Robot Learning

Action Distillation

Action distillation is the transfer of action-selection behavior from a teacher policy into a student policy by training the student to match the teacher's output actions or action distributions. Common instances include distilling a privileged simulation policy with access to ground-truth state into a deployable policy using only onboard sensors, and compressing a large or slow model into a smaller, faster one.

Why it matters for physical AI

Teacher-student distillation is the standard bridge from privileged simulation training to real-world sensing in legged locomotion, and it shrinks large foundation policies to sizes that meet onboard latency budgets.