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，我们提出了AR3n（发音为Aaron），一个辅助为主（AAN）控制器，利用强化学习在机器人辅助手写康复任务中提供自适应辅助。与先前的AAN控制器不同，我们的方法不依赖于患者特定的控制器参数或物理模型。我们提出使用虚拟患者模型来在多个受试者中推广 AR3n。该系统根据受试者的跟踪误差实时调节机器人辅助，同时最小化机器人辅助的量。通过一系列模拟和人体实验验证了控制器。最后，进行与传统基于规则的控制器的比较研究，分析两种控制器的辅助机制之间的差异。