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
3D scene or object geometry represented as spatial points, often from depth sensors or reconstruction.
DIRECT ANSWER
Point cloud 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
10 of 10 datasets
Commercial use unclear · Large simulated task suite; source materials define current task count.
A simulated robot learning benchmark with many manipulation tasks in CoppeliaSim.
Source appears permissive; verify data terms · Simulation suite with tasks and environments maintained by the ManiSkill project.
A simulation benchmark and toolkit for manipulation skills and embodied AI policy evaluation.
Commercial use unclear · Human-object interaction dataset with 4D annotations; exact release details live on the source site.
A 4D egocentric human-object interaction dataset with RGB-D and pose-oriented annotations.
Commercial use unclear · Dexterous grasping dataset with YCB objects and pose labels.
A dexterous hand-object interaction dataset centered on grasping YCB objects with 3D annotations.
Commercial use restricted · Large indoor RGB-D scene reconstruction corpus.
An indoor RGB-D reconstruction dataset used for 3D scene understanding.
Commercial use unclear · Multiple scene and task datasets under the AI Habitat ecosystem.
A family of embodied AI datasets and simulation assets for navigation and rearrangement research.
Commercial use unclear · Household activity benchmark and simulation assets.
A benchmark for household activities and embodied AI tasks in simulation.
Commercial use restricted · Large autonomous driving scenes with cameras and LiDAR.
A large autonomous driving dataset with camera, LiDAR, and labeled traffic scenes.
Commercial use unclear · Object-centric multimodal assets; source materials define current object count and modalities.
A dataset family for object-centric physical properties, geometry, and multimodal perception research.
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.
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.