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Acquisition of adaptive walking behaviors using machine learning with Central Pattern Generator

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3 Author(s)
T. Sato ; Faculty of Engineering, Hokkaido University, Japan ; K. Watanabe ; H. Igarashi

Recently, biologically inspired approaches have received much attention for robot control. A typical example of them is control of rhythmic behaviors by Central Pattern Generator (CPG). However, this control has a problem that there are few theories to determine parameters of CPG. For this reason, they are determined experimentally. In this paper, we propose a combination method of Genetic Algorithm and Reinforcement Learning for determining parameters of CPG, and apply to a quadruped robot with the CPG controller. Simulation results show that the robot obtains walking behaviors automatically through learning process without using the parameters set by knowledge of designers.

Published in:

The 2010 International Joint Conference on Neural Networks (IJCNN)

Date of Conference:

18-23 July 2010