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In this paper, we present AR3n (pronounced as Aaron), an assist-as-needed(AAN) controller that utilizes reinforcement learning to supply adaptiveassistance during a robot assisted handwriting rehabilitation task. Unlikeprevious AAN controllers, our method does not rely on patient specificcontroller parameters or physical models. We propose the use of a virtualpatient model to generalize AR3n across multiple subjects. The system modulatesrobotic assistance in realtime based on a subject's tracking error, whileminimizing the amount of robotic assistance. The controller is experimentallyvalidated through a set of simulations and human subject experiments. Finally,a comparative study with a traditional rule-based controller is conducted toanalyze differences in assistance mechanisms of the two controllers.

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