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.

翻译：本研究提出了一种基于核残差学习的四足机器人运动控制框架。首先，利用从模型预测控制（MPC）控制器采集的数据训练核神经网络。在冻结核网络的同时，通过强化学习训练残差控制器网络，以获取通用运动技能及抵御外部扰动的能力。借助该框架，我们以高样本效率与可控性学习到鲁棒的四足运动控制器，可提供连续速度下的全向运动。其通用性与鲁棒性在专家MPC控制器无法穿越的未知地形上得到了验证。此外，学习到的核函数能够产生一系列功能性运动行为，并可泛化至未见的步态。