In this work, we consider the complex control problem of making a monopod reach a target with a jump. The monopod can jump in any direction and the terrain underneath its foot can be uneven. This is a template of a much larger class of problems, which are extremely challenging and computationally expensive to solve using standard optimisation-based techniques. Reinforcement Learning (RL) could be an interesting alternative, but the application of an end-to-end approach in which the controller must learn everything from scratch, is impractical. The solution advocated in this paper is to guide the learning process within an RL framework by injecting physical knowledge. This expedient brings to widespread benefits, such as a drastic reduction of the learning time, and the ability to learn and compensate for possible errors in the low-level controller executing the motion. We demonstrate the advantage of our approach with respect to both optimization-based and end-to-end RL approaches.

翻译：本文研究单足机器人通过跳跃到达目标位置的复杂控制问题。该单足机器人可向任意方向跳跃，且足部接触的地形可能不平整。此类问题属于更广泛问题类别中的典型代表，采用传统基于优化的方法求解时计算复杂度极高且极具挑战性。强化学习可能是一种有趣的替代方案，但若采用端到端学习方法，要求控制器从头学习全部技能，在实践中并不可行。本文提出的解决方案是在强化学习框架中注入物理知识以引导学习过程。这一策略带来了广泛优势，包括大幅缩短学习时间，以及具备学习并补偿运动执行层低级控制器潜在误差的能力。我们通过与基于优化方法和端到端强化学习方法的对比实验，验证了本文方法的优越性。