Robot Learning

Test-Time Adaptation

Test-time adaptation is the adjustment of a trained model during deployment, without access to original training labels, to cope with distribution shift in the inputs it encounters. Techniques include entropy minimization on unlabeled test data, updating normalization statistics, self-supervised auxiliary objectives, and, in robotics, online system identification or rapid adaptation modules that infer environment parameters from recent experience, as in the RMA approach for legged locomotion.

Why it matters for physical AI

Real environments drift away from any training distribution through lighting, wear, payload, and terrain changes, and policies that adapt online rather than failing silently are substantially more viable for long-duration autonomous deployment.