Graduation School of Informatics, Kyoto University, Yoshida-honmachi, Sakyo-ku, Kyoto, 606-8501, Japan.
Graduate School of Information Science and Technology, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-8654, Japan.
Adv Sci (Weinh). 2024 Jul;11(25):e2304402. doi: 10.1002/advs.202304402. Epub 2024 Apr 19.
Harnessing complex body dynamics has long been a challenge in robotics, particularly when dealing with soft dynamics, which exhibit high complexity in interacting with the environment. Recent studies indicate that these dynamics can be used as a computational resource, exemplified by the McKibben pneumatic artificial muscle, a common soft actuator. This study demonstrates that bifurcations, including periodic and chaotic dynamics, can be embedded into the pneumatic artificial muscle, with the entire bifurcation structure using the framework of physical reservoir computing. These results suggest that dynamics not present in training data can be embedded through bifurcation embedment, implying the capability to incorporate various qualitatively different patterns into pneumatic artificial muscle without the need to design and learn all required patterns explicitly. Thus, this study introduces a novel approach to simplify robotic devices and control training by reducing reliance on external pattern generators and the amount and types of training data needed for control.
长期以来，复杂的身体动力学一直是机器人学中的一个挑战，特别是在处理软动力学时，软动力学在与环境相互作用时表现出高度的复杂性。最近的研究表明，这些动力学可以被用作一种计算资源，以 McKibben 气动人工肌肉为例，它是一种常见的软执行器。本研究表明，分叉，包括周期和混沌动力学，可以被嵌入到气动人工肌肉中，整个分叉结构使用物理储层计算的框架。这些结果表明，未在训练数据中出现的动力学可以通过分叉嵌入来嵌入，这意味着可以将各种定性不同的模式嵌入到气动人工肌肉中，而无需明确设计和学习所有所需的模式。因此，本研究通过减少对外部模式发生器的依赖以及控制所需的训练数据的数量和类型，引入了一种简化机器人设备和控制训练的新方法。
