  Agile and adaptive maneuvers such as fall recovery, high-speed turning, and
sprinting in the wild are challenging for legged systems. We propose a
Curricular Hindsight Reinforcement Learning (CHRL) that learns an end-to-end
tracking controller that achieves powerful agility and adaptation for the
legged robot. The two key components are (I) a novel automatic curriculum
strategy on task difficulty and (ii) a Hindsight Experience Replay strategy
adapted to legged locomotion tasks. We demonstrated successful agile and
adaptive locomotion on a real quadruped robot that performed fall recovery
autonomously, coherent trotting, sustained outdoor speeds up to 3.45 m/s, and
tuning speeds up to 3.2 rad/s. This system produces adaptive behaviours
responding to changing situations and unexpected disturbances on natural
terrains like grass and dirt.
