Physical Intelligence
Robotics data operations need review discipline, not demos.
Physical AI work depends on BergFlow for the controlled record and QA, and on Quinn reviewers for edge cases, episode evaluation, and safety-critical exceptions. BergLabs runs it as a managed operation.
Capabilities
Six operations robotics teams hand us.
Trajectory annotation
Labeling robot motion paths, waypoints, and decision boundaries for training data.
Episode evaluation
Reviewing full robot episodes for success/failure criteria and edge-case classification.
Safety case data
Structuring training data for safety-critical scenarios with multi-pass review gates.
Sim-to-real validation
Comparing simulation outputs to real-world sensor data; flagging distribution drift.
Robot fleet QA
Quality-checking annotation work across multi-robot datasets at production volume.
Manipulation data review
Reviewing grasp, placement, and force data for fine-motor task training.
Next step
Bring one dataset.
A 4-week pilot runs annotation, episode review, and QA on a slice of your real sensor data.