Abstract
In our work on evolutionary robotics, two different approaches have been developed: simulation-based behavior learning with genetic algorithm and direct teaching with a learning classifier system in real environments. As the first approach, action-based environment recognition for an autonomous mobile robot. The robot moves in various environments by executing behaviors designed by a human designer and obtains different action sequences. These action sequences are automatically classified by self-organizing maps and the structure of the environment is identified from them. We also developed a GA-based behavior learning method in which a robot can learn suitable behaviors to recognize, and conducted simulation experiments to verify the learning ability. However, all the experiments has been done through only computer simulation. Thus we attempted to develop a direct teaching framework in which a real robot learned from human teacher using LCS in real and physical environments. Direct teaching means a human teacher gives adequate actions to a mobile robot by a GUI at work, and this teaching can accelerate a robot to learn classifiers. This framework is important to realize evolutionary robotics which can learn sufficiently fast in real environments, and we confirmed that it is useful by experimenting with a small mobile robot. In this chapter, we describe these two innovative approaches in evolutionary robotics in detail and discuss them in various points of view.
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Yamada, S., Katagami, D. (2009). Evolutionary Robotics: From Simulation-Based Behavior Learning to Direct Teaching in Real Environments. In: Liu, D., Wang, L., Tan, K.C. (eds) Design and Control of Intelligent Robotic Systems. Studies in Computational Intelligence, vol 177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-89933-4_4
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