Robot Learning

Policy

A policy is the function by which a robot selects actions, mapping states or raw observations to motor commands, either deterministically or as a probability distribution. In modern robot learning, policies are typically neural networks trained by reinforcement or imitation learning, consuming images and proprioception and emitting joint targets, end-effector twists, or action chunks at rates from a few hertz to several hundred.

Why it matters for physical AI

The policy is the deployable artifact of robot learning: its input modalities, action space, and inference rate define the interface between learned intelligence and physical hardware.