Policy Distillation Data for Robotics: Annotating Teacher-Student Pairs for Compact Deployable Models
The rapid development of robotic systems is largely due to the use of increasingly sophisticated machine learning models capable of performing complex tasks of perception, decision-making, and control. One promising approach to solving this problem is policy distillation, which involves transferring knowledge from a complex, high-performance teacher model
