Robot Learning

Interactive Imitation Learning

Interactive imitation learning is a family of methods in which an expert provides feedback during policy execution, rather than only supplying demonstrations upfront. DAgger established the paradigm by querying expert actions on states the learner visits, and variants like HG-DAgger and intervention-based schemes let humans take over only when the policy errs, concentrating supervision where it is most informative.

Why it matters for physical AI

Covariate shift makes pure behavior cloning brittle on states of its own making; on-policy corrections repair exactly those failure modes, and intervention logs double as a natural deployment supervision signal.