Pilarski Patrick M, Dawson Michael R, Degris Thomas, Fahimi Farbod, Carey Jason P, Sutton Richard S
Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada.
IEEE Int Conf Rehabil Robot. 2011;2011:5975338. doi: 10.1109/ICORR.2011.5975338.
As a contribution toward the goal of adaptable, intelligent artificial limbs, this work introduces a continuous actor-critic reinforcement learning method for optimizing the control of multi-function myoelectric devices. Using a simulated upper-arm robotic prosthesis, we demonstrate how it is possible to derive successful limb controllers from myoelectric data using only a sparse human-delivered training signal, without requiring detailed knowledge about the task domain. This reinforcement-based machine learning framework is well suited for use by both patients and clinical staff, and may be easily adapted to different application domains and the needs of individual amputees. To our knowledge, this is the first my-oelectric control approach that facilitates the online learning of new amputee-specific motions based only on a one-dimensional (scalar) feedback signal provided by the user of the prosthesis.
作为实现适应性强、智能的人造肢体这一目标的一项贡献，本研究介绍了一种连续的演员-评论家强化学习方法，用于优化多功能肌电设备的控制。通过使用模拟的上臂机器人假肢，我们展示了如何仅使用稀疏的人工提供的训练信号，从肌电数据中导出成功的肢体控制器，而无需关于任务领域的详细知识。这种基于强化的机器学习框架非常适合患者和临床工作人员使用，并且可以轻松适应不同的应用领域和个体截肢者的需求。据我们所知，这是第一种仅基于假肢使用者提供的一维（标量）反馈信号，促进新的截肢者特定运动在线学习的肌电控制方法。
