Koseki Shunsuke, Kutsuzawa Kyo, Owaki Dai, Hayashibe Mitsuhiro
Neuro-Robotics Lab, Department of Robotics, Graduate School of Engineering, Tohoku University, Sendai, Japan.
Front Neurorobot. 2023 Jan 23;16:1054239. doi: 10.3389/fnbot.2022.1054239. eCollection 2022.
Generating multimodal locomotion in underactuated bipedal robots requires control solutions that can facilitate motion patterns for drastically different dynamical modes, which is an extremely challenging problem in locomotion-learning tasks. Also, in such multimodal locomotion, utilizing body morphology is important because it leads to energy-efficient locomotion. This study provides a framework that reproduces multimodal bipedal locomotion using passive dynamics through deep reinforcement learning (DRL). An underactuated bipedal model was developed based on a passive walker, and a controller was designed using DRL. By carefully planning the weight parameter settings of the DRL reward function during the learning process based on a curriculum learning method, the bipedal model successfully learned to walk, run, and perform gait transitions by adjusting only one command input. These results indicate that DRL can be applied to generate various gaits with the effective use of passive dynamics.
在欠驱动双足机器人中生成多模态运动需要控制解决方案，该方案能够促进截然不同的动力学模式下的运动模式，这在运动学习任务中是一个极具挑战性的问题。此外，在这种多模态运动中，利用身体形态很重要，因为它能实现节能运动。本研究提供了一个框架，该框架通过深度强化学习（DRL）利用被动动力学来再现多模态双足运动。基于被动步行器开发了一个欠驱动双足模型，并使用DRL设计了一个控制器。通过基于课程学习方法在学习过程中精心规划DRL奖励函数的权重参数设置，双足模型成功学会了仅通过调整一个命令输入来行走、跑步和执行步态转换。这些结果表明，DRL可应用于通过有效利用被动动力学来生成各种步态。
