Abstract
We describe the method in which a visually guided swing motion for a 16DOF two-armed bipedal robot is acquired by applying GA (genetic algorithms) to a NN (neural network) controller. The evolutionary approach to the acquisition of various motions for robots has been successfully used by many researchers, but most studies have been carried out only through computer simulations. In this research, we adopt a real robot with a complicated body used in a noisy environment. The evolutionary processes are examined in a virtual world constructed on a CRS-CS6400 parallel computer which simulates such factors as swing dynamics, visual processes, noise reduction processes, and timelags in a control system. It took about 2 hours for an artificial evolution to create a successful individual after 50 alternations of generations from an initial population of 200 unsuccessful genes. Using the NN decoded from the most successful individual of the last generation, a real two-armed bipedal robot that could swing successfully was obtained.
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