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.

本研究介绍了AR3n ，一种采用强化学习来提供适应性辅助的控制器，用于机器人辅助手写康复任务。利用虚拟患者模型将AR3n推广到多个受试者。该控制器通过追踪误差实时调节机器人辅助力度，同时最小化机器人辅助量，并通过一系列模拟和人体试验进行实验验证。最后，与传统基于规则的控制器进行比较研究，分析两个控制器辅助机制的差异。

AR3n：基于强化学习的机器人康复辅助控制器