Zero-Shot Retargeting of Learned Quadruped Locomotion Policy Using A Hybrid Kinodynamic Model and Predictive Control

As a rivaling control technique, Reinforcement Learning (RL) has demonstrated great performance in quadruped locomotion. However, it remains a challenge to reuse a policy on another robot, i.e., policy transferability, which saves time for retraining. In this work, we reduce the gap by devloping a planning-and-control framework that systematically integrates RL and Model Predictive Control (MPC). The planning stage employs RL to generate a dynamically-plausible trajectory as well as the contact schedule. These information are then used to seed the MPC in the low level to stabilize and robustify the motion. In addition, our MPC controller employs a novel Hybrid Kino-Dynamics (HKD) model which implicitly optimizes the foothold locations. The results are surprisingly good since the policy trained for the Unitree A1 robot could be transferred to the MIT Mini Cheetah with the proposed pipeline.