AR3n: A Reinforcement Learning-based Assist-As-Needed Controller for Robotic Rehabilitation
This work addresses the need for more flexible and generalizable assist-as-needed controllers in robotic rehabilitation, offering a potential improvement over traditional methods, though it appears incremental as it builds on existing AAN concepts with a new learning approach.
The paper tackles the problem of providing adaptive robotic assistance in handwriting rehabilitation by introducing AR3n, a reinforcement learning-based controller that generalizes across subjects without patient-specific parameters, and it shows experimental validation through simulations and human tests with a comparative analysis against a rule-based controller.
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.