Adaptive Gait Acquisition Using Multi-Agent Learning for Wall Climbing Robots

Abstract

In this paper we present work in progress to examine the use of two machine learning techniques to determine the gait of a wall climbing robot. We describe the use of the genetic algorithm and then that of the reinforcement learning technique Q-learning, within a multiple-agent framework, for this task. We assert that there is one agent responsible for the control of each leg of the robot, where each agent is represented by a rule-based controller. It is shown that it is possible to use these techniques to control the gait of the basic robot.