AR3n: A Reinforcement Learning-based Assist-As-Needed Controller for Robotic Rehabilitation

• URL: http://arxiv.org/abs/2303.00085v4
• Date: Mon, 17 Apr 2023 03:11:45 GMT
• Title: AR3n: A Reinforcement Learning-based Assist-As-Needed Controller for Robotic Rehabilitation
• Authors: Shrey Pareek, Harris Nisar and Thenkurussi Kesavadas
• Abstract summary: We present AR3n, an assist-as-needed (AAN) controller that utilizes reinforcement learning to supply adaptive assistance during a robot assisted handwriting rehabilitation task. We propose the use of a virtual patient model to generalize AR3n across multiple subjects. The system modulates robotic assistance in realtime based on a subject's tracking error, while minimizing the amount of robotic assistance.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we present AR3n (pronounced as Aaron), an assist-as-needed (AAN) controller that utilizes reinforcement learning 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 realtime based on a subject's tracking error, 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 assistance mechanisms of the two controllers.

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