Reservoir Computing for Torque-Restricted Pendulum Control

Abstract

Torque-restricted control remains a significant challenge in robotics, often necessitating precise modeling or large amounts of data for effective controller design. To address this problem, we introduce a novel training method that utilizes a Reservoir Computing (RC) framework to serve as a model-free controller that can effectively control a nonlinear robot using minimal training. This paper explores the application of the proposed framework to a torque-restricted single pendulum and achieves similar control performance to that of model-free reinforcement learning controllers while utilising just 0.5% of the data and a simple passive data collection method. We analyze 1,000 unique successful reservoir structures, examining their internal connectivity and memory properties, and identify key structural features that enhance control performance. Finally, this paper also explores our proposed controller’s robustness to changes in pendulum dimensionality and torque limit with successful control achieved for a large range of varying properties without any additional training.