The intrinsic nonlinearities of soft robots present significant control but simultaneously provide them with rich computational potential. Reservoir computing (RC) has shown effectiveness in online learning systems for controlling nonlinear systems such as soft actuators. Conventional RC can be extended into physical reservoir computing (PRC) by leveraging the nonlinear dynamics of soft actuators for computation. This paper introduces a PRC-based online learning framework to control the motion of a pneumatic soft bending actuator, utilizing another pneumatic soft actuator as the PRC model. Unlike conventional designs requiring two RC models, the proposed control system employs a more compact architecture with a single RC model. Additionally, the framework enables zero-shot online learning, addressing limitations of previous PRC-based control systems reliant on offline training. Simulations and experiments validated the performance of the proposed system. Experimental results indicate that the PRC model achieved superior control performance compared to a linear model, reducing the root-mean-square error (RMSE) by an average of over 37% in bending motion control tasks. The proposed PRC-based online learning control framework provides a novel approach for harnessing physical systems' inherent nonlinearities to enhance the control of soft actuators.

翻译：软体机器人固有的非线性特性带来了显著的控制挑战，但同时赋予了它们丰富的计算潜力。储层计算（RC）已在控制非线性系统（如软体驱动器）的在线学习系统中展现出有效性。通过利用软体驱动器的非线性动力学进行计算，传统RC可扩展为物理储层计算（PRC）。本文提出了一种基于PRC的在线学习框架，用于控制气动软体弯曲驱动器的运动，该框架利用另一个气动软体驱动器作为PRC模型。与需要两个RC模型的传统设计不同，所提出的控制系统采用更紧凑的单RC模型架构。此外，该框架支持零样本在线学习，解决了先前基于PRC的控制系统依赖离线训练的局限性。仿真和实验验证了所提出系统的性能。实验结果表明，在弯曲运动控制任务中，PRC模型相比线性模型实现了更优的控制性能，其均方根误差（RMSE）平均降低了超过37%。所提出的基于PRC的在线学习控制框架为利用物理系统固有的非线性特性以增强软体驱动器的控制提供了一种新方法。