PHYSICAL AI
Train robots on real human expertise.
Egocentric demonstrations, hand-object interactions, tool use, and task outcomes captured from skilled people in real operating environments.
Design a physical AI collectionHUMAN DEMONSTRATIONS
Capture the complete physical task—not just the movement.
Pair what the expert sees with their objective, task state, actions, tool interactions, corrections, and verified result.
Egocentric POV
First-person visual context aligned to the expert’s natural task perspective.
Hand-object interaction
Manipulation, contact, grasp, placement, and state change through the task.
Tool use
Tool selection, setup, operation, switching, and relationship to the task objective.
Task decomposition
Break long-horizon activities into observable phases, actions, and state transitions.
Success + verification
Ground demonstrations in task outcomes, acceptance checks, and final state.
Failure + recovery
Capture mistakes, unexpected conditions, human correction, and recovery behavior.
THE FULL OUTCOME SPACE
Success alone is not enough.
Physical systems need examples of how tasks fail, how experts detect the change, and how they adapt. Collection programs can intentionally cover clean demonstrations, errors, interventions, edge cases, and recoveries.
OPERATING ENVIRONMENTS
Where physical intelligence happens.
Programs can target high-value tasks across controlled sites, working facilities, and skilled-trade environments.
- Assembly + manufacturing01
- Repair + maintenance02
- Inspection + quality03
- Warehouse operations04
- Construction + skilled trades05
- Equipment operation06
- Laboratory workflows07
- Agriculture + field work08