  We investigate the use of prior knowledge of human and animal movement to
learn reusable locomotion skills for real legged robots. Our approach builds
upon previous work on imitating human or dog Motion Capture (MoCap) data to
learn a movement skill module. Once learned, this skill module can be reused
for complex downstream tasks. Importantly, due to the prior imposed by the
MoCap data, our approach does not require extensive reward engineering to
produce sensible and natural looking behavior at the time of reuse. This makes
it easy to create well-regularized, task-oriented controllers that are suitable
for deployment on real robots. We demonstrate how our skill module can be used
for imitation, and train controllable walking and ball dribbling policies for
both the ANYmal quadruped and OP3 humanoid. These policies are then deployed on
hardware via zero-shot simulation-to-reality transfer. Accompanying videos are
available at https://bit.ly/robot-npmp.
