Accepted inputs
- Fixed-camera and egocentric video
- Frame sequences
- Synchronized perception data
- Model-generated proposals requiring verification
ROBOTICS & PHYSICAL AI
Robotics and Physical AI Data Annotation
Physical AI datasets connect visual perception with time, action and outcome. We support workflows that combine object tracks, action intervals, workspace labels and event states, while keeping observable evidence separate from assumptions about intent.
SCOPE
What this service covers
Robotic-arm and workspace videoEgocentric operational videoObject and tool trackingAction and outcome segmentsTrajectory and state annotationsSensor-data QA against client schemas
Annotation approach
- Object-action taxonomy alignment
- Persistent identity rules
- Temporal-boundary definitions
- Action outcome and failure states
- Sequence-level review
Common edge cases
- Occluded manipulations
- Concurrent human and robot actions
- Aborted or incomplete tasks
- Tool-object identity confusion
- Camera or sensor discontinuity
QUALITY ASSURANCE
Review that follows the guideline
Quality checks are selected for the data type and acceptance criteria. Metrics and review coverage depend on the project, platform and agreed workflow.
Track and trajectory continuityAction-boundary reviewObject-action relationship checksOutcome-label validationSequence and export consistency
Applications
- Robotic manipulation
- Warehouse automation
- Industrial inspection
- Human-robot interaction
- Operational safety models
Output formats
- Timestamped JSON
- Frame-level JSON or CSV
- Tracking exports
- Client-defined robotics schemas
What to include in your brief
- Perception and action taxonomy
- Sequence examples
- Identity and temporal-boundary rules
- Required object-action relationships
- Platform, output and acceptance criteria
START WITH A CALIBRATION BATCH
Reduce uncertainty before production.
Share your guidelines, sample characteristics, volume, preferred platform and timeline. We’ll assess the scope and propose a representative calibration step.
Discuss your dataset