Robot Learning

Reactive Policy

A reactive policy is a controller that maps the current observation, or a short history, directly to an action without explicit planning, search, or internal simulation at decision time. Most end-to-end learned policies, from behavior-cloned manipulation networks to RL locomotion controllers, are reactive, achieving high control rates and robustness to disturbances while relying on training-time experience rather than deliberation to handle long-horizon structure.

Why it matters for physical AI

Fast closed-loop reaction is what lets learned controllers correct errors and reject perturbations in real time, but the deliberation-free design also explains their struggles with novel long-horizon tasks, motivating hybrid planner-policy architectures.