Stabilization of Biped Robot based on Two mode Q-learning

Abstract

In this paper, two mode Q-learning, an extension of Q-learning, is used to stabilize the Zero Moment Point (ZMP) of a biped robot in the standing posture. In two mode Q-learning, the experiences of both success and failure of an agent are used for fast convergence. To demonstrate the effectiveness of two mode Q-learning against conventional Q-learning, the property of convergence is investigated through simulation in a grid world. This paper also presents the experimental results of the ZMP compensation in the standing posture of a biped robot. The external force generated from sagittal plane impacts to biped robot and the ZMP compensation scheme, based on two mode Q-learning, is employed. The effectiveness of two mode Q-learning is veri£ed by the use of real experiment.