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.

翻译：在本文中,我们介绍AR3n(宣布为Aaron),这是一个需要的辅助(AAN)控制器,它利用强化学习,在机器人协助的笔迹修复任务期间提供适应性援助。与以前AAN控制器不同,我们的方法并不依赖病人特定的控制参数或物理模型。我们建议使用虚拟病人模型,将AR3n在多个科目上加以普及。系统根据对象的跟踪错误实时调节机器人协助,同时尽量减少机器人协助的数量。控制器通过一系列模拟和人类主题实验进行实验验证。最后,与传统的基于规则的控制器进行比较研究,分析两个控制器在援助机制上的差异。</s>