Simulation
Synthetic robot environments used for scalable benchmark and policy development.
ROBOT FACETS
Datasets by robot
Pick the embodiment your team is training on. Each robot facet aggregates the public datasets that ship trajectories, video, and metadata for that platform — with truelabel's commercial-use and consent-risk notes layered on.
DIRECT ANSWER
Datasets are facet-tagged by platform when the original capture used that robot or a close mechanical analogue. Robot-specific facets matter for sim-to-real transfer, controller compatibility, and gripper-equivalence checks before policy training.
8 FACETS
Browse datasets by robot
Franka
Research manipulator often used for tabletop manipulation and imitation-learning datasets.
ALOHA
Low-cost bimanual teleoperation platform frequently used for imitation-learning demonstrations.
UR5
Industrial collaborative robot arm used in manipulation and automation research.
Humanoid
Human-shaped robot platforms for whole-body control, teleoperation, and deployment data.
Mobile manipulator
Robot platforms that combine a mobile base with one or more manipulation arms.
Sawyer
Rethink Robotics Sawyer manipulator used in several teleoperation and manipulation benchmark datasets.
xArm
UFACTORY xArm manipulator family used in real-world collection, imitation-learning, and VLA evaluation setups.
CROSS-CATALOG
Pair with another facet
Combine this facet with a second filter (modality, task, robot, format, license, or commercial-use) on the main dataset catalog to narrow the buyer decision faster.
KEEP DIGGING
Use this record as part of a broader dataset review
A dataset record is only useful when it connects into the rest of the buyer workflow. The next review step is usually not another summary; it is a fit check, rights triage, source comparison, or custom bounty spec that names the missing proof.
For physical AI teams, the hard question is whether the public source can support a specific model objective under real deployment constraints. That requires adjacent dataset records, tools, comparisons, and sourcing paths, plus external references that a reviewer can open and challenge.
Use the links below to keep the review grounded. Start broad when discovery is incomplete, move into profile and comparison pages when the candidate source is known, and switch to custom collection when the blocker is rights, consent, geography, robot embodiment, or target environment coverage.
Where to go next
- 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 data for a robot we don't cover?
If your embodiment isn't in the catalog, commission a custom collection on your exact platform with sample QA and rights review.