Household manipulation
Domestic tasks such as opening doors, folding laundry, cooking, cleaning, and object retrieval.
TASK FACETS
Datasets by task
Pick the task your model is being trained or evaluated on. Task taxonomy at the dataset level rarely matches deployment-task taxonomy — these facets surface the closest matches, then the per-dataset profile names the gaps.
DIRECT ANSWER
Robotics datasets vary widely in task labeling. Some are clip-level (action recognition), some are episode-level (manipulation policy), some are skill-level (grasping primitives). The task facet groups by what the buyer is most likely to need first; refine via the dataset profile or alternatives page.
9 FACETS
Browse datasets by task
Robot grasping
Picking up or stabilizing objects with robot grippers, hands, or manipulators.
Long-horizon manipulation
Multi-step manipulation sequences that require planning, memory, subtask boundaries, and recovery over extended task horizons.
Bimanual manipulation
Two-arm tasks that require coordinated movement, handoff, or deformable-object handling.
Human-object interaction
Human hands, bodies, tools, and objects interacting in real scenes.
Navigation
Embodied movement through real or simulated environments with scene understanding and planning.
Tool use
Tasks where a robot or demonstrator uses tools, fixtures, appliances, or articulated objects to change the environment.
Deformable manipulation
Manipulation of cloth, bags, cables, food, or other objects whose shape changes during the task.
Furniture assembly
Long-horizon assembly tasks involving parts, fixtures, spatial reasoning, and repeated contact-rich manipulation.
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 task not covered here?
If your task is too specific or domain-bound for any public corpus, commission a custom capture program with task-aligned acceptance criteria.