Task library
Physical AI training data tasks
Task-specific pages that translate robotics and physical AI data needs into concrete bounty specs, modalities, formats, QA checks, and buyer CTAs.
How to use this hub
Start here when you know the broad category but haven't nailed the exact bounty spec yet. Each linked page narrows the request into a concrete data shape: modality, task, environment, metadata, rights, consent, delivery format, and sample QA. That structure is what turns a vague physical AI data need into something a supplier can prove or reject with evidence.
The hub isn't meant to be the last page you read. It should hand off to a detail page where the specific intent is answered with sample specs, comparison tables, proof requirements, and external source context.
10 pages — search and filter
10 of 10 datasets
Assembly training data
Task data
Assembly training data helps physical AI teams collect scoped examples in bench assembly, light manufacturing, repair, and fixture workflows. When sourcing it, specify multi-view video, hand pose, tool use, and task step labels, target volume, delivery format, rights, consent, and QA rules for step order, tool visibility, part state, and failure/recovery examples.
Bimanual manipulation training data
Task data
Bimanual manipulation training data helps physical AI teams collect scoped examples in assembly, folding, packing, fixture holding, and tool handoff. When sourcing it, specify dual-arm robot traces or two-hand human demonstrations, target volume, delivery format, rights, consent, and QA rules for left/right sync, contact handoffs, and role labels for each arm.
Dexterous manipulation training data
Task data
Dexterous manipulation training data helps physical AI teams collect scoped examples in tools, small objects, drawers, fasteners, and deformables. When sourcing it, specify egocentric video, hand pose, tactile or glove signals, target volume, delivery format, rights, consent, and QA rules for finger visibility, contact phases, and precise task segmentation.
Grasping training data
Task data
Grasping training data helps physical AI teams collect scoped examples in bins, shelves, tabletops, and cluttered work surfaces. When sourcing it, specify multi-view video, object labels, and grasp outcome metadata, target volume, delivery format, rights, consent, and QA rules for outcome labels, object visibility, and repeated attempts across object shapes.
Kitchen tasks training data
Task data
Kitchen tasks training data helps physical AI teams collect scoped examples in residential kitchens, counters, cabinets, sinks, and appliances. When sourcing it, specify egocentric video, object states, hand pose, and environment metadata, target volume, delivery format, rights, consent, and QA rules for task start/end boundaries, object identity, lighting, and consent for private spaces.
Manipulation training data
Task data
Manipulation training data helps physical AI teams collect scoped examples in tabletop, shelf, bin, and drawer manipulation. When sourcing it, specify egocentric or wrist video with object and hand pose, target volume, delivery format, rights, consent, and QA rules for hands and manipulated objects in frame for active segments.
Navigation training data
Task data
Navigation training data helps physical AI teams collect scoped examples in homes, offices, sidewalks, warehouses, and logistics routes. When sourcing it, specify egocentric video, IMU, odometry, and scene metadata, target volume, delivery format, rights, consent, and QA rules for route coverage, timestamp sync, obstacle labels, and privacy review.
Robot demonstrations training data
Task data
Robot demonstrations training data helps physical AI teams collect scoped examples in home, warehouse, and workshop tasks. When sourcing it, specify video plus task outcome labels, target volume, delivery format, rights, consent, and QA rules for complete task boundaries and visible object interactions.
Teleoperation training data
Task data
Teleoperation training data helps physical AI teams collect scoped examples in robot workcells, warehouses, kitchens, and labs. When sourcing it, specify robot state, action traces, and synchronized camera streams, target volume, delivery format, rights, consent, and QA rules for timestamp alignment, state/action completeness, and recoverable failure examples.
Warehouse picking training data
Task data
Warehouse picking training data helps physical AI teams collect scoped examples in racks, bins, totes, conveyors, and packing stations. When sourcing it, specify egocentric video, exocentric video, barcode/object metadata, and pick outcomes, target volume, delivery format, rights, consent, and QA rules for SKU visibility, hand-object contact, scan events, and consented facility capture.
Procurement questions before posting a bounty
- What exact model behavior or evaluation question should this data improve?
- Which modality, camera viewpoint, robot state, or metadata stream is required?
- What evidence proves the supplier has rights, consent, and provenance?
- Which delivery format must the sample open in before scale-up?
- What specific failure reasons should cause sample rejection?
Quality gate before a page becomes a deal spec
A page in this hub should not be treated as a finished procurement document by itself. It is a starting point for a bounty. Before a buyer funds capture or licenses off-the-shelf data, the page needs to become a short operating spec: accepted examples, rejected examples, file format, metadata fields, consent requirements, delivery location, and a named reviewer who can approve the sample.
The practical test is simple: if two suppliers read the same detail record, would they submit comparable samples? If not, the buyer needs to narrow the research into a more specific bounty. The strongest truelabel references help with that narrowing by linking from broad hubs into task pages, dataset profiles, format guides, glossary definitions, and public dataset alternatives.
| Gate | Question | Pass signal |
|---|---|---|
| Intent | What model behavior does the data improve? | The objective is tied to a task, benchmark, or evaluation gap. |
| Evidence | What proves a supplier can deliver? | A sample package includes files, manifest, rights, and QA notes. |
| Ingestion | Can the buyer load the sample? | The sample opens in the expected format or converter. |
Hub FAQ
How should buyers use the Physical AI training data tasks hub?
Use the Physical AI training data tasks hub to move from a broad physical AI data need into a concrete page with modality, sample, QA, format, rights, and supplier-evidence requirements.
Are these pages public datasets?
No. These pages are sourcing and specification guides for posting bounties. They help buyers define what a supplier must prove before data is accepted.
Why does this hub link to so many detail pages?
Each detail page handles one specific task, dataset, comparison, definition, or format. The hub is the index that helps a buyer pick the right one for the bounty they want to post.
What makes a page ready for a bounty?
A page is ready when it names a model objective, concrete files, metadata requirements, rights and consent expectations, sample QA checks, and a delivery format.
External source context
- Scale AI physical AI data engine
Shows enterprise demand for custom physical AI collection and enrichment programs.
- NVIDIA Physical AI Data Factory Blueprint
Frames physical AI data as an end-to-end factory problem spanning curation, generation, evaluation, and delivery.
- Open X-Embodiment
Baseline open robotics data entity for cross-embodiment tasks and VLA pretraining discussions.
- Ego4D dataset
Canonical egocentric video benchmark for first-person physical-world capture and limitations.