Abstract
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.
Abstract (translated)
在本文中,我们介绍了AR3n(发音为Aaron),一种需要时提供协助(AAN)控制器,它利用强化学习提供自适应协助,在机器人协助手写康复任务中执行。与以前的AAN控制器不同,我们的方法和方法不依赖于患者特定的控制器参数或物理模型。我们建议使用虚拟患者模型来泛化AR3n在不同受试者中的普及。系统实时基于一个受试者跟踪误差来调整机器人协助,同时最小化机器人协助的数量。控制器通过一组模拟和人类受试者实验进行了实验验证。最后,与传统的基于规则的控制器进行了比较研究,以分析两个控制器的协助机制之间的差异。
URL
https://arxiv.org/abs/2303.00085
