Selecting Mechanical Parameters of a Monopode Jumping System with Reinforcement Learning
This work addresses the challenge of modeling nonlinearities in flexible legged systems for robotics, offering a method to optimize design parameters, though it appears incremental as it applies existing reinforcement learning techniques to a specific domain.
The authors tackled the problem of designing mechanical parameters for a flexible monopode jumping system by using reinforcement learning to learn both control strategies and mechanical parameters, demonstrating that the learned designs are optimal within the provided design space.
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.