Legged systems have many advantages when compared to their wheeled counterparts. For example, they can more easily navigate extreme, uneven terrain. However, there are disadvantages as well, particularly the difficulty seen in modeling the nonlinearities of the system. Research has shown that using flexible components within legged locomotive systems improves performance measures such as efficiency and running velocity. Because of the difficulties encountered in modeling flexible systems, control methods such as reinforcement learning can be used to define control strategies. Furthermore, reinforcement learning can be tasked with learning mechanical parameters of a system to match a control input. It is shown in this work that when deploying reinforcement learning to find design parameters for a pogo-stick jumping system, the designs the agents learn are optimal within the design space provided to the agents.

翻译：与轮式系统相比,牵引系统有许多优势,例如,它们更容易在极端、不均匀的地形中航行。然而,也有缺点,特别是模拟系统非线性所遇到的困难。研究显示,在腿式机车系统中使用灵活的部件可以提高性能措施,例如效率和运行速度。由于在模拟灵活系统方面遇到的困难,可以使用强化学习等控制方法来界定控制战略。此外,强化学习可承担一项任务,即学习系统机械参数以匹配控制输入。这项工作显示,在部署增援学习以寻找操纵杆跳系统的设计参数时,代理所学习的设计在为代理提供的设计空间内是最佳的。