The … Yale Workshop on Adaptive and Learning Systems
Clinical tests have shown that the dynamics of a human arm, controlled using Functional Electrical Stimulation (FES), can vary significantly between and during trials. In this paper, we study the application of Reinforcement Learning to create a controller that can adapt to these
changing dynamics of a human arm. Development and tests were done in simulation using a two-dimensional arm model and Hill-based muscle dynamics. An actor-critic architecture is used with artificial neural networks for both the actor and the critic. We begin by training it using a Proportional Derivative (PD) controller as a supervisor. We then make clinically relevant changes
to the dynamics of the arm and test the actor-critic’s ability to adapt without supervision in a reasonable number of episodes.
Thomas, Philip S.; Branicky, Michael; van den Bogert, Antonie; and Jagodnik, Kathleen, "Creating a Reinforcement Learning Controller for Functional Electrical Stimulation of a Human Arm" (2008). Mechanical Engineering Faculty Publications. 413.