Robot Learning

Human-in-the-Loop

Human-in-the-loop refers to system designs in which a person actively participates in a robot's learning or operation, providing demonstrations, corrective interventions, preference labels, or approvals rather than leaving the system fully autonomous. In robot learning it spans interactive imitation methods like DAgger and HG-DAgger, intervention-based RL such as HIL-SERL, and deployment-time oversight where operators take over on failure.

Why it matters for physical AI

Interventions both guarantee safe behavior during the fragile early life of a policy and generate precisely targeted training data, forming the improvement flywheel behind most commercial robot fleets.