Robot Learning

Teacher-Student Distillation

Teacher-student distillation is a training paradigm in which a teacher policy with access to privileged information, such as ground-truth terrain geometry, contact states, or object poses available only in simulation, supervises a student policy that must act from deployable observations like proprioception and cameras. The approach was popularized in legged locomotion by Lee et al.'s 2020 work on the ANYmal quadruped and is now standard in sim-to-real reinforcement learning pipelines.

Why it matters for physical AI

Distillation lets reinforcement learning exploit simulator-only state during training while producing policies that run on real sensors, and the same recipe is used to compress large robot foundation models into fast onboard controllers.