Long-horizon context
A locally plausible action can be wrong when the earlier instruction or later outcome is considered.
Where programs get difficult
The hard part is rarely drawing the first label.
Embodiment matters
The same visual scene can imply different actions for different kinematics, tools and control policies.
Failure boundaries
Attempts often degrade gradually, making the useful start, recovery and failure points hard to define.
Multiview disagreement
Occlusion, latency and camera placement can make one view misleading without the rest of the episode.
What we deliver
Ground truth built for the system around it.
- Episode, phase and subtask segmentation
- Action, state and trajectory annotation
- Manipulation and human–object interaction
- Contact, grasp and handoff events
- Failure, recovery and task-outcome labels
- Teleoperation quality and demonstration review
Quality controls
- Episode-level instructions and observable boundary rules
- Synchronized review across camera, state and action streams
- Specialist escalation for manipulation and control ambiguity
- Consensus where intent or outcome remains genuinely subjective
- Reusable failure taxonomies and traceable adjudication
From ambiguity to production
Prove the workflow before adding volume.
Frame the task
Define the behavior, embodiment, observation window and decision the labels must support.
Align episodes
Synchronize views, robot state, actions, language and outcomes before annotation begins.
Annotate & review
Label temporally with trained teams, independent review and explicit failure adjudication.
Deliver evidence
Return usable supervision with provenance, quality decisions and agreed acceptance support.
Bring us the episodes that look right but train wrong.
A useful pilot can begin with a small set of successful, failed and ambiguous demonstrations. We’ll help turn them into a specification your data operation can scale.
Request a pilot plan