Learning Soccer Juggling Skills with Layer-wise Mixture-of-Experts
Learning physics-based character controllers that can successfully integrate diverse motor skills using a single policy remains a challenging problem. We present a system to learn control policies for multiple soccer juggling skills, based on deep reinforcement learning. We introduce a task-description framework for these skills which facilitates the specification of individual soccer juggling tasks and the transitions between them. Desired motions can be authored using interpolation of crude reference poses or based on motion capture data. We show that a layer-wise mixture-of-experts architecture offers significant benefits. During training, transitions are chosen with the help of an adaptive random walk, in support of efficient learning. We demonstrate foot, head, knee, and chest juggles, foot stalls, the challenging around-the-world trick, as well as robust transitions. Our work provides a significant step towards realizing physics-based characters capable of the precision-based motor skills of human athletes.
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