Applying Adaptive Actor-Critic Learning to Human Upper Lime Lifting Motion

Abstract

An adaptive reinforcement learning method designed to facilitate the on-line lifting motion of the human forearm is here proposed. Its purpose is to use the control based on the proposed learning method to perform the lifting motion. The learning algorithm is an actor-critic learning based on the neural network that used the normalized radial basis function. The paper shows a simulation of the motion of the forearm lifting process. As shown in the results, the forearm continues to lift from a horizontal position to a vertical position. During this process, both the state space and action space are continuous.