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- W4381785377 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." @default.
- W4381785377 created "2023-06-24" @default.
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- W4381785377 date "2023-01-01" @default.
- W4381785377 modified "2023-10-10" @default.
- W4381785377 title "AR3n: A Reinforcement Learning-Based Assist-as-Needed Controller for Robotic Rehabilitation" @default.
- W4381785377 doi "https://doi.org/10.1109/mra.2023.3282434" @default.
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