  This work developed a kernel-based residual learning framework for
quadrupedal robotic locomotion. Initially, a kernel neural network is trained
with data collected from an MPC controller. Alongside a frozen kernel network,
a residual controller network is trained via reinforcement learning to acquire
generalized locomotion skills and resilience against external perturbations.
With this proposed framework, a robust quadrupedal locomotion controller is
learned with high sample efficiency and controllability, providing
omnidirectional locomotion at continuous velocities. Its versatility and
robustness are validated on unseen terrains that the expert MPC controller
fails to traverse. Furthermore, the learned kernel can produce a range of
functional locomotion behaviors and can generalize to unseen gaits.
