DATASET FACET · ROBOT
Humanoid datasets for physical AI
Human-shaped robot platforms for whole-body control, teleoperation, and deployment data.
DIRECT ANSWER
Humanoid pages collect datasets where this robotis materially relevant, then add truelabel’s commercial use, consent risk, and deployment fit notes so buyers can decide whether public data is enough.
MATCHED DATASETS
2 catalog entries
2 of 2 datasets
AgiBot World
Published May 2026 · custom
A large-scale real-world robot manipulation dataset family for fine-grained manipulation, tool use, and multi-robot collaboration.
- Hugging Face organization page describes the Beta release as 1M+ trajectories and 2,976.4 hours across 217 tasks, 87 skills, 3,000+ objects, and 100+ real-world scenarios.
- Commercial use unclear
- Best for: large-scale manipulation pretraining
RoboCasa
Published May 2026 · custom
A large-scale kitchen simulation framework and dataset family for everyday manipulation tasks in diverse household environments.
- RoboCasa365 source materials describe 365 everyday tasks, 2,500 kitchen environments, 600+ hours of human demonstration data, and 1,600+ hours of synthetic demonstrations.
- Commercial use unclear
- Best for: large-scale kitchen simulation
HUMANOID READINESS MATRIX
Humanoid whole-body datasets: a readiness matrix
Whole-body humanoid data coordinates locomotion and manipulation rather than treating a single arm in isolation. It reaches models through five distinct routes: human motion, retargeted motion, simulation, teleop, and policy rollout. This matrix separates those routes and records embodiment, DoF, arms/hands/base, locomotion coupling, action rate, cameras, proprioception/torque, license, release state, and real-hardware validation.
This is a directional sample of two humanoid robot datasets, not representative coverage. It supports whole-body humanoid dataset, loco-manipulation dataset, and humanoid manipulation dataset discovery without claiming a neighboring procurement or procedure intent. Unknown publisher fields stay unknown; the matrix does not infer deployment readiness or transfer guarantees.
| Dataset / route | Embodiment / DoF | Arms, hands, base | Locomotion / rate | Sensors | Release / validation | Evidence |
|---|---|---|---|---|---|---|
| AgiBot World teleop | Humanoid and mobile-manipulator recordings DoF: Unknown — not verified in the registered primary source | Dual-arm whole-body platform; exact hand/base fields require publisher review | Unknown — whole-body locomotion coupling is not established by the card fields used here Action rate: Unknown — not verified in the registered primary source | RGB-D listed by the curated catalog Proprioception listed; torque availability unverified | released Real-world trajectories are publisher-reported; validation protocol unverified · License: Custom — review dataset terms | needs-review · medium Claim: lane04-agibot-release · Entity: agibot-world · Field: whole-body readiness fields · Unit: categorical · dataset-huggingface-agibot-world-beta · dataset · AgiBotWorld-Beta card checked 2026-07-22 · Dataset card overview and release metadata · 2026-07-22 · unverified — source retrieval hash not recorded · Source-reported, not independently validated; unknown fields require direct publisher review. |
| RoboCasa simulation | Simulated manipulation platforms; not a humanoid hardware release DoF: Unknown — not verified in the registered primary source | Manipulation-focused simulation; humanoid hands/base unverified | Not established — manipulation-focused simulation Action rate: Unknown — not verified in the registered primary source | RGB-D and point-cloud observations listed by the curated catalog Proprioception listed; torque availability unverified | released No humanoid real-hardware validation established by this project source · License: Custom — review project and asset terms | needs-review · medium Claim: lane04-robocasa-simulation-route · Entity: robocasa · Field: whole-body readiness fields · Unit: categorical · project-robocasa-ai · project · Project page checked 2026-07-22 · Project overview and release links · 2026-07-22 · unverified — source retrieval hash not recorded · Source-reported, not independently validated; unknown fields require direct publisher review. |
Need a procedure or procurement path? Read the humanoid dataset guide, review custom humanoid data sourcing, or use the fit checker. Request path: whole-body humanoid capture matched to a spec.
READ THE TAG WITH CARE
Do not treat this tag as the whole sourcing decision
Facet groupings are discovery aids, not final recommendations. A shared modality, task, robot, format, license, or commercial-use label only says that datasets are worth comparing; it does not prove that the source is safe, complete, or useful for a target model.
Use this grouping to shortlist candidates, then open the dataset profiles, run fit and license checks, and compare sources against the buyer's target environment. Thin tag results become useful only when they route the reader into deeper evidence and action surfaces.
The external references below keep the facet grounded in robotics data practice. They help reviewers understand why format, embodiment, trajectory quality, licensing, and real-world coverage matter before a team commits engineering time to ingestion.
When a facet has only a few matching datasets, treat that as a signal rather than a weakness. It may mean the public corpus is thin for that robot, task, or format, and the next move is a custom supplement with the facet written into acceptance criteria.
Where to go next
- Robot training data marketplaceRobotics datasets
- Physical AI dataset catalogUse the catalog to compare source-backed dataset profiles by modality, task, rights signal, consent risk, and deployment fit.
- Hugging Face robotics indexScan the broader robotics dataset surface before narrowing into promoted profiles, comparisons, and custom collection specs.
- Dataset changelogTrack source updates, licensing notes, and buyer-readiness changes that should trigger a renewed review.
- Dataset fit checkerScore whether a public source is enough for the model, rights path, modalities, and target environment.
- License risk checkerSeparate source license language from contributor consent, redistribution, private-space risk, and model-use assumptions.
- Data spec generatorTurn a public-source gap into a scoped capture request with sample QA, metadata, and delivery requirements.
- Vendor alternatives hubCompare data providers when the answer is not another public dataset but a better sourcing or capture route.
- Data annotation companiesUse the company index to separate annotation vendors, data engines, marketplaces, and specialist capture teams.
Other places to verify the claims
- Scale AI physical AI data engineMarket context for why physical AI systems need custom, enriched, real-world data beyond generic labeling workflows.
- LeRobot documentationRobotics dataset and tooling context for Hugging Face based collection, sharing, conversion, and training workflows.
- Open X-EmbodimentA cross-embodiment robotics dataset reference for comparing trajectory scale, robot diversity, and VLA training assumptions.
- DROID datasetA large in-the-wild robot manipulation dataset reference for real-world trajectory capture and deployment transfer risk.
TRUELABEL ROUTING
Need humanoid data with cleaner rights?
Commission target-scoped data with contributor consent, capture metadata, sample QA, and delivery into your own storage.