What "good" physical AI data looks like
Episode volume alone does not predict deployment success. Sync, calibration, and tail coverage matter.
Good physical AI data is auditable: time synchronization proven, sensor extrinsics stable, and tail behaviors represented at known rates. Episode volume alone does not predict deployment success. Define acceptance tests before scale, not after terabytes land in object storage. Operant writes those bars into custom robotics data collection programs so your ML team can trust what arrives.
A large corpus with drifted clocks or missing outcome labels wastes training time. The policy learns a mix of the world and the logging error.
Acceptance tests that belong in scope
- Sync tolerances across cameras, proprioception, and force or LiDAR. That is the job of multi-sensor synchronization.
- Calibration files that match the episodes they describe.
- Metadata completeness: scene ID, outcome, lighting, and the filters your eval will use.
- Tail coverage at agreed ratios, not a leftover of whatever happened to occur. See edge-case data collection and eval benchmarks from real-world tails.
Run those tests on the pilot, then keep them as gates during scale. QA practice should match the statement of work, not a visual spot-check at delivery.
Book a discovery call to align quality bars with your training stack.
Scope your capture program
Book a discovery call to align on your stack and data requirements.