论文标题
通过增强学习选择单台跳跃系统的机械参数
Selecting Mechanical Parameters of a Monopode Jumping System with Reinforcement Learning
论文作者
论文摘要
与车轮同行相比，腿部系统具有许多优势。例如，他们可以更容易地在极端，不平衡的地形中导航。但是，也存在缺点，尤其是在建模系统的非线性时出现的困难。研究表明，在腿部机车系统中使用柔性组件可改善效率和运行速度等性能指标。由于在建模灵活系统中遇到的困难，因此可以使用诸如增强学习之类的控制方法来定义控制策略。此外，可以通过系统的学习机械参数来匹配控件输入。在这项工作中表明，当部署强化学习以找到Pogo-Stick跳跃系统的设计参数时，代理商学习的设计在提供给代理商的设计空间内是最佳的。
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.
