“…It expands the reach of robots and enables them to solve a wide range of tasks, from daily life delivery to planetary exploration in challenging, uneven terrain [16,2]. Recently, besides the success of Deep Reinforcement Learning (RL) in navigation [56,27,86,42] and robotic manipulation [49,48,78,40], we have also witnessed the tremendous improvement of locomotion skills for quadruped robots, allowing them to walk on uneven terrain [85,84], and even generalize to real-world with mud, snow, and running water [46]. While these results are encouraging, most RL approaches focus on learning a robust controller for blind quadrupedal locomotion, using only the proprioceptive measurements as inputs.…”