Using RL to Generalize Robot Policies for Multiple Embodiments

This work proposes a data-efficient framework that combines the sample efficiency of IL with the adaptability of reinforcement learning (RL) to learn a general policy that can be quickly adapted to novel robot embodiments without retraining from scratch.