Physical AI Data Collection: Designing Capture + QA at Scale

Physical AI data pipelines break when capture and annotation are designed separately. Sensor context, environment metadata, and task intent must be captured together or downstream labels become ambiguous.

For embodied workloads, QA starts in the field: capture protocol checks, device validation, and sampling audits prevent bad raw input from entering expensive labeling stages.

Use a three-gate system: capture quality gate, annotation quality gate, and validation gate. Each gate should have explicit pass/fail criteria and responsible owner roles.

Build sampling-based deep review on top of broad automated checks. This controls cost while preserving confidence in edge-case handling.