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 · MODALITY
Point cloud datasets for physical AI
3D points from depth cameras, LiDAR, reconstruction, or simulation, used for scene geometry, mapping, object shape, and manipulation planning.
DIRECT ANSWER
Point cloud 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
6 catalog entries
6 of 6 datasets
ManiSkill
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.
ScanNet
Commercial use restricted · Large indoor RGB-D scene reconstruction corpus.
An indoor RGB-D reconstruction dataset used for 3D scene understanding.
Habitat datasets
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.
Waymo Open Dataset
Commercial use restricted · Large autonomous driving scenes with cameras and LiDAR.
A large autonomous driving dataset with camera, LiDAR, and labeled traffic scenes.
ObjectFolder
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.
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.
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 point cloud data with cleaner rights?
Commission target-scoped data with contributor consent, capture metadata, sample QA, and delivery into your own storage.