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Acquiring adaptive behaviors of robots automatically is one of the most interesting topics of the evolutionary systems. In previous works, we have developed an adaptive autonomous control method for redundant robots. The QDSEGA is one of the methods that we have proposed for them. The QDSEGA is realized by combining Q-learning and GA, and it can acquire suitable behaviors by adapting a movement of a robot for a task. In this paper, we focus on the adaptability of the QDSEGA and discuss the robustness of the autonomous redundant robot that is controlled by the QDSEGA. To demonstrate the effectiveness of the QDSEGA, simulations of obstacle avoidance by a 10-link manipulator in the changeable environment and locomotion by a 12-legged robot with failures have been carried out, and as a result, adaptive behaviors for each environment and each broken body have emerged.
learning (artificial intelligence)
Digital Object Identifier: 10.1109/CEC.2003.1299412
Published with permission from the copyright holder. this is the institute's copy, as published in Evolutionary Computation, 2003. CEC '03. The 2003 Congress on, 8-12 Dec. 2003, Volume 4, Pages 2572-2579.
Copyright © 2003 IEEE. All rights reserved.