AR3n: A Reinforcement Learning-Based Assist-as-Needed Controller for Robotic Rehabilitation
Shrey Pareek, Harris Nisar, Thenkurussi Kesavadas
Abstract
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.