What "good" physical AI data looks like
Episode volume alone does not predict deployment success, sync, calibration, and tail coverage matter.
Good physical AI datasets are auditable: time synchronization proven, sensor extrinsics stable, and tail behaviors represented at known rates.
Teams should define acceptance tests before scale, not after terabytes land in object storage.
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