Physical intelligence data production
Capture robotics data where systems actually operate.
Capture fleet episodes with source context intact. Every exception reaches human review before evaluated data returns to model teams.
- Who it is for
Manipulation, mobile, AV, drone and warehouse teams.
- What you get
Synced, QC-passed episodes with review evidence.
- How it is priced
Scoped per programme: capture, review depth and volume.
The capture rig
A take is not a training feed.
Four first-person views, one session timeline, and a QC state on every track before anyone labels a frame.
RoboCap records head, chest, left-wrist and right-wrist views to one session. Each view carries a QC state, pending, passed or failed, and annotation stays locked until all four pass. That one rule keeps reviewers from labelling footage that will be thrown away.
- Sync drift checked against the session timeline
- Dropped frames and gaps flagged per view
- Task coverage confirmed against the shot list
- Exposure and occlusion checked before release
- Pending
Captured and synced, waiting for QC.
- Passed
QC passed on this view. Annotation unlocks when all four pass.
- Failed
Gap, drift or coverage problem. Recaptured or excluded, with the reason logged.
Real environments
Captured where the system will operate.
Models trained on staged demos stall in real kitchens, warehouses and streets.
We capture in the environments your system will meet, with the lighting, clutter, interruptions and edge cases intact. Source context stays attached to every episode: where it was captured, on which rig, by which operator and under which task script. When a model fails, the team can trace the failure back to the data that taught it.
- Lighting, clutter and interruptions kept, not staged away
- Operator, rig and task script recorded per episode
- Failures traceable to the episodes behind them
- Fleet episodes triaged before they reach review
Capabilities
Six capabilities for physical AI teams.
- 01
Data capture and generation
Task capture on multi-view rigs in real environments, with scripts, operators and sessions managed end to end.
- 02
VLA data workflows
Vision-language-action data: synced video, trajectories and natural-language instructions, reviewed and versioned.
- 03
Multimodal annotation
Video, sensor, 3D and audio labelled on one governed record per episode.
- 04
Episode evaluation and QA
Episodes scored for success, failure and recoverability, with reviewer notes on every exception.
- 05
Fleet data operations
Fleet uploads triaged, deduplicated and routed so review time goes to the episodes that matter.
- 06
Trajectory annotation
Grasp points, contact events and paths annotated frame-accurately against the session timeline.
The review loop
Capture, structure, review, evaluate, feed back.
Every exception reaches a person before evaluated data returns to the model team.
01Capture
Multi-view sessions recorded in real environments to a task script.
02Structure
Views synced, episodes segmented and metadata attached to the record.
03Review
QC gates per view; specialists review exceptions with the footage attached.
04Evaluate
Episodes scored and labelled; disagreements adjudicated by senior reviewers.
05Feed back
Model failures come back as new capture and labelling tasks.
Data modalities
Five modalities from the physical world.
- VideoMulti-view, synced
- Sensor and telemetryIMU, force, joint states
- 3D and point cloudDepth and lidar
- AudioAmbient and narration
- Text and instruction dataTask language for VLA
Who this is for
Teams putting models into the physical world.
- Manipulation systemsGrasping and dexterous tasks
- Mobile roboticsNavigation in shared spaces
- Autonomous vehiclesPerception and edge cases
- DronesAerial capture and inspection
- Warehouse automationPicking, packing and flow
Questions
Questions about physical intelligence data
Capture, sync, QC and who it is for.
Physical AI
What is physical intelligence data?
Task data from the real world (video, sensor, 3D, audio and instruction data) captured and reviewed so robotics and physical AI models can learn from it.
How are multi-camera takes kept in sync?
Head, chest, left-wrist and right-wrist views are recorded to one session timeline, and each track is checked for drift and dropped frames before QC passes.
Why is annotation locked until QC passes?
A take is not a training feed. Locking annotation until sync, coverage and quality checks pass keeps reviewers from labelling footage that will be thrown away.
Do you support VLA datasets?
Yes. We prepare vision-language-action data: synced video, trajectories and natural-language instructions, reviewed and versioned in BergFlow.
Which teams is this for?
Manipulation systems, mobile robotics, autonomous vehicles, drones and warehouse automation teams.
Bring a robotics workflow into review.
Tell us the task, the environment and the model. We scope the capture, the QC gates and the first batch.