  Learning various motor skills for quadrupedal robots is a challenging problem
that requires careful design of task-specific mathematical models or reward
descriptions. In this work, we propose to learn a single capable policy using
deep reinforcement learning by imitating a large number of reference motions,
including walking, turning, pacing, jumping, sitting, and lying. On top of the
existing motion imitation framework, we first carefully design the observation
space, the action space, and the reward function to improve the scalability of
the learning as well as the robustness of the final policy. In addition, we
adopt a novel adaptive motion sampling (AMS) method, which maintains a balance
between successful and unsuccessful behaviors. This technique allows the
learning algorithm to focus on challenging motor skills and avoid catastrophic
forgetting. We demonstrate that the learned policy can exhibit diverse
behaviors in simulation by successfully tracking both the training dataset and
out-of-distribution trajectories. We also validate the importance of the
proposed learning formulation and the adaptive motion sampling scheme by
conducting experiments.
