Control Pneumatic Soft Bending Actuator with Online Learning Pneumatic Physical Reservoir Computing
This provides a more efficient control method for soft robots by enabling zero-shot online learning with a compact architecture, though it is incremental as it builds on existing PRC approaches.
The paper tackled controlling a pneumatic soft bending actuator by introducing a physical reservoir computing (PRC) framework that uses the actuator's nonlinear dynamics for online learning, achieving over 37% reduction in RMSE compared to a linear model in bending motion tasks.
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.