DATASET FACET · MODALITY
Teleoperation trajectories datasets for physical AI
Robot demonstrations recorded while a human controls the platform, often including action states, end-effector poses, and synchronized video.
DIRECT ANSWER
Teleoperation trajectories pages collect datasets where this modalityis 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
10 catalog entries
10 of 10 datasets
Open X-Embodiment
Published May 2026 · custom
A large cross-institution collection of robot demonstrations spanning many embodiments and manipulation tasks.
- Multi-institution robot demonstration corpus; exact per-task scale varies by contributing dataset.
- Commercial use unclear
- Best for: robot foundation model pretraining
DROID
Published May 2026 · custom
A real-world robot manipulation dataset focused on diverse teleoperated demonstrations outside narrow lab-only settings.
- Large real-world manipulation corpus; check source for current release counts.
- Commercial use unclear
- Best for: real-world manipulation pretraining
ALOHA
Published May 2026 · custom
A low-cost bimanual teleoperation platform and dataset family used for imitation learning in dexterous manipulation.
- Task-specific demonstrations released around the ALOHA platform and follow-on projects.
- Commercial use unclear
- Best for: bimanual imitation learning
RH20T
Published May 2026 · custom
A real-world contact-rich robot manipulation dataset with multimodal sensing, force, audio, and human demonstration video.
- Source describes more than 110,000 contact-rich manipulation sequences with visual, force, audio, action, and human demonstration signals.
- Commercial use unclear
- Best for: contact-rich manipulation
AgiBot World
Published May 2026 · custom
A large-scale real-world robot manipulation dataset family for fine-grained manipulation, tool use, and multi-robot collaboration.
- 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.
- Commercial use unclear
- Best for: large-scale manipulation pretraining
RoboSet
Published May 2026 · custom
A real-world multi-task kitchen manipulation dataset with teleoperated and kinesthetic demonstrations.
- Source describes 30,050 trajectories, including 9,500 collected through teleoperation, across 12 skills and 38 tasks with four camera views.
- Commercial use unclear
- Best for: real-world kitchen manipulation
RoboTurk
Published May 2026 · custom
A large-scale teleoperation data collection platform and dataset family for robot manipulation tasks.
- Project materials describe over 100 hours of real robot data and thousands of successful manipulation demonstrations collected through remote users.
- Commercial use unclear
- Best for: teleoperation collection design
UMI
Published May 2026 · custom
Universal Manipulation Interface is an in-the-wild human demonstration framework for transferring portable gripper data to robot policies.
- 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.
- Commercial use unclear
- Best for: portable in-the-wild demonstrations
FurnitureBench
Published May 2026 · custom
A real-world long-horizon furniture assembly benchmark with successful demonstration data.
- Documentation describes 219.6 hours and 5,100 successful furniture assembly demonstrations collected with controller and keyboard inputs.
- Commercial use unclear
- Best for: long-horizon assembly
LeRobot datasets
Published May 2026 · custom
A Hugging Face robotics dataset ecosystem and standardized dataset format for multimodal robot learning data.
- LeRobot documentation describes a standardized dataset ecosystem on Hugging Face Hub using Parquet for tabular data and MP4 for video observations.
- Commercial use unclear
- Best for: robotics dataset distribution
READ THE TAG WITH CARE
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.
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
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Commission target-scoped data with contributor consent, capture metadata, sample QA, and delivery into your own storage.