12684v2/fig3.png","page":"https://arxiv.org/html/2602.12684v2","credit":"Xiaomi-Robotics-0 authors","alt":"Xiaomi-Robotics-0 policy architecture","kind":"architecture","reviewed":"2026-10-17","width":970,"height":460,"note":"Figure3:original model architecture and staged training; replaced data-mixture Figure2 after visual review."}},"xiaomi1":{"policy":{"path":"/images/catalog/xiaomi1-policy.webp","original":"https://arxiv.org/html/2607.15330v2/model.png","page":"https://arxiv.org/html/2607.15330v2","credit":"Xiaomi-Robotics-1 authors","alt":"Xiaomi-Robotics-1 policy architecture","kind":"architecture","reviewed":"2026-10-17","width":2047,"height":779,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"starvla":{"policy":{"path":"/images/catalog/starvla-policy.webp","original":"https://arxiv.org/html/2604.05014v1/starvla_dataflow.png","page":"https://arxiv.org/html/2604.05014v1","credit":"StarVLA codebase authors","alt":"StarVLA codebase policy architecture","kind":"architecture","reviewed":"2026-10-17","width":1297,"height":645,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"starvla-alpha":{"policy":{"path":"/images/catalog/starvla-alpha-policy.webp","original":"https://arxiv.org/html/2604.11757v2/method.png","page":"https://arxiv.org/html/2604.11757v2","credit":"StarVLA-α authors","alt":"StarVLA-α policy architecture","kind":"architecture","reviewed":"2026-10-17","width":2048,"height":726,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"evo1":{"policy":{"path":"/images/catalog/evo1-policy.webp","original":"https://arxiv.org/html/2511.04555v2/evo1_architecture_v7.png","page":"https://arxiv.org/html/2511.04555v2","credit":"Evo-1 authors","alt":"Evo-1 policy architecture","kind":"architecture","reviewed":"2026-10-17","width":1544,"height":904,"note":"Cropped method portion of original Evo-1 architecture; excludes right-side benchmark result insets."}},"simplememvla":{"policy":{"path":"/images/catalog/simplememvla-policy.webp","original":"https://arxiv.org/html/2609.05533v2/fig_arch_stream.png","page":"https://arxiv.org/html/2609.05533v2","credit":"SimpleMemVLA authors","alt":"SimpleMemVLA policy architecture","kind":"architecture","reviewed":"2026-10-17","width":1457,"height":827,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"egovla":{"policy":{"path":"/images/catalog/egovla-policy.webp","original":"https://arxiv.org/html/2507.12440v3/VLM-Pipeline-V13-Proprio.png","page":"https://arxiv.org/html/2507.12440v3","credit":"EgoVLA authors","alt":"EgoVLA policy architecture","kind":"architecture","reviewed":"2026-10-17","width":2008,"height":719,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"mopa":{"policy":{"path":"/images/catalog/mopa-policy.webp","original":"https://arxiv.org/html/2609.12081v2/main.png","page":"https://arxiv.org/html/2609.12081v2","credit":"MoPA authors","alt":"MoPA policy architecture","kind":"architecture","reviewed":"2026-10-17","width":1209,"height":580,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"kinrt":{"policy":{"path":"/images/catalog/kinrt-policy.webp","original":"https://arxiv.org/html/2607.26807v2/thirteen.jpg","page":"https://arxiv.org/html/2607.26807v2","credit":"KinRT authors","alt":"KinRT policy architecture","kind":"architecture","reviewed":"2026-10-17","width":1681,"height":657,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"fast-vla":{"policy":{"path":"/images/catalog/fast-vla-policy.webp","original":"https://arxiv.org/html/2610.21389v1/fastvla_arch_v12_sage.png","page":"https://arxiv.org/html/2610.21389v1","credit":"Fast-VLA authors","alt":"Fast-VLA policy architecture","kind":"architecture","reviewed":"2026-10-17","width":2031,"height":839,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"memoryvla":{"policy":{"path":"/images/catalog/memoryvla-policy.webp","original":"https://arxiv.org/html/2508.19236v2/fig_2_method.png","page":"https://arxiv.org/html/2508.19236v2","credit":"MemoryVLA authors","alt":"MemoryVLA policy architecture","kind":"architecture","reviewed":"2026-10-17","width":1347,"height":485,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"molmoact":{"policy":{"path":"/images/catalog/molmoact-policy.webp","original":"https://arxiv.org/html/2508.07917v4/figures/final/fig2_model_and_training.png","page":"https://arxiv.org/html/2508.07917v4","credit":"MolmoAct authors","alt":"MolmoAct policy architecture","kind":"architecture","reviewed":"2026-10-17","width":2156,"height":783,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"rmbench":{"policy":{"path":"/images/catalog/rmbench-policy.webp","original":"https://arxiv.org/html/2603.01229v4/mem0_policy.png","page":"https://arxiv.org/html/2603.01229v4","credit":"RMBench / Mem-0 authors","alt":"RMBench / Mem-0 policy architecture","kind":"architecture","reviewed":"2026-10-17","width":1557,"height":860,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."},"benchmark":{"path":"/images/catalog/rmbench-benchmark.webp","original":"https://arxiv.org/html/2603.01229v4/benchmark.png","page":"https://arxiv.org/html/2603.01229v4","credit":"RMBench authors","alt":"RMBench / Mem-0 task overview","kind":"task-overview","reviewed":"2026-10-17","width":1695,"height":752,"note":"Figure 1 presents all 9memory-dependent task families and execution steps; use this in benchmark context."}},"dm05":{"policy":{"path":"/images/catalog/dm05-policy.webp","original":"https://www.dexmal.com/blog/dm0.5/dm05-model-architecture.png","page":"https://www.dexmal.com/blog/dm0.5","credit":"Dexmal Team","alt":"DM0.5 / OpenDM policy architecture","kind":"architecture","reviewed":"2026-10-17","width":2012,"height":938,"note":"Original model architecture diagram from official technical blog."}},"galaxea-g05":{"policy":{"path":"/images/catalog/galaxea-g05-policy.webp","original":"https://opengalaxea.github.io/G05/Galaxea_G0_5.pdf","page":"https://opengalaxea.github.io/G05/Galaxea_G0_5.pdf#page=1","credit":"Galaxea Team","alt":"Galaxea G0.5 autoregressive reasoning/action pipeline","kind":"architecture","reviewed":"2026-10-17","width":969,"height":471,"note":"Figure1 top-left method panel,PDF page1,RGB/state→autoregressive VLM→reasoning/action codes→ActionCodec; score plots and text excluded."}},"memoryvla-plusplus":{"policy":{"path":"/images/catalog/memoryvla-plusplus-policy.webp","original":"https://arxiv.org/html/2606.09827v1/fig_overall.png","page":"https://arxiv.org/html/2606.09827v1","credit":"MemoryVLA++ authors","alt":"MemoryVLA++ policy architecture","kind":"architecture","reviewed":"2026-10-17","width":1501,"height":602,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"abot-m05":{"policy":{"path":"/images/catalog/abot-m05-policy.webp","original":"https://arxiv.org/html/2607.00678v2/model.png","page":"https://arxiv.org/html/2607.00678v2","credit":"ABot-M0.5 authors","alt":"ABot-M0.5 policy architecture","kind":"architecture","reviewed":"2026-10-17","width":1162,"height":572,"note":"Original method architecture / policy pipeline, selected from the primary manuscript."}},"vlact":{"policy":{"path":"/images/catalog/vlact-policy.webp","original":"https://raw.githubusercontent.com/starVLA/VLAct/main/assets/vlact/fig3_method.png","page":"https://arxiv.org/html/2608.27550","credit":"VLAct authors","alt":"VLAct representation-centric policy pretraining and adaptation","kind":"pipeline","reviewed":"2026-10-17","width":1769,"height":697,"note":"Figure3 from official model repository:pretraining and fine-tuning pipeline."}},"embodiedevo":{"policy":{"path":"/images/catalog/embodiedevo-policy.webp","original":"https://arxiv.org/html/2610.18867v2/Figures/embodied-rsi-overview.png","page":"https://arxiv.org/abs/2610.18867","credit":"EmbodiedEvo authors","alt":"Figure 1: A coding agent develops executable robot-control programs and reusable Skills and Lessons through Playground interaction.","kind":"pipeline","reviewed":"2026-10-17","width":1599,"height":742,"note":"Figure 1: A coding agent develops executable robot-control programs and reusable Skills and Lessons through Playground interaction."}},"roboicl":{"policy":{"path":"/images/catalog/roboicl-policy.webp","original":"https://arxiv.org/html/2609.34261v1/figures/method.png","page":"https://arxiv.org/abs/2609.34261","credit":"RoboICL authors","alt":"Figure 2: A frozen VLM controls robots using a shared observation–action–receipt grammar for demonstration context and interaction memory, without an external skill library.","kind":"pipeline","reviewed":"2026-10-17","width":2200,"height":1228,"note":"Figure 2: A frozen VLM controls robots using a shared observation–action–receipt grammar for demonstration context and interaction memory, without an external skill library."}},"roboprobe":{"policy":{"path":"/images/catalog/roboprobe-policy.webp","original":"https://arxiv.org/pdf/2609.24170#page=4","page":"https://arxiv.org/abs/2609.24170","credit":"RoboProbe / GPT-6 Astra Direct-EEF authors","alt":"Figure 2: A frozen frontier model selects end-effector targets from RGB and robot state.","kind":"pipeline","reviewed":"2026-10-17","width":1353,"height":925,"note":"Figure 2: A frozen frontier model selects end-effector targets from RGB and robot state. Extracted from PDF page 4, cropped to the actual diagram and excluding body text / caption."}},"astra-embodied-policies":{"policy":{"path":"/images/catalog/astra-embodied-policies-policy.webp","original":"https://arxiv.org/pdf/2609.38537#page=2","page":"https://arxiv.org/abs/2609.38537","credit":"GPT-6 Astra as Embodied Policies authors","alt":"Figure 1: Evaluates direct Astra-generated commands with analytic control and hybrid configurations that review learned-policy proposals or command frozen whole-body controllers.","kind":"pipeline","reviewed":"2026-10-17","width":1660,"height":801,"note":"Figure 1: Evaluates direct Astra-generated commands with analytic control and hybrid configurations that review learned-policy proposals or command frozen whole-body controllers. Extracted from PDF page 2, cropped to the actual diagram and excluding body text / caption."}},"astra-robomme":{"policy":{"path":"/images/catalog/astra-robomme-policy.webp","original":"https://arxiv.org/pdf/2610.17350#page=4","page":"https://arxiv.org/abs/2610.17350","credit":"Astra on RoboMME authors","alt":"Figure 2: A full-context frontier VLM retains the episode video and prior responses while commanding every end-effector waypoint in memory-dependent manipulation.","kind":"pipeline","reviewed":"2026-10-17","width":1656,"height":400,"note":"Figure 2: A full-context frontier VLM retains the episode video and prior responses while commanding every end-effector waypoint in memory-dependent manipulation. Extracted from PDF page 4, cropped to the actual diagram and excluding body text / caption."}},"coap":{"policy":{"path":"/images/catalog/coap-policy.webp","original":"https://arxiv.org/html/2610.12369v1/fig_method_overview.png","page":"https://arxiv.org/abs/2610.12369","credit":"Code-Only-as-Policy (COAP) authors","alt":"Figure 3: Offline coding-agent improvement produces a reusable library that measures state and executes stateful robot programs.","kind":"pipeline","reviewed":"2026-10-17","width":756,"height":468,"note":"Figure 3: Offline coding-agent improvement produces a reusable library that measures state and executes stateful robot programs."}},"embodirsi":{"policy":{"path":"/images/catalog/embodirsi-policy.webp","original":"https://arxiv.org/html/2609.38905v3/overview.png","page":"https://arxiv.org/abs/2609.38905","credit":"EmbodiRSI authors","alt":"Figure 1: Real-to-simulation reconstruction and policy rollout feedback drive targeted error correction, adaptive demonstration collection and repeated policy fine-tuning.","kind":"pipeline","reviewed":"2026-10-17","width":1655,"height":1123,"note":"Figure 1: Real-to-simulation reconstruction and policy rollout feedback drive targeted error correction, adaptive demonstration collection and repeated policy fine-tuning."}},"adarsi":{"policy":{"path":"/images/catalog/adarsi-policy.webp","original":"https://arxiv.org/html/2610.20474v1/figures/AdaRSI_pipeline.jpg","page":"https://arxiv.org/abs/2610.20474","credit":"AdaRSI authors","alt":"Figure 2: Improves a frozen executor through harness revision first, then fine-tunes only the substeps whose failures remain after harness repair.","kind":"pipeline","reviewed":"2026-10-17","width":2200,"height":1056,"note":"Figure 2: Improves a frozen executor through harness revision first, then fine-tunes only the substeps whose failures remain after harness repair."}},"re-0":{"policy":{"path":"/images/catalog/re-0-policy.webp","original":"https://arxiv.org/html/2609.32416v1/fig1_main_user.png","page":"https://arxiv.org/abs/2609.32416","credit":"RE-0 authors","alt":"Figure 1: Verifies bounded repairs to failing code-policy segments through paired counterfactual rollouts, then distills accepted interventions into a standalone student.","kind":"pipeline","reviewed":"2026-10-17","width":2200,"height":1274,"note":"Figure 1: Verifies bounded repairs to failing code-policy segments through paired counterfactual rollouts, then distills accepted interventions into a standalone student."}},"rsi-123-rounds":{"policy":{"path":"/images/catalog/rsi-123-rounds-policy.webp","original":"https://arxiv.org/pdf/2609.31760#page=3","page":"https://arxiv.org/abs/2609.31760","credit":"123 Rounds of Robot Skill Discovery authors","alt":"Figure 2: A longitudinal negative-result study audits why an agentic skill-discovery loop can accumulate accepted changes without solving its target manipulation task.","kind":"pipeline","reviewed":"2026-10-17","width":1698,"height":445,"note":"Figure 2: A longitudinal negative-result study audits why an agentic skill-discovery loop can accumulate accepted changes without solving its target manipulation task. Extracted from PDF page 3, cropped to the actual diagram and excluding body text / caption."}},"rho":{"policy":{"path":"/images/catalog/rho-policy.webp","original":"https://arxiv.org/pdf/2606.16458#page=2","page":"https://arxiv.org/abs/2606.16458","credit":"RHO authors","alt":"Figure 1: A tool-enabled coding agent mutates complete robot-policy repositories; gated evaluation and a Pareto frontier retain useful program variants.","kind":"pipeline","reviewed":"2026-10-17","width":1284,"height":619,"note":"Figure 1: A tool-enabled coding agent mutates complete robot-policy repositories; gated evaluation and a Pareto frontier retain useful program variants. Extracted from PDF page 2, cropped to the actual diagram and excluding body text / caption."}},"sear":{"policy":{"path":"/images/catalog/sear-policy.webp","original":"https://arxiv.org/html/2610.18792v1/harness-loop-and-real-world-sequences.png","page":"https://arxiv.org/abs/2610.18792","credit":"SEAR authors","alt":"Figure 2, upper panel: language-model agents, a retained library, robot-policy updates and external feedback form the self-evolving loop.","kind":"pipeline","reviewed":"2026-10-17","width":1312,"height":725,"note":"Figure 2, upper panel: language-model agents, a retained library, robot-policy updates and external feedback form the self-evolving loop. Cropped to the upper method panel; rollout galleries are excluded."}},"enpire":{"policy":{"path":"/images/catalog/enpire-policy.webp","original":"https://arxiv.org/html/2606.19980v2/figure-v4.png","page":"https://arxiv.org/abs/2606.19980","credit":"ENPIRE authors","alt":"Figure 2: Environment preparation, policy improvement, rollout collection and coding-agent evolution form the autonomous robot-learning loop.","kind":"pipeline","reviewed":"2026-10-17","width":1479,"height":1314,"note":"Figure 2: Environment preparation, policy improvement, rollout collection and coding-agent evolution form the autonomous robot-learning loop."}},"embodiedrsi":{"policy":{"path":"/images/catalog/embodiedrsi-policy.webp","original":"https://arxiv.org/html/2610.10498v1/Main_figure.png","page":"https://arxiv.org/abs/2610.10498","credit":"EmbodiedRSI authors","alt":"Figure 2: A frozen robot foundation model is supported by a fast hypothesis-guided experimental loop and slower memory consolidation that co-evolve code and skills.","kind":"pipeline","reviewed":"2026-10-17","width":2010,"height":1098,"note":"Figure 2: A frozen robot foundation model is supported by a fast hypothesis-guided experimental loop and slower memory consolidation that co-evolve code and skills."}},"agent-as-policy":{"policy":{"path":"/images/catalog/agent-as-policy-policy.webp","original":"https://arxiv.org/html/2609.12541v3/agp_method_v12.png","page":"https://arxiv.org/abs/2609.12541","credit":"Agent as Policy (AGP) authors","alt":"Figure 1: General coding agents write and select robot programs from multimodal feedback on physical manipulation tasks, while experience files support repeated-run refinement and transfer.","kind":"pipeline","reviewed":"2026-10-17","width":2038,"height":1307,"note":"Figure 1: General coding agents write and select robot programs from multimodal feedback on physical manipulation tasks, while experience files support repeated-run refinement and transfer."}},"roboskill":{"policy":{"path":"/images/catalog/roboskill-policy.webp","original":"https://arxiv.org/html/2609.37810v1/RoboSkill-revised-final.png","page":"https://arxiv.org/abs/2609.37810","credit":"RoboSkill authors","alt":"Figure 2: An Explore–Execute–Evolve loop acquires and reuses robot skills, including contact-based exploration that combines visual and tactile feedback.","kind":"pipeline","reviewed":"2026-10-17","width":1981,"height":1029,"note":"Figure 2: An Explore–Execute–Evolve loop acquires and reuses robot skills, including contact-based exploration that combines visual and tactile feedback."}},"harness-vla":{"policy":{"path":"/images/catalog/harness-vla-policy.webp","original":"https://arxiv.org/html/2607.08448v5/figures/scheme.png","page":"https://arxiv.org/abs/2607.08448","credit":"Harness VLA authors","alt":"Figure 1: An agent composes analytic primitives and a frozen VLA, using memory to invoke the learned policy within more reliable operating conditions.","kind":"pipeline","reviewed":"2026-10-17","width":2200,"height":1411,"note":"Figure 1: An agent composes analytic primitives and a frozen VLA, using memory to invoke the learned policy within more reliable operating conditions."}},"show-harness":{"policy":{"path":"/images/catalog/show-harness-policy.webp","original":"https://arxiv.org/html/2609.10522v1/method.png","page":"https://arxiv.org/abs/2609.10522","credit":"Show-Harness authors","alt":"Figure 3: A shared semantic action interface connects VLM perception and reasoning to robot motion across embodiments, with frozen frontier-model and adapted compact-model variants.","kind":"pipeline","reviewed":"2026-10-17","width":1064,"height":529,"note":"Figure 3: A shared semantic action interface connects VLM perception and reasoning to robot motion across embodiments, with frozen frontier-model and adapted compact-model variants."}},"gpt-policy":{"policy":{"path":"/images/catalog/gpt-policy-policy.webp","original":"https://arxiv.org/html/2609.19138v1/overview.png","page":"https://arxiv.org/abs/2609.19138","credit":"GPT-Policy authors","alt":"Figure 2: A frozen VLM combines task references, current observations and persistent interaction history to select constrained Cartesian robot tools.","kind":"pipeline","reviewed":"2026-10-17","width":2048,"height":1108,"note":"Figure 2: A frozen VLM combines task references, current observations and persistent interaction history to select constrained Cartesian robot tools."}},"urai":{"policy":{"path":"/images/catalog/urai-policy.webp","original":"https://arxiv.org/html/2609.39018v1/URAI_fig2_framework.png","page":"https://arxiv.org/abs/2609.39018","credit":"Universal Robot-Agent Interface (URAI) authors","alt":"Figure 2: Separates an offline programming agent that writes and validates tools from an execution agent that selects their parameters through a shared GUI / API backend.","kind":"pipeline","reviewed":"2026-10-17","width":1291,"height":734,"note":"Figure 2: Separates an offline programming agent that writes and validates tools from an execution agent that selects their parameters through a shared GUI / API backend."}},"pyrualean":{"policy":{"path":"/images/catalog/pyrualean-policy.webp","original":"https://arxiv.org/pdf/2610.01939#page=4","page":"https://arxiv.org/abs/2610.01939","credit":"PyRUA-Lean authors","alt":"Figure 2: Uses Python cells to compose existing robot primitives with control flow and selective feedback, reducing repeated model calls relative to tool-by-tool execution.","kind":"pipeline","reviewed":"2026-10-17","width":1629,"height":642,"note":"Figure 2: Uses Python cells to compose existing robot primitives with control flow and selective feedback, reducing repeated model calls relative to tool-by-tool execution. Extracted from PDF page 4, cropped to the actual diagram and excluding body text / caption."}},"roborsi":{"policy":{"path":"/images/catalog/roborsi-policy.webp","original":"https://arxiv.org/html/2610.12424v1/2027_ICLR_Framework2.0.png","page":"https://arxiv.org/abs/2610.12424","credit":"RoboRSI (Noematrix) authors","alt":"Figure 2: A multi-agent skill hierarchy uses Top-down Skill Refinement to diagnose execution failures and revise reusable base, atomic and compound skills.","kind":"pipeline","reviewed":"2026-10-17","width":798,"height":418,"note":"Figure 2: A multi-agent skill hierarchy uses Top-down Skill Refinement to diagnose execution failures and revise reusable base, atomic and compound skills."}},"rise-opendrivelab":{"policy":{"path":"/images/catalog/rise-opendrivelab-policy.webp","original":"https://arxiv.org/html/2602.11075v2/world_model.png","page":"https://arxiv.org/abs/2602.11075","credit":"RISE (Compositional World Model) authors","alt":"Figure 4: Learns a compositional visual world model and progress-based value model, then improves a robot policy through imagined rollouts rather than repeated physical exploration.","kind":"pipeline","reviewed":"2026-10-17","width":763,"height":485,"note":"Figure 4: Learns a compositional visual world model and progress-based value model, then improves a robot policy through imagined rollouts rather than repeated physical exploration."}},"me-brain-1":{"policy":{"path":"/images/catalog/me-brain-1-policy.webp","original":"https://arxiv.org/html/2609.24271v2/ME_Brain_Main_Figure.png","page":"https://arxiv.org/abs/2609.24271","credit":"ME-Brain-1.0 authors","alt":"Figure 1: Combines hierarchical multimodal memory, a skill-using Cognitive Core and a world-aware action model to retain experience across deployment.","kind":"pipeline","reviewed":"2026-10-17","width":2010,"height":1139,"note":"Figure 1: Combines hierarchical multimodal memory, a skill-using Cognitive Core and a world-aware action model to retain experience across deployment."}},"icrt":{"policy":{"path":"/images/catalog/icrt-policy.webp","original":"https://arxiv.org/html/2408.15980v2/corl_2024/figures/method_figure.png","page":"https://arxiv.org/abs/2408.15980","credit":"In-Context Robot Transformer (ICRT) authors","alt":"Figure 3: A causal sensorimotor transformer learns to imitate a prompted task from demonstration trajectories without target-task fine-tuning.","kind":"pipeline","reviewed":"2026-10-17","width":2200,"height":433,"note":"Figure 3: A causal sensorimotor transformer learns to imitate a prompted task from demonstration trajectories without target-task fine-tuning."}},"instant-policy":{"policy":{"path":"/images/catalog/instant-policy-policy.webp","original":"https://arxiv.org/html/2411.12633v2/figs/net.png","page":"https://arxiv.org/abs/2411.12633","credit":"Instant Policy authors","alt":"Figure 3: A graph-based diffusion model adapts manipulation behavior from one or two demonstration trajectories using segmented point-cloud geometry.","kind":"pipeline","reviewed":"2026-10-17","width":1511,"height":534,"note":"Figure 3: A graph-based diffusion model adapts manipulation behavior from one or two demonstration trajectories using segmented point-cloud geometry."}},"ricl":{"policy":{"path":"/images/catalog/ricl-policy.webp","original":"https://arxiv.org/html/2508.02062v1/regentic-pi0-arch.png","page":"https://arxiv.org/abs/2508.02062","credit":"RICL authors","alt":"Figure 2: Post-trains a pretrained VLA to use retrieved demonstration groups, enabling new-task adaptation through context without mandatory task-specific weight updates.","kind":"pipeline","reviewed":"2026-10-17","width":1929,"height":837,"note":"Figure 2: Post-trains a pretrained VLA to use retrieved demonstration groups, enabling new-task adaptation through context without mandatory task-specific weight updates."}},"behavior-prompting-policy":{"policy":{"path":"/images/catalog/behavior-prompting-policy-policy.webp","original":"https://arxiv.org/html/2606.30457v2/architecture.png","page":"https://arxiv.org/abs/2606.30457","credit":"Behavior Prompting Policy (BPP) authors","alt":"Figure 2: A demonstration-conditioned diffusion policy uses dense observation and action sequences to reproduce new manipulation behavior, with one prompt demonstration at deployment.","kind":"pipeline","reviewed":"2026-10-17","width":1490,"height":387,"note":"Figure 2: A demonstration-conditioned diffusion policy uses dense observation and action sequences to reproduce new manipulation behavior, with one prompt demonstration at deployment."}},"synthicl":{"policy":{"path":"/images/catalog/synthicl-policy.webp","original":"https://arxiv.org/html/2606.08154v1/figures/model2.drawio.png","page":"https://arxiv.org/abs/2606.08154","credit":"SynthICL authors","alt":"Figure 2: A visual-context flow-matching policy learns in-context imitation from synthetic pseudo-demonstrations and an auxiliary subgoal-image objective.","kind":"pipeline","reviewed":"2026-10-17","width":1133,"height":695,"note":"Figure 2: A visual-context flow-matching policy learns in-context imitation from synthetic pseudo-demonstrations and an auxiliary subgoal-image objective."}},"stellavla":{"policy":{"path":"/images/catalog/stellavla-policy.webp","original":"https://arxiv.org/html/2608.11671v1/framework.png","page":"https://arxiv.org/abs/2608.11671","credit":"StellaVLA authors","alt":"Figure 2: Converts demonstrations into structured visual, kinematic and language context, then trains a VLA with an action expert and a spatial-language auxiliary objective.","kind":"pipeline","reviewed":"2026-10-17","width":1058,"height":416,"note":"Figure 2: Converts demonstrations into structured visual, kinematic and language context, then trains a VLA with an action expert and a spatial-language auxiliary objective."}},"keypoint-action-tokens":{"policy":{"path":"/images/catalog/keypoint-action-tokens-policy.webp","original":"https://arxiv.org/html/2403.19578v3/figures/fig2_png.png","page":"https://arxiv.org/abs/2403.19578","credit":"Keypoint Action Tokens (KAT) authors","alt":"Figure 1: Visual observations become 3D keypoint tokens; a prompted language model predicts action tokens decoded into end-effector poses.","kind":"pipeline","reviewed":"2026-10-17","width":1788,"height":467,"note":"Figure 1: Visual observations become 3D keypoint tokens; a prompted language model predicts action tokens decoded into end-effector poses."}},"roboprompt":{"policy":{"path":"/images/catalog/roboprompt-policy.webp","original":"https://arxiv.org/pdf/2410.12782#page=1","page":"https://arxiv.org/abs/2410.12782","credit":"RoboPrompt authors","alt":"Figure 1: Object-pose and demonstrated keyframe encodings form the context for language-model action prediction.","kind":"pipeline","reviewed":"2026-10-17","width":1772,"height":1221,"note":"Figure 1: Object-pose and demonstrated keyframe encodings form the context for language-model action prediction. Extracted from PDF page 1, cropped to the actual diagram and excluding body text / caption."}},"reflective-vla":{"policy":{"path":"/images/catalog/reflective-vla-policy.webp","original":"https://arxiv.org/html/2606.25215v1/fig2.png","page":"https://arxiv.org/abs/2606.25215","credit":"Reflective VLA authors","alt":"Figure 2: Conditions a continuous-action VLA on bounded histories of observations, executed action chunks and observed consequences.","kind":"pipeline","reviewed":"2026-10-17","width":718,"height":317,"note":"Figure 2: Conditions a continuous-action VLA on bounded histories of observations, executed action chunks and observed consequences."}},"robottt":{"policy":{"path":"/images/catalog/robottt-policy.webp","original":"https://arxiv.org/html/2607.15275v1/model_arch-fig.png","page":"https://arxiv.org/abs/2607.15275","credit":"RoboTTT authors","alt":"Figure 2: Introduces fast-weight test-time training into a robot action model to compress long histories and adapt behavior from context.","kind":"pipeline","reviewed":"2026-10-17","width":1166,"height":447,"note":"Figure 2: Introduces fast-weight test-time training into a robot action model to compress long histories and adapt behavior from context."}},"ici-vla":{"policy":{"path":"/images/catalog/ici-vla-policy.webp","original":"https://arxiv.org/html/2609.07581v1/method.png","page":"https://arxiv.org/abs/2609.07581","credit":"ICI-VLA authors","alt":"Figure 2: Retrieves phase-aligned micro-demonstrations to condition a text-action VLM, using offline alignment and retrieval training while keeping evaluation parameters fixed.","kind":"pipeline","reviewed":"2026-10-17","width":1220,"height":894,"note":"Figure 2: Retrieves phase-aligned micro-demonstrations to condition a text-action VLM, using offline alignment and retrieval training while keeping evaluation parameters fixed."}},"iclr-visual-reasoning":{"policy":{"path":"/images/catalog/iclr-visual-reasoning-policy.webp","original":"https://arxiv.org/html/2603.07530v2/method_3.png","page":"https://arxiv.org/abs/2603.07530","credit":"ICLR: Visual Reasoning Imitation authors","alt":"Figure 2: Augments demonstration-conditioned sensorimotor prediction with explicit image-space future keypoint traces that are generated before robot actions.","kind":"pipeline","reviewed":"2026-10-17","width":1537,"height":345,"note":"Figure 2: Augments demonstration-conditioned sensorimotor prediction with explicit image-space future keypoint traces that are generated before robot actions."}},"robodawn":{"policy":{"path":"/images/catalog/robodawn-policy.webp","original":"https://arxiv.org/html/2609.22966v1/pipeline.png","page":"https://arxiv.org/abs/2609.22966","credit":"Robo Dawn authors","alt":"Figure 2: A frozen VLM uses a compact semantic motion vocabulary and in-context examples, grounded at the gripper interaction point.","kind":"pipeline","reviewed":"2026-10-17","width":1255,"height":810,"note":"Figure 2: A frozen VLM uses a compact semantic motion vocabulary and in-context examples, grounded at the gripper interaction point."}},"vima":{"policy":{"path":"/images/catalog/vima-policy.webp","original":"https://arxiv.org/html/2210.03094v2/arch-fig.png","page":"https://arxiv.org/abs/2210.03094","credit":"VIMA authors","alt":"Figure 3: Object-centric observations and a T5-encoded multimodal prompt condition the causal robot-action transformer through cross-attention.","kind":"pipeline","reviewed":"2026-10-17","width":2009,"height":1412,"note":"Figure 3: Object-centric observations and a T5-encoded multimodal prompt condition the causal robot-action transformer through cross-attention."}},"code-as-policies":{"policy":{"path":"/images/catalog/code-as-policies-policy.webp","original":"https://arxiv.org/pdf/2209.07753#page=1","page":"https://arxiv.org/abs/2209.07753","credit":"Code as Policies (CaP) authors","alt":"Figure 1: A code-writing language model composes perception and robot-control APIs, recursively generating helper functions.","kind":"pipeline","reviewed":"2026-10-17","width":859,"height":557,"note":"Figure 1: A code-writing language model composes perception and robot-control APIs, recursively generating helper functions. Extracted from PDF page 1, cropped to the actual diagram and excluding body text / caption."}},"voxposer":{"policy":{"path":"/images/catalog/voxposer-policy.webp","original":"https://arxiv.org/html/2307.05973v2/method.png","page":"https://arxiv.org/abs/2307.05973","credit":"VoxPoser authors","alt":"Figure 2: Composes 3D value maps from language-model programs and visual grounding, then optimizes robot motion within those maps.","kind":"pipeline","reviewed":"2026-10-17","width":1098,"height":318,"note":"Figure 2: Composes 3D value maps from language-model programs and visual grounding, then optimizes robot motion within those maps."}},"rekep":{"policy":{"path":"/images/catalog/rekep-policy.webp","original":"https://arxiv.org/html/2409.01652v2/method.png","page":"https://arxiv.org/abs/2409.01652","credit":"ReKep authors","alt":"Figure 2: Generates relational constraints over visual 3D keypoints and solves a sequence of constrained optimization problems for manipulation.","kind":"pipeline","reviewed":"2026-10-17","width":1258,"height":564,"note":"Figure 2: Generates relational constraints over visual 3D keypoints and solves a sequence of constrained optimization problems for manipulation."}},"robocat":{"policy":{"path":"/images/catalog/robocat-policy.webp","original":"https://arxiv.org/html/2306.11706v2/robocat_fig_TMLR.png","page":"https://arxiv.org/abs/2306.11706","credit":"RoboCat authors","alt":"Figure 1: Generalist training, few-demonstration adaptation, autonomous data generation and retraining comprise the self-improvement cycle.","kind":"pipeline","reviewed":"2026-10-17","width":1930,"height":644,"note":"Figure 1: Generalist training, few-demonstration adaptation, autonomous data generation and retraining comprise the self-improvement cycle."}},"robogen":{"policy":{"path":"/images/catalog/robogen-policy.webp","original":"https://arxiv.org/html/2311.01455v3/images/method.png","page":"https://arxiv.org/abs/2311.01455","credit":"RoboGen authors","alt":"Figure 2: A generative simulation pipeline proposes robot tasks, assets and learning supervision, then chooses reinforcement learning, motion planning or trajectory optimization to acquire skills.","kind":"pipeline","reviewed":"2026-10-17","width":2200,"height":949,"note":"Figure 2: A generative simulation pipeline proposes robot tasks, assets and learning supervision, then chooses reinforcement learning, motion planning or trajectory optimization to acquire skills."}},"eureka":{"policy":{"path":"/images/catalog/eureka-policy.webp","original":"https://arxiv.org/html/2310.12931v2/figures/eureka.png","page":"https://arxiv.org/abs/2310.12931","credit":"Eureka authors","alt":"Figure 2: Uses language-model reward-code generation and evolutionary reward reflection to train robot skills through reinforcement learning.","kind":"pipeline","reviewed":"2026-10-17","width":2200,"height":1031,"note":"Figure 2: Uses language-model reward-code generation and evolutionary reward reflection to train robot skills through reinforcement learning."}},"dreureka":{"policy":{"path":"/images/catalog/dreureka-policy.webp","original":"https://arxiv.org/html/2406.01967v1/figures/concept/concept.png","page":"https://arxiv.org/abs/2406.01967","credit":"DrEureka authors","alt":"Figure 1: Language-model reward generation and reward-aware physics priors guide domain randomization and sim-to-real policy training.","kind":"pipeline","reviewed":"2026-10-17","width":2200,"height":967,"note":"Figure 1: Language-model reward generation and reward-aware physics priors guide domain randomization and sim-to-real policy training."}},"autort":{"policy":{"path":"/images/catalog/autort-policy.webp","original":"https://arxiv.org/pdf/2401.12963#page=4","page":"https://arxiv.org/abs/2401.12963","credit":"AutoRT authors","alt":"Figure 1: Visual scene descriptions and language-model task selection coordinate fleet data collection and execution-policy assignment.","kind":"pipeline","reviewed":"2026-10-17","width":1246,"height":923,"note":"Figure 1: Visual scene descriptions and language-model task selection coordinate fleet data collection and execution-policy assignment. Extracted from PDF page 4, cropped to the actual diagram and excluding body text / caption."}},"inner-monologue":{"policy":{"path":"/images/catalog/inner-monologue-policy.webp","original":"https://arxiv.org/html/2207.05608v1/feedback-teaser.png","page":"https://arxiv.org/abs/2207.05608","credit":"Inner Monologue authors","alt":"Figure 1: Perception, success detection and optional human feedback supply language evidence for iterative skill planning.","kind":"pipeline","reviewed":"2026-10-17","width":1564,"height":675,"note":"Figure 1: Perception, success detection and optional human feedback supply language evidence for iterative skill planning."}},"saycan":{"policy":{"path":"/images/catalog/saycan-policy.webp","original":"https://arxiv.org/html/2204.01691v2/figures/vfs_llm_all.png","page":"https://arxiv.org/abs/2204.01691","credit":"SayCan authors","alt":"Figure 15: Language-model relevance and learned affordance probabilities jointly select the next executable robot skill.","kind":"pipeline","reviewed":"2026-10-17","width":1996,"height":1230,"note":"Figure 15: Language-model relevance and learned affordance probabilities jointly select the next executable robot skill."}},"progprompt":{"policy":{"path":"/images/catalog/progprompt-policy.webp","original":"https://arxiv.org/html/2209.11302v1/plan_and_exec.png","page":"https://arxiv.org/abs/2209.11302","credit":"ProgPrompt authors","alt":"Figure 2: Prompts an LLM to produce situated robot programs using an allowed action API, object inventory and assertion-style feedback.","kind":"pipeline","reviewed":"2026-10-17","width":1500,"height":728,"note":"Figure 2: Prompts an LLM to produce situated robot programs using an allowed action API, object inventory and assertion-style feedback."}},"instruct2act":{"policy":{"path":"/images/catalog/instruct2act-policy.webp","original":"https://arxiv.org/html/2305.11176v3/framework.png","page":"https://arxiv.org/abs/2305.11176","credit":"Instruct2Act authors","alt":"Figure 2: Maps multimodal instructions into executable robot programs through a modular visual-perception and action API.","kind":"pipeline","reviewed":"2026-10-17","width":1468,"height":873,"note":"Figure 2: Maps multimodal instructions into executable robot programs through a modular visual-perception and action API."}},"robotool":{"policy":{"path":"/images/catalog/robotool-policy.webp","original":"https://arxiv.org/html/2310.13065v1/pipeline.png","page":"https://arxiv.org/abs/2310.13065","credit":"RoboTool authors","alt":"Figure 2: A four-role language-model system reasons about objects as tools and generates robot programs for creative tool-use tasks.","kind":"pipeline","reviewed":"2026-10-17","width":1082,"height":509,"note":"Figure 2: A four-role language-model system reasons about objects as tools and generates robot programs for creative tool-use tasks."}},"cap-x":{"policy":{"path":"/images/catalog/cap-x-policy.webp","original":"https://arxiv.org/html/2603.22435v2/capagent0_figure.png","page":"https://arxiv.org/abs/2603.22435","credit":"CaP-X / CaP-Agent0 authors","alt":"Figure 7: CaP-Agent0 combines an automatically synthesized skill library, visual differencing and parallel coding-agent reasoning.","kind":"pipeline","reviewed":"2026-10-17","width":2044,"height":1090,"note":"Figure 7: CaP-Agent0 combines an automatically synthesized skill library, visual differencing and parallel coding-agent reasoning."}},"embodiedswe":{"policy":{"path":"/images/catalog/embodiedswe-policy.webp","original":"https://arxiv.org/html/2609.27308v2/overview_labels_revised.png","page":"https://arxiv.org/abs/2609.27308","credit":"EmbodiedSWE authors","alt":"Figure 1: Uses coding agents to solve long-horizon simulator-control tasks, then diversifies verified programs into demonstrations for visuomotor policy training.","kind":"pipeline","reviewed":"2026-10-17","width":660,"height":353,"note":"Figure 1: Uses coding agents to solve long-horizon simulator-control tasks, then diversifies verified programs into demonstrations for visuomotor policy training."}},"zetta":{"policy":{"path":"/images/catalog/zetta-policy.webp","original":"https://arxiv.org/html/2608.16590v1/agent_framework_v2.png","page":"https://arxiv.org/abs/2608.16590","credit":"Zetta authors","alt":"Figure 2: Supports a frozen VLA with action-frequency critics, episodic recoveries and validated skill evolution at three time scales.","kind":"pipeline","reviewed":"2026-10-17","width":1256,"height":574,"note":"Figure 2: Supports a frozen VLA with action-frequency critics, episodic recoveries and validated skill evolution at three time scales."}},"rapid":{"policy":{"path":"/images/catalog/rapid-policy.webp","original":"https://arxiv.org/html/2609.30249v1/method.png","page":"https://arxiv.org/abs/2609.30249","credit":"RAPID authors","alt":"Figure 2: Builds reusable object-centric relational programs from demonstrations, using simulation variants and cross-entropy trajectory optimization to verify generalization.","kind":"pipeline","reviewed":"2026-10-17","width":1830,"height":414,"note":"Figure 2: Builds reusable object-centric relational programs from demonstrations, using simulation variants and cross-entropy trajectory optimization to verify generalization."}},"physical-coding":{"policy":{"path":"/images/catalog/physical-coding-policy.webp","original":"https://arxiv.org/html/2609.35432v1/figure-1.png","page":"https://arxiv.org/abs/2609.35432","credit":"Physical Coding authors","alt":"Figure 1: Couples an executable account of task state and constraints with executable control programs, using independently checked physical outcomes to revise the system.","kind":"pipeline","reviewed":"2026-10-17","width":2033,"height":1141,"note":"Figure 1: Couples an executable account of task state and constraints with executable control programs, using independently checked physical outcomes to revise the system."}},"playful":{"policy":{"path":"/images/catalog/playful-policy.webp","original":"https://arxiv.org/html/2606.19419v1/f2.png","page":"https://arxiv.org/abs/2606.19419","credit":"RATs / Playful Agentic Robot Learning authors","alt":"Figure 2: Learns a reusable skill library through self-directed play before receiving downstream task requests, selecting experiences that balance novelty and learnability.","kind":"pipeline","reviewed":"2026-10-17","width":2047,"height":913,"note":"Figure 2: Learns a reusable skill library through self-directed play before receiving downstream task requests, selecting experiences that balance novelty and learnability."}},"aspire":{"policy":{"path":"/images/catalog/aspire-policy.webp","original":"https://arxiv.org/html/2607.00272v1/system_design.png","page":"https://arxiv.org/abs/2607.00272","credit":"ASPIRE authors","alt":"Figure 1: Parallel coding agents discover and repair robot programs, then retain validated skills that can guide other tasks or embodiments.","kind":"pipeline","reviewed":"2026-10-17","width":1455,"height":874,"note":"Figure 1: Parallel coding agents discover and repair robot programs, then retain validated skills that can guide other tasks or embodiments."}},"closed-loop-robot-software":{"policy":{"path":"/images/catalog/closed-loop-robot-software-policy.webp","original":"https://arxiv.org/pdf/2609.19906#page=2","page":"https://arxiv.org/abs/2609.19906","credit":"Closed-Loop Robot Software authors","alt":"Figure 1: Optimizes per-task executable controllers from demonstrations and reuses optimized software as references for related tasks.","kind":"pipeline","reviewed":"2026-10-17","width":1771,"height":947,"note":"Figure 1: Optimizes per-task executable controllers from demonstrations and reuses optimized software as references for related tasks. Extracted from PDF page 2, cropped to the actual diagram and excluding body text / caption."}},"shaper":{"policy":{"path":"/images/catalog/shaper-policy.webp","original":"https://arxiv.org/html/2608.11350v2/figures/main.png","page":"https://arxiv.org/abs/2608.11350","credit":"SHAPER authors","alt":"Figure 2: Adapts a frozen planner by separately evolving textual skills and the context-construction harness through held-out rollout feedback.","kind":"pipeline","reviewed":"2026-10-17","width":2200,"height":816,"note":"Figure 2: Adapts a frozen planner by separately evolving textual skills and the context-construction harness through held-out rollout feedback."}},"roboharn-evo":{"policy":{"path":"/images/catalog/roboharn-evo-policy.webp","original":"https://arxiv.org/html/2609.37583v2/figure2_query_cycle_v10.png","page":"https://arxiv.org/abs/2609.37583","credit":"RoboHarn-Evo authors","alt":"Figure 2: Retains task-level and action-level physical knowledge and repairs or deactivates entries using interaction evidence.","kind":"pipeline","reviewed":"2026-10-17","width":2012,"height":1107,"note":"Figure 2: Retains task-level and action-level physical knowledge and repairs or deactivates entries using interaction evidence."}},"zeva":{"policy":{"path":"/images/catalog/zeva-policy.webp","original":"https://arxiv.org/html/2608.30880v2/zeva_v2.png","page":"https://arxiv.org/abs/2608.30880","credit":"Zeva authors","alt":"Figure 2: Uses a learned causal transition encoder and two-timescale interaction memory to condition a frozen robot policy on observed action effects.","kind":"pipeline","reviewed":"2026-10-17","width":1069,"height":563,"note":"Figure 2: Uses a learned causal transition encoder and two-timescale interaction memory to condition a frozen robot policy on observed action effects."}},"simex":{"policy":{"path":"/images/catalog/simex-policy.webp","original":"https://arxiv.org/html/2609.38982v1/figure2.png","page":"https://arxiv.org/abs/2609.38982","credit":"SimEX authors","alt":"Figure 2: Simulation reconstruction, capability discovery and few-trial physical toolbox repair form the two-stage robot autoresearch pipeline.","kind":"pipeline","reviewed":"2026-10-17","width":1667,"height":1002,"note":"Figure 2: Simulation reconstruction, capability discovery and few-trial physical toolbox repair form the two-stage robot autoresearch pipeline."}},"racap":{"policy":{"path":"/images/catalog/racap-policy.webp","original":"https://arxiv.org/html/2609.29394v1/RACaP_Figure1_editable.png","page":"https://arxiv.org/abs/2609.29394","credit":"RACaP authors","alt":"Figure 1: Separates curriculum-driven robot-API evolution from a frozen runtime hierarchy that reasons over tasks, transports and local execution feedback.","kind":"pipeline","reviewed":"2026-10-17","width":1915,"height":1258,"note":"Figure 1: Separates curriculum-driven robot-API evolution from a frozen runtime hierarchy that reasons over tasks, transports and local execution feedback."}},"rpg":{"policy":{"path":"/images/catalog/rpg-policy.webp","original":"https://arxiv.org/html/2610.02204v1/pipeline-0930.png","page":"https://arxiv.org/abs/2610.02204","credit":"Reconstruct, Practice, Go Real (RPG) authors","alt":"Figure 2: Uses dataset-derived practice tasks, privileged simulator comparison and video analysis to improve a reusable symbolic robot-skill system before physical deployment.","kind":"pipeline","reviewed":"2026-10-17","width":1993,"height":906,"note":"Figure 2: Uses dataset-derived practice tasks, privileged simulator comparison and video analysis to improve a reusable symbolic robot-skill system before physical deployment."}},"scale-selection":{"policy":{"path":"/images/catalog/scale-selection-policy.webp","original":"https://arxiv.org/pdf/2609.39304#page=2","page":"https://arxiv.org/abs/2609.39304","credit":"Scale and Selection: Harness Evolution authors","alt":"Figure 1: Panels (a) and (b) show the visual-interface controller and champion–challenger harness-evolution loop. The PDF is required because raster subimages alone omit the surrounding vector pipeline.","kind":"pipeline","reviewed":"2026-10-17","width":1641,"height":867,"note":"Figure 1: Panels (a) and (b) show the visual-interface controller and champion–challenger harness-evolution loop. The PDF is required because raster subimages alone omit the surrounding vector pipeline. Extracted from PDF page 2, cropped to the actual diagram and excluding body text / caption."}},"robo-cop":{"policy":{"path":"/images/catalog/robo-cop-policy.webp","original":"https://arxiv.org/html/2610.09228v1/figures/method.png","page":"https://arxiv.org/abs/2610.09228","credit":"Robo-COP authors","alt":"Figure 2: Co-evolves a robot orchestrator and learned policy by selectively collecting skill-level data, training candidates and verifying whether to deploy them.","kind":"pipeline","reviewed":"2026-10-17","width":2200,"height":1050,"note":"Figure 2: Co-evolves a robot orchestrator and learned policy by selectively collecting skill-level data, training candidates and verifying whether to deploy them."}},"find":{"policy":{"path":"/images/catalog/find-policy.webp","original":"https://arxiv.org/html/2609.32069v2/find-overview.png","page":"https://arxiv.org/abs/2609.32069","credit":"FIND authors","alt":"Figure 1: Runs real-world policy improvement in a persistent workspace by choosing feasible weak tasks and evaluating outcomes from before / after images.","kind":"pipeline","reviewed":"2026-10-17","width":855,"height":340,"note":"Figure 1: Runs real-world policy improvement in a persistent workspace by choosing feasible weak tasks and evaluating outcomes from before / after images."}},"cwm-rsi":{"policy":{"path":"/images/catalog/cwm-rsi-policy.webp","original":"https://arxiv.org/html/2607.28251v2/figure1_risk_score.png","page":"https://arxiv.org/abs/2607.28251","credit":"Criticality World Model RSI authors","alt":"Figure 1: Targets risky scenarios with a learned criticality model, then trains and selectively deploys specialist robot policies.","kind":"pipeline","reviewed":"2026-10-17","width":1393,"height":731,"note":"Figure 1: Targets risky scenarios with a learned criticality model, then trains and selectively deploys specialist robot policies."}},"appl":{"policy":{"path":"/images/catalog/appl-policy.webp","original":"https://arxiv.org/pdf/2609.35690#page=2","page":"https://arxiv.org/abs/2609.35690","credit":"Agent Priors-guided Policy Learning (APPL) authors","alt":"Figure 1: Construction, documented skill priors and runtime composition form the complete policy-learning and execution pipeline. The PDF is required because raster subimages alone omit the surrounding vector pipeline.","kind":"pipeline","reviewed":"2026-10-17","width":1400,"height":713,"note":"Figure 1: Construction, documented skill priors and runtime composition form the complete policy-learning and execution pipeline. The PDF is required because raster subimages alone omit the surrounding vector pipeline. Extracted from PDF page 2, cropped to the actual diagram and excluding body text / caption."}},"hero":{"policy":{"path":"/images/catalog/hero-policy.webp","original":"https://arxiv.org/pdf/2607.26809#page=1","page":"https://arxiv.org/abs/2607.26809","credit":"HERO authors","alt":"Figure 1: the HERO hierarchy connects heuristic reasoning, successful exemplars and learned visuomotor reflex policies.","kind":"pipeline","reviewed":"2026-10-17","width":1691,"height":614,"note":"Figure 1: the HERO hierarchy connects heuristic reasoning, successful exemplars and learned visuomotor reflex policies. Extracted from PDF page 1, cropped to the actual diagram and excluding body text / caption."}},"robofoundry":{"policy":{"path":"/images/catalog/robofoundry-policy.webp","original":"https://arxiv.org/html/2609.32862v1/robo_model.png","page":"https://arxiv.org/abs/2609.32862","credit":"RoboFoundry authors","alt":"Figure 2: Evolves an executable system of context, memory, hierarchical skills and recovery routines while binding skills to different robot embodiments.","kind":"pipeline","reviewed":"2026-10-17","width":2200,"height":1317,"note":"Figure 2: Evolves an executable system of context, memory, hierarchical skills and recovery routines while binding skills to different robot embodiments."}},"adahvla":{"policy":{"path":"/images/catalog/adahvla-policy.webp","original":"https://arxiv.org/html/2609.29204v1/overview.png","page":"https://arxiv.org/abs/2609.29204","credit":"AdaHVLA authors","alt":"Figure 2: Adapts an executable harness around frozen robot / navigation policies through isolated analysis, revision and assessment contexts.","kind":"pipeline","reviewed":"2026-10-17","width":1187,"height":759,"note":"Figure 2: Adapts an executable harness around frozen robot / navigation policies through isolated analysis, revision and assessment contexts."}},"harnesspai":{"policy":{"path":"/images/catalog/harnesspai-policy.webp","original":"https://arxiv.org/html/2609.29166v1/images/framework/framework.png","page":"https://arxiv.org/abs/2609.29166","credit":"HarnessPAI authors","alt":"Figure 8: Task, skill and executable-code memories surround a planner that invokes perception, control and action-model primitives.","kind":"pipeline","reviewed":"2026-10-17","width":2200,"height":702,"note":"Figure 8: Task, skill and executable-code memories surround a planner that invokes perception, control and action-model primitives."}},"agentic-robotics-loop":{"policy":{"path":"/images/catalog/agentic-robotics-loop-policy.webp","original":"https://arxiv.org/html/2608.07555v1/agenticrobotics_pipeline.png","page":"https://arxiv.org/abs/2608.07555","credit":"AgenticRobotics authors","alt":"Figure 1: Uses an LLM control plane, disposable workers and durable experiment transactions to automate reproducible robot-policy screening and improvement campaigns.","kind":"pipeline","reviewed":"2026-10-17","width":1672,"height":941,"note":"Figure 1: Uses an LLM control plane, disposable workers and durable experiment transactions to automate reproducible robot-policy screening and improvement campaigns."}},"sensenova-roborsi":{"policy":{"path":"/images/catalog/sensenova-roborsi-policy.webp","original":"https://raw.githubusercontent.com/OpenSenseNova/SenseNova-RoboRSI/main/assets/sensenova-roborsi-overview-en.webp","page":"https://github.com/OpenSenseNova/SenseNova-RoboRSI","credit":"SenseNova-RoboRSI authors","alt":"Official runtime overview shows model / skill interfaces, benchmark execution, trace collection and recursive harness improvement.","kind":"architecture","reviewed":"2026-10-17","width":1426,"height":1084,"note":"Official runtime overview shows model / skill interfaces, benchmark execution, trace collection and recursive harness improvement."}},"physicalrsi":{"policy":{"path":"/images/catalog/physicalrsi-policy.webp","original":"https://yanming03.github.io/PhysicalRSI/v2/#architecture-explainer","page":"https://yanming03.github.io/PhysicalRSI/v2/","credit":"PhysicalRSI authors","alt":"The official HTML architecture canvas shows evidence-driven harness revision, skill routing and the physical execution loop.","kind":"architecture","reviewed":"2026-10-17","width":1797,"height":1145,"note":"Official HTML architecture canvas: rollout evidence guides harness revision; skill routing connects observations and episode state to code / VLA execution. Captured from the original rendered architecture canvas, excluding website navigation; no figure elements were reconstructed."}},"robot-icl-survey":{"policy":{"path":"/images/catalog/robot-icl-survey-policy.webp","original":"https://arxiv.org/pdf/2609.36012#page=10","page":"https://arxiv.org/pdf/2609.36012#page=10","credit":"Huang et al. / Robot ICL survey authors","alt":"Four context-to-control interfaces: conditioned actions, geometric transfer, predictive control and skill/agent execution.","kind":"taxonomy","reviewed":"2026-10-17","width":1404,"height":876,"note":"Author conceptual taxonomy, cropped from PDF page 10; describes reviewed method families rather than a new executable policy.","sourceSha256":"fa58ae769a646cdc1dfe38b7c2fed350a78eb8a546ae1664d7b91e2e95de7657"}},"weights-or-skills-survey":{"policy":{"path":"/images/catalog/weights-or-skills-survey-policy.webp","original":"https://arxiv.org/pdf/2608.01851#page=3","page":"https://arxiv.org/pdf/2608.01851#page=3","credit":"Jena et al. / Weights or Skills? authors","alt":"Six robot-learning branches, with code-as-policy subdivided by self-improvement mechanism.","kind":"taxonomy","reviewed":"2026-10-17","width":1188,"height":1530,"note":"Author conceptual taxonomy, cropped from PDF page 3; describes reviewed method families rather than a new executable policy.","sourceSha256":"3b42da899db4a7ab01e3e4bf7cc89572798606a499d269bee0af86e433b4728c"}},"wam-control-survey":{"policy":{"path":"/images/catalog/wam-control-survey-policy.webp","original":"https://arxiv.org/pdf/2609.16074#page=7","page":"https://arxiv.org/pdf/2609.16074#page=7","credit":"Lu et al. / WAM survey authors","alt":"Four WAM architecture classes crossing shared/separate backbones with joint/inverse-dynamics prediction.","kind":"taxonomy","reviewed":"2026-10-17","width":1554,"height":318,"note":"Author conceptual taxonomy, cropped from PDF page 7; describes reviewed method families rather than a new executable policy.","sourceSha256":"c410bbbd06e25c4592a043acf66ca2dcab5ef03eeae601c29093e50898338df4"}},"wam-ttt":{"policy":{"path":"/images/catalog/wam-ttt-policy.webp","original":"https://arxiv.org/html/2607.06988v2/pipeline.png","page":"https://arxiv.org/html/2607.06988","credit":"WAM-TTT authors","alt":"WAM-TTT — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":1336,"height":545,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"osvi-wm":{"policy":{"path":"/images/catalog/osvi-wm-policy.webp","original":"https://arxiv.org/html/2505.20425v2/Figs/flow.png","page":"https://arxiv.org/html/2505.20425","credit":"OSVI-WM authors","alt":"OSVI-WM — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":1435,"height":763,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"demo-jepa":{"policy":{"path":"/images/catalog/demo-jepa-policy.webp","original":"https://arxiv.org/html/2605.20811v1/pipeline.png","page":"https://arxiv.org/html/2605.20811","credit":"Demo-JEPA authors","alt":"Demo-JEPA — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":2033,"height":1359,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"host":{"policy":{"path":"/images/catalog/host-policy.webp","original":"https://arxiv.org/html/2607.20033v4/Fig2.png","page":"https://arxiv.org/html/2607.20033","credit":"HOST authors","alt":"HOST — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":1958,"height":1902,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"zero-wam":{"policy":{"path":"/images/catalog/zero-wam-policy.webp","original":"https://robbyant-research.github.io/Zero-WAM/static/images/paper/framework-v1.0.webp","page":"https://robbyant-research.github.io/Zero-WAM/","credit":"Zero-WAM authors","alt":"Zero-WAM — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":2000,"height":781,"note":"Official model framework Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"recap-retrieval":{"policy":{"path":"/images/catalog/recap-retrieval-policy.webp","original":"https://arxiv.org/html/2606.15631v1/method2.png","page":"https://arxiv.org/html/2606.15631","credit":"ReCAP authors","alt":"ReCAP — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":917,"height":700,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"universal-planning-networks":{"policy":{"path":"/images/catalog/universal-planning-networks-policy.webp","original":"https://arxiv.org/html/1804.00645v2/upn_fig1_2.svg","page":"https://arxiv.org/html/1804.00645","credit":"Universal Planning Networks authors","alt":"Universal Planning Networks — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":1465,"height":592,"note":"Figure 2: complete gradient-based planning method Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"visual-mpc-retrying":{"policy":{"path":"/images/catalog/visual-mpc-retrying-policy.webp","original":"https://proceedings.mlr.press/v87/ebert18a/ebert18a.pdf","page":"https://proceedings.mlr.press/v87/ebert18a/ebert18a.pdf#page=1","credit":"Ebert et al. / CoRL 2018","alt":"Visual MPC with Retrying — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":825,"height":1030,"note":"Figure 1, PDF p. 1: complete training and closed-loop visual-MPC architecture, rendered with all vector labels. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"core-counterfactual":{"policy":{"path":"/images/catalog/core-counterfactual-policy.webp","original":"https://arxiv.org/html/2608.14822v1/framework_core.png","page":"https://arxiv.org/html/2608.14822","credit":"CoRe authors","alt":"CoRe — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":2037,"height":780,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"tau0-vla":{"policy":{"path":"/images/catalog/tau0-vla-policy.webp","original":"https://arxiv.org/html/2608.16885v1/framework_0726.png","page":"https://arxiv.org/html/2608.16885","credit":"τ0-VLA authors","alt":"τ0-VLA — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":1961,"height":808,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"silvr":{"policy":{"path":"/images/catalog/silvr-policy.webp","original":"https://arxiv.org/html/2506.06658v3/silvr_teaser_figure_resized_new.png","page":"https://arxiv.org/html/2506.06658","credit":"SILVR authors","alt":"SILVR — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":1980,"height":576,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"gavel":{"policy":{"path":"/images/catalog/gavel-policy.webp","original":"https://arxiv.org/html/2609.19315v1/media/GAVEL_pipeline.png","page":"https://arxiv.org/html/2609.19315","credit":"GAVEL authors","alt":"GAVEL — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":2800,"height":1215,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"sandwich-residuals":{"policy":{"path":"/images/catalog/sandwich-residuals-policy.webp","original":"https://arxiv.org/html/2609.21740v1/Figure2.png","page":"https://arxiv.org/html/2609.21740","credit":"Sandwich-Residuals authors","alt":"Sandwich-Residuals — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":1905,"height":835,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"metapusher":{"policy":{"path":"/images/catalog/metapusher-policy.webp","original":"https://arxiv.org/html/2609.21122v1/Framework.png","page":"https://arxiv.org/html/2609.21122","credit":"MetaPusher authors","alt":"MetaPusher — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":867,"height":339,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"map-wam":{"policy":{"path":"/images/catalog/map-wam-policy.webp","original":"https://arxiv.org/html/2609.11561v1/framework_cropped.png","page":"https://arxiv.org/html/2609.11561","credit":"MaP-WAM authors","alt":"MaP-WAM — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":1578,"height":830,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"fare":{"policy":{"path":"/images/catalog/fare-policy.webp","original":"https://arxiv.org/html/2609.18016v1/framework_v2.png","page":"https://arxiv.org/html/2609.18016","credit":"FaRe authors","alt":"FaRe — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":1280,"height":474,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"flowcontrol":{"policy":{"path":"/images/catalog/flowcontrol-policy.webp","original":"https://arxiv.org/html/2007.00291v1/images/method_figure.png","page":"https://arxiv.org/html/2007.00291","credit":"FlowControl authors","alt":"FlowControl — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":1052,"height":277,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"dome":{"policy":{"path":"/images/catalog/dome-policy.webp","original":"https://arxiv.org/html/2204.02863v2/main_figure_3.png","page":"https://arxiv.org/html/2204.02863","credit":"DOME authors","alt":"DOME — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":2046,"height":850,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"dinobot":{"policy":{"path":"/images/catalog/dinobot-policy.webp","original":"https://arxiv.org/html/2402.13181v1/figures/big_method.png","page":"https://arxiv.org/html/2402.13181","credit":"DINOBot authors","alt":"DINOBot — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":1768,"height":719,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"ditto":{"policy":{"path":"/images/catalog/ditto-policy.webp","original":"https://arxiv.org/html/2403.15203v2/method_v4.png","page":"https://arxiv.org/html/2403.15203","credit":"DITTO authors","alt":"DITTO — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":2023,"height":431,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"mt3":{"policy":{"path":"/images/catalog/mt3-policy.webp","original":"https://arxiv.org/html/2511.10110v1/Figure_2.png","page":"https://arxiv.org/html/2511.10110","credit":"MT3 authors","alt":"MT3 — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":875,"height":1170,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"odil":{"policy":{"path":"/images/catalog/odil-policy.webp","original":"https://arxiv.org/html/2503.06831v1/Figs/overview-v2.png","page":"https://arxiv.org/html/2503.06831","credit":"ODIL authors","alt":"ODIL — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":2644,"height":622,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"semancorr":{"policy":{"path":"/images/catalog/semancorr-policy.webp","original":"https://arxiv.org/html/2607.28382v2/Pipeline.png","page":"https://arxiv.org/html/2607.28382","credit":"SemAnCorr authors","alt":"SemAnCorr — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":1800,"height":480,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"manilong-shot":{"policy":{"path":"/images/catalog/manilong-shot-policy.webp","original":"https://arxiv.org/html/2512.16302v1/trainingv2.png","page":"https://arxiv.org/html/2512.16302","credit":"ManiLong-Shot authors","alt":"ManiLong-Shot — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":1557,"height":884,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"imop":{"policy":{"path":"/images/catalog/imop-policy.webp","original":"https://arxiv.org/html/2405.13178v2/assets/new_main-inv_regm_network.jpeg","page":"https://arxiv.org/html/2405.13178","credit":"IMOP authors","alt":"IMOP — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":2800,"height":645,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"hrt1":{"policy":{"path":"/images/catalog/hrt1-policy.webp","original":"https://raw.githubusercontent.com/IRVLUTD/HRT1/main/media/pics/overview.png","page":"https://github.com/IRVLUTD/HRT1","credit":"HRT1 authors","alt":"HRT1 — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":2492,"height":1102,"note":"Official complete HRT1 framework Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"robotap":{"policy":{"path":"/images/catalog/robotap-policy.webp","original":"https://arxiv.org/html/2308.15975v2/overview_figure_v2.svg","page":"https://arxiv.org/html/2308.15975","credit":"RoboTAP authors","alt":"RoboTAP — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":1626,"height":622,"note":"Figure 2: dense tracks, active points, plan and servoing pipeline Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"trajectory-transfer-pose":{"policy":{"path":"/images/catalog/trajectory-transfer-pose-policy.webp","original":"https://arxiv.org/html/2310.12077v1/Figure_2.png","page":"https://arxiv.org/html/2310.12077","credit":"Trajectory Transfer via Unseen Pose Estimation authors","alt":"Trajectory Transfer via Unseen Pose Estimation — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":1200,"height":675,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"interaction-warping":{"policy":{"path":"/images/catalog/interaction-warping-policy.webp","original":"https://arxiv.org/html/2306.12392v2/main_figure3.png","page":"https://arxiv.org/html/2306.12392","credit":"Interaction Warping authors","alt":"Interaction Warping — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":912,"height":363,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"functo":{"policy":{"path":"/images/catalog/functo-policy.webp","original":"https://arxiv.org/html/2502.11744v2/imgs/pipeline-4.png","page":"https://arxiv.org/html/2502.11744","credit":"FUNCTO authors","alt":"FUNCTO — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":2287,"height":1797,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"magic-contact":{"policy":{"path":"/images/catalog/magic-contact-policy.webp","original":"https://arxiv.org/html/2411.09627v2/pipeline_revised_revised.png","page":"https://arxiv.org/html/2411.09627","credit":"MAGIC authors","alt":"MAGIC — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":1560,"height":340,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"gift-functional-transfer":{"policy":{"path":"/images/catalog/gift-functional-transfer-policy.webp","original":"https://arxiv.org/html/2503.15371v2/figures/Fig_2_Pipeline-min_.png","page":"https://arxiv.org/html/2503.15371","credit":"GIFT authors","alt":"GIFT — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":2800,"height":909,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"sparse-meets-dense":{"policy":{"path":"/images/catalog/sparse-meets-dense-policy.webp","original":"https://sparse-meets-dense.github.io/static/images/method_page.jpg","page":"https://sparse-meets-dense.github.io/","credit":"Sparse Meets Dense authors","alt":"Sparse Meets Dense — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":2097,"height":1085,"note":"Official sparse/dense method pipeline Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"af-osil-ms":{"policy":{"path":"/images/catalog/af-osil-ms-policy.webp","original":"https://arxiv.org/html/2509.24972v1/method.jpg","page":"https://arxiv.org/html/2509.24972","credit":"AF-OSIL-MS authors","alt":"AF-OSIL-MS — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":1846,"height":958,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"elastic-motion-policy":{"policy":{"path":"/images/catalog/elastic-motion-policy-policy.webp","original":"https://arxiv.org/html/2503.08029v2/img/block_diagram_v5.png","page":"https://arxiv.org/html/2503.08029","credit":"Elastic Motion Policy authors","alt":"Elastic Motion Policy — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":2800,"height":1310,"note":"Figure 5: complete EMP imitation pipeline Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"screwmimic":{"policy":{"path":"/images/catalog/screwmimic-policy.webp","original":"https://arxiv.org/html/2405.03666v1/Method.png","page":"https://arxiv.org/html/2405.03666","credit":"ScrewMimic authors","alt":"ScrewMimic — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":1619,"height":684,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"relational-ndf":{"policy":{"path":"/images/catalog/relational-ndf-policy.webp","original":"https://arxiv.org/html/2211.09786v1/demo_inference_execution.png","page":"https://arxiv.org/html/2211.09786","credit":"Relational NDF authors","alt":"Relational NDF — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":1199,"height":295,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"parts-shape-warping":{"policy":{"path":"/images/catalog/parts-shape-warping-policy.webp","original":"https://arxiv.org/html/2604.15455v1/big_fig.png","page":"https://arxiv.org/html/2604.15455","credit":"Parts-based Shape Warping authors","alt":"Parts-based Shape Warping — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":1326,"height":636,"note":"Author method/policy pipeline; figure text and boundaries reviewed during media preparation. Visually reviewed for complete boundaries, policy role and legibility; author/publisher figure rights remain separate from code licenses."}},"poil":{"policy":{"path":"/images/catalog/poil-policy.webp","original":"https://arxiv.org/pdf/2609.30404","page":"https://arxiv.org/pdf/2609.30404#page=3","credit":"POIL authors","alt":"POIL — author policy/control pipeline","kind":"pipeline","reviewed":"2026-10-17","width":2032,"height":616,"note":"Figure 1, PDF p. 3: complete transfer/execution pipeline including the bottom twist label and robot icon; the arXiv HTML PNG clips the lower edge. Full PDF render visually reviewed; author/publisher rights separate."}},"icpi":{"policy":{"path":"/images/catalog/icpi-policy.webp","original":"https://arxiv.org/html/2508.15021v1/figures/llmirp_method.png","page":"https://arxiv.org/abs/2508.15021","credit":"In-Context Policy Improvement (ICPI) authors","alt":"In-Context Policy Improvement (ICPI) policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"6a130341568cf3d361dd5369b3b415c6c81262448b72cf75fa9614c00b5aafcf","width":1824,"height":561,"note":"Figure 2: A frozen language model predicts adjustments to a low-dimensional dynamic-manipulation controller from preceding trials and example policy improvements."}},"neural-task-programming":{"policy":{"path":"/images/catalog/neural-task-programming-policy.webp","original":"https://arxiv.org/html/1710.01813v2/pipeline.png","page":"https://arxiv.org/abs/1710.01813","credit":"Neural Task Programming (NTP) authors","alt":"Neural Task Programming (NTP) policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"7d79a4cddbddfd328c555032c90181a9ce8ece5f5134464bd2e4446a0774cea0","width":903,"height":232,"note":"Figure 2: Recursively decomposes a demonstrated task into subtasks and callable neural programs, with information scoping to support compositional generalization."}},"xskill":{"policy":{"path":"/images/catalog/xskill-policy.webp","original":"https://arxiv.org/pdf/2307.09955","page":"https://arxiv.org/abs/2307.09955","credit":"XSkill authors","alt":"XSkill policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"60fc0417c4a72efc89672687e8957a14af3038333b2595d8ef465dfde61ae8ae","width":1543,"height":555,"note":"PDF page 1, Figure 1: Discovers shared human–robot skill prototypes from videos, learns a skill-conditioned diffusion controller and composes skills from a new human prompt video."}},"uniskill-human-video":{"policy":{"path":"/images/catalog/uniskill-human-video-policy.webp","original":"https://arxiv.org/html/2505.08787v4/CoRL_fig2.png","page":"https://arxiv.org/abs/2505.08787","credit":"UniSkill (Human Video Imitation) authors","alt":"UniSkill (Human Video Imitation) policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"370c51bc71125d46366514cf6a7a25076407fe359fc689cc26e6c433ecaca2f4","width":1432,"height":589,"note":"Figure 2: Learns embodiment-agnostic skill representations through inverse and forward skill dynamics, then conditions robot diffusion policies on skills extracted from human videos."}},"droc":{"policy":{"path":"/images/catalog/droc-policy.webp","original":"https://arxiv.org/html/2311.10678v2/figures/overview.png","page":"https://arxiv.org/abs/2311.10678","credit":"DROC authors","alt":"DROC policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"e6c19298017cb444b122a7d3b1d91ef9e200dd67a1748a4ef200df203f16ba5b","width":1478,"height":494,"note":"Figure 1: Distills reusable knowledge from online language corrections and retrieves past visual / textual experiences to correct robot plans and skill parameters."}},"architect":{"policy":{"path":"/images/catalog/architect-policy.webp","original":"https://arxiv.org/pdf/2607.23784","page":"https://arxiv.org/abs/2607.23784","credit":"ARCHITECT authors","alt":"ARCHITECT policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"776b5aa0505929401fbd14a2e5a8d2ea0aa3dcc9a3946de646ecfbcd12cc0a30","width":1283,"height":661,"note":"PDF page 2, Figure 1: Uses human language feedback and execution traces to synthesize modular robot programs and retain corrected behaviors in a reusable skill library."}},"lrll":{"policy":{"path":"/images/catalog/lrll-policy.webp","original":"https://arxiv.org/html/2406.18746v2/img/fig2.png","page":"https://arxiv.org/abs/2406.18746","credit":"Lifelong Robot Library Learning (LRLL) authors","alt":"Lifelong Robot Library Learning (LRLL) policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"45a1a95ca759a8b932569ce8873398b1b2ac2b765f59f2590a9c6db18e1721a8","width":1919,"height":400,"note":"Figure 2: Alternates human-guided exploration with skill abstraction to compress successful experiences into a growing composable robot-program library."}},"lyra":{"policy":{"path":"/images/catalog/lyra-policy.webp","original":"https://arxiv.org/pdf/2509.18597","page":"https://arxiv.org/abs/2509.18597","credit":"LYRA authors","alt":"LYRA policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"9a0b5d7f1d91c4d4ade2e26102bb9ef058748f2911e5ff7c3a0252f53acd00f7","width":2011,"height":1600,"note":"PDF page 3, Figure 2: Distills human feedback into modular reusable skills and retrieves skill / example memory to generate ultra-long-horizon robot programs."}},"teach-and-grow":{"policy":{"path":"/images/catalog/teach-and-grow-policy.webp","original":"https://arxiv.org/html/2608.17209v3/fig2.png","page":"https://arxiv.org/abs/2608.17209","credit":"Teach-and-Grow Learning (TGL) authors","alt":"Teach-and-Grow Learning (TGL) policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"fc7a9b2540330cfcbaaec30140e0bacbee36965c1d37b62e9beb22df0ac90c17","width":1980,"height":1192,"note":"Figure 2: Turns a few successful demonstrations into closed-loop Skill Blocks, then expands a verified skill library and experience memory from execution."}},"reflect":{"policy":{"path":"/images/catalog/reflect-policy.webp","original":"https://arxiv.org/html/2306.15724v4/FS_method_summary.png","page":"https://arxiv.org/abs/2306.15724","credit":"REFLECT authors","alt":"REFLECT policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"9ef123d3a20ba1455b3f465d10a4e41f38c4e486995f68b84a9ce6c22c72f73b","width":1413,"height":509,"note":"Figure 2: Summarizes multisensory robot experience into structured events, explains execution failures and supplies those explanations to a correction planner."}},"knowno":{"policy":{"path":"/images/catalog/knowno-policy.webp","original":"https://arxiv.org/html/2307.01928v2/robot-help-teaser.png","page":"https://arxiv.org/abs/2307.01928","credit":"KnowNo authors","alt":"KnowNo policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"e2e69d655d192971d0de1f9ac0a909a42b127520931bbe5a93ff5afa4ccdcab6","width":1764,"height":944,"note":"Figure 1: Calibrates a language planner with conformal prediction so it requests human clarification when multiple next actions remain plausible."}},"racer":{"policy":{"path":"/images/catalog/racer-policy.webp","original":"https://arxiv.org/html/2409.14674v1/model_framework_final.png","page":"https://arxiv.org/abs/2409.14674","credit":"RACER authors","alt":"RACER policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"074d08e8d7c3781086584e76694aed66550ce5781fa1cb6fe57f72a600f9a2e2","width":905,"height":306,"note":"Figure 3: A vision-language supervisor issues rich corrective instructions to a visuomotor actor trained with failure-recovery trajectories."}},"lmpc":{"policy":{"path":"/images/catalog/lmpc-policy.webp","original":"https://arxiv.org/html/2402.11450v2/ims/lmpc-rollout-vs-skip-v2.png","page":"https://arxiv.org/abs/2402.11450","credit":"Language Model Predictive Control (LMPC) authors","alt":"Language Model Predictive Control (LMPC) policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"9fc8d57fd8c4280543110220067c49279414947425809816d01f6c81d881da32","width":2200,"height":977,"note":"Figure 3: Learns to predict future human–robot teaching interactions, then searches imagined interaction rollouts to choose code responses that require fewer corrections."}},"correctvla":{"policy":{"path":"/images/catalog/correctvla-policy.webp","original":"https://arxiv.org/html/2608.29967v1/figure/fig2.detail.png","page":"https://arxiv.org/abs/2608.29967","credit":"CorrectVLA authors","alt":"CorrectVLA policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"4b01a0b4328dd1e93835ec5d93a1cb663848d825eb4621c22848bf63f7718e8a","width":2124,"height":847,"note":"Figure 2: Converts one task-level language correction into temporally scoped additive action adjustments while preserving the underlying VLA weights."}},"awda":{"policy":{"path":"/images/catalog/awda-policy.webp","original":"https://arxiv.org/html/2302.04856v1/system.png","page":"https://arxiv.org/abs/2302.04856","credit":"Attributed Waypoints and Demonstration Augmentation (AWDA) authors","alt":"Attributed Waypoints and Demonstration Augmentation (AWDA) policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"4bcd4f933fe73f097a30d57902cb5e1b16e6efd40a9c646fa53eec50f2360d16","width":2028,"height":547,"note":"Figure 3: Separates demonstration-conditioned task inference from execution by predicting attributed waypoints that hand-crafted motor primitives can realize."}},"orion":{"policy":{"path":"/images/catalog/orion-policy.webp","original":"https://arxiv.org/html/2405.20321v2/method_policy.png","page":"https://arxiv.org/abs/2405.20321","credit":"ORION authors","alt":"ORION policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"e91ad0ef3a7beb44b40a57aeb91b178ad7d923bf2e2dcdc74c0f2a5e3cee9292","width":1663,"height":1048,"note":"Figure 3: Extracts open-world object-graph plans from a single human video and grounds object-centric interaction geometry to execute the task in a new scene."}},"physcap":{"policy":{"path":"/images/catalog/physcap-policy.webp","original":"https://arxiv.org/html/2608.21031v2/main_figure.png","page":"https://arxiv.org/abs/2608.21031","credit":"PhysCaP authors","alt":"PhysCaP policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"6043cc6e08aa24d3ef268bb807ebef679ea3118b64a40463c973668ce3b704d2","width":2044,"height":512,"note":"Figure 2: Uses targeted physical interaction to estimate latent properties such as mass and stiffness before synthesizing a robot-control program."}},"agm":{"policy":{"path":"/images/catalog/agm-policy.webp","original":"https://arxiv.org/pdf/2608.29537","page":"https://arxiv.org/abs/2608.29537","credit":"Achievement-Grounded Memory (AGM) authors","alt":"Achievement-Grounded Memory (AGM) policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"511b0afb47beb3dbab32640da4e8dec9665ab5fbd7be12f9678da9b8691eff9e","width":1581,"height":865,"note":"PDF page 3, Figure 1: Advances a task-memory pointer only after verifying physical achievement, preventing attempted actions from being stored as completed progress."}},"steerable-policies":{"policy":{"path":"/images/catalog/steerable-policies-policy.webp","original":"https://arxiv.org/html/2602.13193v3/HighLevelInstantiations.png","page":"https://arxiv.org/abs/2602.13193","credit":"Steerable Policies authors","alt":"Steerable Policies policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"4ecbcb7d8c00720ff7eb916546c7351bc751955fbbf61ac7fabe34cf9e379953","width":1029,"height":739,"note":"Figure 3: Trains VLAs to follow commands at task, subtask, motion and image-coordinate levels, then controls them with a learned reasoner or a prompted VLM."}},"guava":{"policy":{"path":"/images/catalog/guava-policy.webp","original":"https://arxiv.org/html/2606.18363v3/guava_overview_icra.png","page":"https://arxiv.org/abs/2606.18363","credit":"Guava-4B authors","alt":"Guava-4B policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"bcc3889d441d53d45ed4a5a8589a363681c420ad6ec1417811e5b9c6df9cb7d2","width":940,"height":309,"note":"Figure 1: Distills physically validated frontier-model interaction traces into a compact tool-using manipulation agent, with perturbation branches teaching recovery."}},"pigey":{"policy":{"path":"/images/catalog/pigey-policy.webp","original":"https://arxiv.org/pdf/2607.21725","page":"https://arxiv.org/abs/2607.21725","credit":"Pigey / Physical Agency authors","alt":"Pigey / Physical Agency policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"ff76deb13cb32dd31b24573dfbd33915fb20f25e1c062cddbd4638fb5fe4c804","width":863,"height":492,"note":"PDF page 5, Figure 2: Routes subgoals through complementary frozen VLA and geometric-planning backends, verifying outcomes and recovering through a closed physical-agent loop."}},"malmm":{"policy":{"path":"/images/catalog/malmm-policy.webp","original":"https://arxiv.org/html/2411.17636v2/Main_Pipeline3.png","page":"https://arxiv.org/abs/2411.17636","credit":"MALMM authors","alt":"MALMM policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"59bd49d748db03fa298b886b0a4830130e11cb54f69e7aaf02808960325923df","width":1144,"height":654,"note":"Figure 2: Coordinates separate planner, coder and supervisor agents to generate manipulation programs and recover from failures using observations after each step."}},"proc3s":{"policy":{"path":"/images/catalog/proc3s-policy.webp","original":"https://arxiv.org/html/2406.05572v2/new_diagram.png","page":"https://arxiv.org/abs/2406.05572","credit":"PRoC3S authors","alt":"PRoC3S policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"3d601dcaad34a3f7ba2ff50598bd93e1f16e7006e2ab2872e1e1ca4367b984e7","width":1297,"height":516,"note":"Figure 2: Prompts an LLM to synthesize programs with open continuous parameters, then solves their geometric / kinematic constraints and replans infeasible specifications."}},"ical":{"policy":{"path":"/images/catalog/ical-policy.webp","original":"https://arxiv.org/pdf/2406.14596","page":"https://arxiv.org/abs/2406.14596","credit":"In-Context Abstraction Learning (ICAL) authors","alt":"In-Context Abstraction Learning (ICAL) policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"fe6e17dc31e0631ce8afc73e82ae53ad50a5579e15d3fb2e99bd0464a4164ad1","width":1581,"height":408,"note":"PDF page 4, Figure 1 top panel: noisy demonstrations become revised multimodal examples through abstraction, human feedback and memory."}},"embodiskill":{"policy":{"path":"/images/catalog/embodiskill-policy.webp","original":"https://arxiv.org/html/2605.10332v2/fig2.png","page":"https://arxiv.org/abs/2605.10332","credit":"EmbodiSkill authors","alt":"EmbodiSkill policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"3b890837f72fe22bafbd05f74c182b5e081b41114af7b251abb5c9374beb0af9","width":1195,"height":409,"note":"Figure 2: Distinguishes faulty skill content from failures to follow valid guidance, then makes targeted revisions to an external procedural skill."}},"uni-skill":{"policy":{"path":"/images/catalog/uni-skill-policy.webp","original":"https://arxiv.org/html/2603.02623v1/fig_main2.png","page":"https://arxiv.org/abs/2603.02623","credit":"Uni-Skill (SkillFolder) authors","alt":"Uni-Skill (SkillFolder) policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"66305a657f1325033086c17907d604eaa58931d7c0e3e9179b6ade34c79b755e","width":1467,"height":558,"note":"Figure 3: Uses a hierarchical skill-video repository to retrieve examples, implement missing skills and extend a robot planner beyond a fixed initial skill library."}},"act-observe-rewrite":{"policy":{"path":"/images/catalog/act-observe-rewrite-policy.webp","original":"https://arxiv.org/pdf/2603.04466","page":"https://arxiv.org/abs/2603.04466","credit":"Act-Observe-Rewrite (AOR) authors","alt":"Act-Observe-Rewrite (AOR) policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"fa998d60e6a33503143e1ba429aa6704565d149c4de610cbc17e2fd8e7e61b14","width":1904,"height":641,"note":"PDF page 5, Figure 1: Rewrites the full Python controller between episodes using multimodal failure evidence, while the generated controller executes the next trial without model calls at every step."}},"hi-vla-design":{"policy":{"path":"/images/catalog/hi-vla-design-policy.webp","original":"https://arxiv.org/html/2606.10267v2/pull_newest.png","page":"https://arxiv.org/abs/2606.10267","credit":"Hierarchical VLA Design Study authors","alt":"Hierarchical VLA Design Study policy architecture and execution 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workspace."}},"mimicplay":{"policy":{"path":"/images/catalog/mimicplay-policy.webp","original":"https://arxiv.org/html/2302.12422v2/method_v12.png","page":"https://arxiv.org/abs/2302.12422","credit":"MimicPlay authors","alt":"MimicPlay policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"3e1ff436ca722311261566f22f5cc2d47611f1cd5ffcc0cc1c7240bbfa366171","width":1245,"height":389,"note":"Figure 2: Learns latent plans from human play videos and uses them to condition a low-level robot imitation policy for long-horizon manipulation."}},"sun-kuafu":{"policy":{"path":"/images/catalog/sun-kuafu-policy.webp","original":"https://arxiv.org/html/2608.31167v2/figures/pipeline1.png","page":"https://arxiv.org/abs/2608.31167","credit":"SUN Programs / Kuafu authors","alt":"SUN Programs / Kuafu policy architecture and execution 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learning."}},"sgpt":{"policy":{"path":"/images/catalog/sgpt-policy.webp","original":"https://raw.githubusercontent.com/mlresearch/v305/main/assets/jain25b/jain25b.pdf","page":"https://proceedings.mlr.press/v305/jain25b.html","credit":"ST-GPT / SGPT authors","alt":"ST-GPT / SGPT policy architecture and execution flow","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"1e58b0c8bb702d4fdf9fe31e483623528458324497d0e60d19b605b215fcd6f6","width":976,"height":844,"note":"PDF page 5, Figure 3: subgoal encoder and cross-attention decoder for SGPT action prediction."}},"introspective-planning":{"policy":{"path":"/images/catalog/introspective-planning-policy.webp","original":"https://arxiv.org/html/2402.06529v4/teaser.png","page":"https://arxiv.org/abs/2402.06529","credit":"Introspective Planning authors","alt":"Introspective Planning policy architecture and execution 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query."}},"volo":{"policy":{"path":"/images/catalog/volo-policy.webp","original":"https://arxiv.org/html/2606.07723v1/system.png","page":"https://arxiv.org/abs/2606.07723","credit":"VoLoAgent authors","alt":"VoLoAgent policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"4bcf3e234c0d7ea8d223ce2d78be1e3e0551262647ff5e8b9c3ebbd931751cf5","width":1525,"height":762,"note":"Figure 3: Orchestrates interruptible robot policies and perception tools while monitoring execution and recovering from open-vocabulary manipulation failures."}},"iclhf":{"policy":{"path":"/images/catalog/iclhf-policy.webp","original":"https://arxiv.org/html/2508.07606v2/method.png","page":"https://arxiv.org/abs/2508.07606","credit":"ICLHF authors","alt":"ICLHF policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"6bd4f8299cfa60260c2439a692ad03761b7356290c26216d78c792f35cacf9a9","width":876,"height":475,"note":"Figure 2: Uses textual human feedback and physical-constraint feedback to revise a household task plan while a graph-based optimizer grounds spatial relations."}},"longact-holomind":{"policy":{"path":"/images/catalog/longact-holomind-policy.webp","original":"https://arxiv.org/html/2605.14504v2/holo_mind_small-v2.png","page":"https://arxiv.org/abs/2605.14504","credit":"HoloMind authors","alt":"HoloMind / LongAct policy architecture and execution flow","kind":"architecture","reviewed":"2026-10-17","sourceSha256":"b96c1890c6902d62c09dbf1a765b154f62935a93f486ad7827524e40a97a47a9","width":1243,"height":681,"note":"Figure 3: two-level planning, executor, spatial / episodic memory and critic feedback in the household agent."}},"stageguard":{"policy":{"path":"/images/catalog/stageguard-policy.webp","original":"https://arxiv.org/html/2609.20791v1/fig1.png","page":"https://arxiv.org/abs/2609.20791","credit":"StageGuard authors","alt":"StageGuard policy architecture and execution 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context."}},"revolve":{"policy":{"path":"/images/catalog/revolve-policy.webp","original":"https://arxiv.org/html/2609.14633v2/Pictures/3.png","page":"https://arxiv.org/abs/2609.14633","credit":"REVOLVE authors","alt":"REVOLVE policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"fec8c2872f86bc21d9413f851355cf7f191250cabac8ed4d9bb51e789508e739","width":2200,"height":1301,"note":"Figure 3: Pairs automated geometric reset / correction with external judgment memory and repeated policy updates to improve real manipulation with less manual intervention."}},"eta-openeta":{"policy":{"path":"/images/catalog/eta-openeta-policy.webp","original":"https://arxiv.org/pdf/2608.03924","page":"https://arxiv.org/abs/2608.03924","credit":"OpenETA authors","alt":"OpenETA policy architecture and execution 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states."}},"robo-harness-k1":{"policy":{"path":"/images/catalog/robo-harness-k1-policy.webp","original":"https://arxiv.org/html/2609.29389v1/k1_framework.png","page":"https://arxiv.org/abs/2609.29389","credit":"Robo-Harness K1 authors","alt":"Robo-Harness K1 policy architecture and execution flow","kind":"pipeline","reviewed":"2026-10-17","sourceSha256":"48109071f76cf4404344c3fe281b789501513074531d9b804a7a6ba2ce631b6e","width":1937,"height":1236,"note":"Figure 2: Augments a direct robot-use VLM with calibrated geometry queries, persistent visual anchors and grasp hypotheses while retaining its native tool-call interface."}},"world-action-agent":{"policy":{"path":"/images/catalog/world-action-agent-policy.webp","original":"https://arxiv.org/html/2609.29964v1/waa_method.png","page":"https://arxiv.org/abs/2609.29964","credit":"World Action Agent (WAA) authors","alt":"World Action Agent (WAA) policy architecture and execution 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