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The paper describes the results of the evolutionary development of a real, neural-network driven mobile robot. The evolutionary approach tothe development of neural controllers for autonomous agents has been successfully used by many researchers, but most-if not all- studies have been carried out with computer simulations. Instead, in this research the whole evolutionary process takes places entirely on a real robot without human intervention. Although the experiments described here tackle a simple task of navigation and obstacle avoidance, we show a number of emergent phenomena that are characteristic of autonomous agents. The neural controllers of the evolved best individuals display a full exploitation of non-linear and recurrent connections that make them more e cient than analogous man-designed agents. In order to fully understand and describe the robot behavior, we have also employed quantitative ethological tools 13, and showed that the adaptation dynamics conform to predictions made for animals. 1
Floreano et al. (Sat,) studied this question.