Agile and Versatile Robot Locomotion via Kernel-based Residual Learning

Abstract

This work developed a kernel-based residual learning framework for quadrupedal robotic locomotion. Ini-tially, a kernel neural network is trained with data collected from an MPC controller. Alongside a frozen kernel network, a residual controller network is trained using reinforcement learning to acquire generalized locomotion skills and robust-ness against external perturbations. The proposed framework successfully learns a robust quadrupedal locomotion controller with high sample efficiency and controllability, which can provide omnidirectional locomotion at continuous velocities. We validated its versatility and robustness on unseen terrains that the expert MPC controller failed to traverse. Furthermore, the learned kernel can produce a range of functional locomotion behaviors and can generalize to unseen gaits.