Control Pneumatic Soft Bending Actuator with Online Learning Pneumatic Physical Reservoir Computing

2025-03-20

AI Summary

Pneumatic soft bending actuators exhibit strong nonlinearity and modeling difficulty, rendering conventional control methods reliant on offline training and complex architectures. Method: This paper proposes a physics-based reservoir computing (PRC) framework for online adaptive control using a single pneumatic actuator. We introduce the first “single-model” pneumatic PRC architecture, embedding the physical reservoir directly into the closed-loop control circuit to enable zero-shot, offline-training-free online recursive least squares (RLS) learning. The approach integrates pneumatic dynamic modeling with nonlinear closed-loop design. Results: Validated in both simulation and physical experiments, the method reduces root-mean-square error (RMSE) of bending motion by over 37% compared to linear models, achieving high accuracy, low latency, and robust environmental adaptability. To our knowledge, this is the first demonstration of an end-to-end online learning paradigm where the soft actuator itself serves as its own controller.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Machine Learning: Learning on the Edge & Model Compression
• Search and Optimization: Learning to Search

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Economics, Online Markets and Human Computation: Economic ramifications for generative AI infrastructure and applications

Abstract

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.

Problem

• Control nonlinearities in soft robots using online learning.
• Utilize physical reservoir computing for soft actuator control.
• Enable zero-shot online learning for improved control performance.

Innovation

• Utilizes pneumatic soft actuator as PRC model
• Single RC model for compact control architecture
• Zero-shot online learning without offline training