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
Franka datasets for physical AI
Research manipulator often used for tabletop manipulation and imitation-learning datasets.
DIRECT ANSWER
Franka 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
11 catalog entries
11 of 11 datasets
Open X-Embodiment
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.
DROID
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.
BridgeData V2
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.
RoboNet
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.
BC-Z
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.
RoboSuite
Source appears permissive; verify data terms · Simulation tasks and assets for manipulation research.
A simulation framework and benchmark suite for robot 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.
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.
TACO Play
Commercial use unclear · TensorFlow Datasets documentation lists TACO Play as Franka kitchen interaction data with train and test splits and a 47.77 GiB dataset size.
A kitchen robot manipulation dataset with Franka arm interaction data available through TensorFlow Datasets.
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
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