Robot Learning

Neural Policy

A neural policy is a control policy represented by a neural network that maps observations, such as camera images and proprioceptive state, directly to robot actions. It is trained end-to-end with imitation learning, reinforcement learning, or both, rather than hand-engineered. Architectures range from small MLPs for joint-space control to transformer- and diffusion-based models such as ACT and Diffusion Policy.

Why it matters for physical AI

Learned policies can capture contact-rich, perception-driven behaviors that are impractical to program explicitly, forming the core executable component of modern robot learning systems.