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 · FORMAT
Hierarchical data format commonly used for trajectories, demonstrations, and arrays.
DIRECT ANSWER
HDF5 pages collect datasets where this formatis 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
17 of 17 datasets
Commercial use unclear · Multi-institution robot demonstration corpus; exact per-task scale varies by contributing dataset.
A large cross-institution collection of robot demonstrations spanning many embodiments and manipulation tasks.
Commercial use unclear · Large real-world manipulation corpus; check source for current release counts.
A real-world robot manipulation dataset focused on diverse teleoperated demonstrations outside narrow lab-only settings.
Commercial use unclear · Robot manipulation demonstrations across multiple tasks; source release describes exact split.
A robot manipulation dataset from Berkeley focused on real-world behavior cloning and task generalization.
Commercial use unclear · Large language-conditioned robot demonstrations described in the source paper and project materials.
A robotics transformer data release associated with language-conditioned robot manipulation research.
Commercial use unclear · Task-specific demonstrations released around the ALOHA platform and follow-on projects.
A low-cost bimanual teleoperation platform and dataset family used for imitation learning in dexterous manipulation.
Source appears permissive; verify data terms · Benchmark datasets and demonstration formats vary by task suite.
A benchmark and dataset framework for robot imitation learning with standardized tasks and evaluation utilities.
Commercial use unclear · Multi-robot manipulation dataset; source materials specify exact robot/task counts.
A multi-robot dataset for visual foresight and manipulation policy research.
Commercial use unclear · Long-horizon simulated benchmark and demonstrations.
A benchmark for language-conditioned long-horizon robot manipulation in simulated environments.
Commercial use unclear · Task demonstrations and model references associated with the BC-Z project.
A behavior cloning project focused on zero-shot task generalization for robots.
Source appears permissive; verify data terms · Simulation tasks and assets for manipulation research.
A simulation framework and benchmark suite for robot manipulation tasks.
Commercial use unclear · Source describes more than 110,000 contact-rich manipulation sequences with visual, force, audio, action, and human demonstration signals.
A real-world contact-rich robot manipulation dataset with multimodal sensing, force, audio, and human demonstration video.
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.
Commercial use unclear · Benchmark datasets are organized around multiple LIBERO task suites, including spatial, object, goal, and long-horizon manipulation variants.
A benchmark suite for lifelong robot learning and language-conditioned manipulation tasks.
Commercial use unclear · Source describes 30,050 trajectories, including 9,500 collected through teleoperation, across 12 skills and 38 tasks with four camera views.
A real-world multi-task kitchen manipulation dataset with teleoperated and kinesthetic demonstrations.
Commercial use unclear · Project materials describe over 100 hours of real robot data and thousands of successful manipulation demonstrations collected through remote users.
A large-scale teleoperation data collection platform and dataset family for robot manipulation tasks.
Commercial use unclear · Project materials emphasize portable in-the-wild data collection and fast demonstrations for tasks such as cup manipulation, dish washing, cloth folding, and dynamic tossing.
Universal Manipulation Interface is an in-the-wild human demonstration framework for transferring portable gripper data to robot policies.
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.