Robot Learning

Online Fine-Tuning

Online fine-tuning is the continued training of a pretrained policy using data gathered while the policy interacts with its target environment. Improvement signals include reinforcement learning rewards, human corrections as in DAgger-style interventions, or autonomous success detection. It contrasts with offline fine-tuning on a fixed dataset and requires safeguards, because the policy is updated on the same system that is acting.

Why it matters for physical AI

Pretrained generalist policies rarely reach production reliability out of the box; closing the last gap with data from the actual deployment site and embodiment is often the decisive step.