Robot Learning

Policy Distillation

Policy distillation is the transfer of behavior from one or more teacher policies into a student network by supervised learning on the teachers' outputs, introduced for deep RL by Rusu et al. in 2016. In robotics it most often takes the teacher-student form: a teacher trained in simulation with privileged state supervises a student that acts only on deployable observations such as onboard cameras and proprioception.

Why it matters for physical AI

Teacher-student distillation is the standard bridge from privileged simulation training to real-world deployment, powering learned quadruped locomotion and many sim-to-real manipulation systems.