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：本文研究的是让单腿机器人通过跳跃到达指定目标的复杂控制问题。该单腿机器人可以向任意方向跳跃，且其足部下方地形可能不平坦。这是一大类问题的典型代表，这类问题采用基于优化的标准技术解决极为困难且计算成本高昂。强化学习（RL）可能是一种有趣的替代方案，但采用端到端方法（控制器必须从零开始学习所有内容）并不实用。本文提出的解决方案是在强化学习框架内，通过注入物理知识来指导学习过程。这一方法带来了广泛益处，例如大幅缩短学习时间，以及能够学习并补偿执行运动的底层控制器中可能存在的误差。我们展示了该方法相对于基于优化的方法和端到端强化学习方法的优势。