Physical AI dataset catalog
Use the catalog to compare source-backed dataset profiles by modality, task, rights signal, consent risk, and deployment fit.
DATASET FACET · ROBOT
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 of 2 datasets
Commercial use unclear · 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.
A large-scale real-world robot manipulation dataset family for fine-grained manipulation, tool use, and multi-robot collaboration.
Commercial use unclear · RoboCasa365 source materials describe 365 everyday tasks, 2,500 kitchen environments, 600+ hours of human demonstration data, and 1,600+ hours of synthetic demonstrations.
A large-scale kitchen simulation framework and dataset family for everyday manipulation tasks in diverse household environments.
FACET REVIEW PATHS
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.
INTERNAL LINKS
Use the catalog to compare source-backed dataset profiles by modality, task, rights signal, consent risk, and deployment fit.
Scan the broader robotics dataset surface before narrowing into promoted profiles, comparisons, and custom collection specs.
Track source updates, licensing notes, and buyer-readiness changes that should trigger a renewed review.
Score whether a public source is enough for the model, rights path, modalities, and target environment.
Separate source license language from contributor consent, redistribution, private-space risk, and model-use assumptions.
Turn a public-source gap into a scoped capture request with sample QA, metadata, and delivery requirements.
Compare data providers when the answer is not another public dataset but a better sourcing or capture route.
Use the company index to separate annotation vendors, data engines, marketplaces, and specialist capture teams.
EXTERNAL REFERENCES
Market context for why physical AI systems need custom, enriched, real-world data beyond generic labeling workflows.
Robotics dataset and tooling context for Hugging Face based collection, sharing, conversion, and training workflows.
A cross-embodiment robotics dataset reference for comparing trajectory scale, robot diversity, and VLA training assumptions.
A large in-the-wild robot manipulation dataset reference for real-world trajectory capture and deployment transfer risk.
TRUELABEL ROUTING
Commission target-scoped data with contributor consent, capture metadata, sample QA, and delivery into your own storage.