Acquisition of visually guided swing motion based on genetic algorithms and neural networks in two-armed bipedal robot

Abstract

We describe the method in which a visually guided swing motion for a 16 DOF two-armed bipedal robot is acquired by applying a GA (genetic algorithm) 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 time lags in a control system. It took about and hours for an artificial evolution to create a successfully individual after 50 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.