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：本文提出了一种名为AR3n（发音为Aaron）的按需辅助（AAN）控制器，该控制器利用强化学习在机器人辅助手写康复训练任务中提供自适应辅助。与以往的AAN控制器不同，我们的方法不依赖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使AR3n适用于多个受试者。该系统根据受试者的跟踪误差实时调节机器人辅助量，同时最小化机器人辅助的施加。通过一系列仿真实验和人体受试者实验对该控制器进行了实验验证。最后，与传统基于规则的控制器进行了对比研究，以分析两种控制器在辅助机制上的差异。