Robot Learning

Reset-Free Reinforcement Learning

Reset-free reinforcement learning is the training of RL agents in the real world without manual environment resets between episodes, which otherwise demand constant human intervention. Approaches include jointly learning a reset or recovery policy that returns the environment to viable start states, cyclic multi-task curricula in which each task resets another, and formulating learning as one continual autonomous process rather than discrete episodes.

Why it matters for physical AI

Human-supervised resets are the hidden labor cost that keeps RL off real robots at scale, so reset-free autonomy is a prerequisite for robots that improve unattended from their own experience.