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AR3n（发音为Aaron），一种利用强化学习的按需辅助（AAN）控制器，用于机器人辅助手写康复任务中提供自适应辅助。与以往的AAN控制器不同，本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使AR3n能够泛化至多个受试者。系统根据受试者的跟踪误差实时调节机器人辅助力度，同时最小化辅助量。通过一系列仿真实验和人类受试者实验对该控制器进行了实验验证。最后，通过与传统基于规则的控制器的对比研究，分析两种控制器在辅助机制上的差异。