Structural Optimization of Lightweight Bipedal Robot Via SERL

Designing a bipedal robot is a complex and challenging task, especially when dealing with a multitude of structural parameters. Traditional design methods often rely on human intuition and experience. However, such approaches are time-consuming, labor-intensive, lack theoretical guidance and hard to obtain optimal design results within vast design spaces, thus failing to full exploit the inherent performance potential of robots. In this context, this paper introduces the SERL (Structure Evolution Reinforcement Learning) algo- rithm, which combines reinforcement learning for locomotion tasks with evolution algorithms. The aim is to identify the op- timal parameter combinations within a given multidimensional design space. Through the SERL algorithm, we successfully * These authors contributed equally to this work. † corresponding author. Research supported by the National Natural Science Foundation of China under grants No.92248304 and Shenzhen Science Fund for Distinguished Young Scholars under Grant RCJC20210706091946001 1 Tsinghua University, 100084 Beijing, China 2 Jianghuai Advanced Technology Center, 230000 Hefei, China 3 Shanghai University, 200444 Shanghai, China. 4 University of Michigan, Ann Arbor, MI 48109, USA designed a bipedal robot named Wow Orin, where the optimal leg length are obtained through optimization based on body structure and motor torque. We have experimentally validated the effectiveness of the SERL algorithm, which is capable of optimizing the best structure within specified design space and task conditions. Additionally, to assess the performance gap between our designed robot and the current state-of-the-art robots, we compared Wow Orin with mainstream bipedal robots Cassie and Unitree H1. A series of experimental results demon- strate the Outstanding energy efficiency and performance of Wow Orin, further validating the feasibility of applying the SERL algorithm to practical design.