  Developing agile behaviors for legged robots remains a challenging problem.
While deep reinforcement learning is a promising approach, learning truly agile
behaviors typically requires tedious reward shaping and careful curriculum
design. We formulate agile locomotion as a multi-stage learning problem in
which a mentor guides the agent throughout the training. The mentor is
optimized to place a checkpoint to guide the movement of the robot's center of
mass while the student (i.e. the robot) learns to reach these checkpoints. Once
the student can solve the task, we teach the student to perform the task
without the mentor. We evaluate our proposed learning system with a simulated
quadruped robot on a course consisting of randomly generated gaps and hurdles.
Our method significantly outperforms a single-stage RL baseline without a
mentor, and the quadruped robot can agilely run and jump across gaps and
obstacles. Finally, we present a detailed analysis of the learned behaviors'
feasibility and efficiency.
