Learning a Single Policy for Diverse Behaviors on a Quadrupedal Robot Using Scalable Motion Imitation

Arnaud Klipfel,Nitish Sontakke,Ren Liu,Sehoon Ha,Arnaud Klipfel,Nitish Sontakke,Ren Liu,Sehoon Ha

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 mo...