RGB-D
Color video paired with depth maps or point clouds for scene geometry, object localization, and manipulation training.
MODALITY FACETS
Datasets by modality
Pick the modality your model architecture expects. Egocentric video, teleoperation traces, RGB-D, point cloud, and tactile signals each unlock different downstream tasks — and each carries different consent and licensing posture by default.
DIRECT ANSWER
Modality is the most common starting point for buyer queries. The wrong modality forces a re-collection; the right modality with wrong rights forces a re-licensing pass. These facets group datasets by what they actually ship at the sensor level.
8 FACETS
Browse datasets by modality
Proprioception
Robot state streams such as joint positions, velocities, gripper state, and end-effector poses.
Teleoperation trajectories
Robot demonstrations recorded while a human controls the platform, often including action states, end-effector poses, and synchronized video.
Point cloud
3D points from depth cameras, LiDAR, reconstruction, or simulation, used for scene geometry, mapping, object shape, and manipulation planning.
Egocentric video
First-person video captured from an operator, wearer, or robot perspective, usually used to model action, attention, and hand-object interaction.
Motion capture
Optical or IMU-derived body and hand pose data used to reconstruct physical movement for training or evaluation.
Third-person video
External-camera video of people, objects, scenes, or tasks, usually used for action understanding and visual pretraining rather than robot state learning.
Tactile sensing
Force, pressure, taxel, or glove-derived signals used for contact-rich manipulation and dexterous control.
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.
RESEARCH PATHS
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.
INTERNAL LINKS
Continue the buyer workflow
Physical AI dataset catalog
Use the catalog to compare source-backed dataset profiles by modality, task, rights signal, consent risk, and deployment fit.
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 a modality combination not in any one dataset?
If your model needs paired modalities (egocentric + tactile, RGB-D + proprioception) that no single public corpus delivers, commission a custom collection with synchronized capture.