AR3n: A Reinforcement Learning-Based Assist-as-Needed Controller for Robotic Rehabilitation

• Published in: IEEE robotics & automation magazine Vol. 31; no. 3; pp. 74 - 82
• Main Authors: Pareek, Shrey, Nisar, Harris J, Kesavadas, Thenkurussi
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.09.2024; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptation models; Behavioral sciences; Comparative studies; Controllers; Handwriting; Handwriting recognition; Impedance; Patient rehabilitation; Rehabilitation; Rehabilitation robots; Reinforcement learning; Robot learning; Robots; Service robots; Tracking errors; Training; Virtual reality
• ISSN: 1070-9932; 1558-223X
• DOI: 10.1109/MRA.2023.3282434

In this article, we present AR3n (pronounced as Aaron), an assist-as-needed (AAN) controller that utilizes reinforcement learning (RL), to supply adaptive assistance during a robot-assisted handwriting rehabilitation task. Unlike previous AAN controllers, our method does not rely on patient-specific controller parameters or physical models. We propose the use of a virtual patient model to generalize AR3n across multiple subjects. The system modulates robotic assistance in real time based on a subject's tracking errors while minimizing the amount of robotic assistance. The controller is experimentally validated through a set of simulations and human subject experiments. Finally, a comparative study with a traditional rule-based controller is conducted to analyze differences in the assistance mechanisms of the two controllers.