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 · TASK
Long-horizon manipulation datasets for physical AI
Multi-step manipulation sequences that require planning, memory, subtask boundaries, and recovery over extended task horizons.
DIRECT ANSWER
Long-horizon manipulation pages collect datasets where this taskis 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
9 catalog entries
9 of 9 datasets
RH20T
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.
AgiBot World
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.
RoboCasa
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.
LIBERO
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.
RoboSet
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.
RoboTurk
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.
UMI
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.
FurnitureBench
Commercial use unclear · Documentation describes 219.6 hours and 5,100 successful furniture assembly demonstrations collected with controller and keyboard inputs.
A real-world long-horizon furniture assembly benchmark with successful demonstration data.
LeRobot datasets
Commercial use unclear · LeRobot documentation describes a standardized dataset ecosystem on Hugging Face Hub using Parquet for tabular data and MP4 for video observations.
A Hugging Face robotics dataset ecosystem and standardized dataset format for multimodal robot learning data.
FACET REVIEW PATHS
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.
INTERNAL LINKS
Continue the buyer workflow
Hugging Face robotics index
Scan the broader robotics dataset surface before narrowing into promoted profiles, comparisons, and custom collection specs.
Dataset changelog
Track source updates, licensing notes, and buyer-readiness changes that should trigger a renewed review.
Dataset fit checker
Score whether a public source is enough for the model, rights path, modalities, and target environment.
License risk checker
Separate source license language from contributor consent, redistribution, private-space risk, and model-use assumptions.
Data spec generator
Turn a public-source gap into a scoped capture request with sample QA, metadata, and delivery requirements.
Vendor alternatives hub
Compare data providers when the answer is not another public dataset but a better sourcing or capture route.
Data annotation companies
Use the company index to separate annotation vendors, data engines, marketplaces, and specialist capture teams.
EXTERNAL REFERENCES
Source context to verify
Scale AI physical AI data engine
Market context for why physical AI systems need custom, enriched, real-world data beyond generic labeling workflows.
LeRobot documentation
Robotics dataset and tooling context for Hugging Face based collection, sharing, conversion, and training workflows.
Open X-Embodiment
A cross-embodiment robotics dataset reference for comparing trajectory scale, robot diversity, and VLA training assumptions.
DROID dataset
A large in-the-wild robot manipulation dataset reference for real-world trajectory capture and deployment transfer risk.
TRUELABEL ROUTING
Need long-horizon manipulation data with cleaner rights?
Commission target-scoped data with contributor consent, capture metadata, sample QA, and delivery into your own storage.