Visual Perception and Reproduction for Imitative Learning of A Partner Robot

This paper proposes visual perception and model reproduction based on imitation of a partner robot interacting with a human. First of all, we discuss the role of imitation, and propose the method for imitative behavior generation. After the robot searches for a human by using a CCD camera, human hand positions are extracted from a series of images taken from the CCD camera. Next, the position sequence of the extracted human hand is used as inputs to a fuzzy spiking neural network to recognize the position sequence as a motion pattern. The trajectory for the robot behavior is generated and updated by a steady-state genetic algorithm based on the human motions pattern. Furthermore, a self-organizing map is used for clustering human hand motion patterns. Finally, we show experimental results of imitative behavior generation through interaction with a human. Key-words: Visual Perception, Partner Robots, Spiking Neural Network, Genetic Algorithm Proceedings of the 5th WSEAS International Conference on Signal Processing, Istanbul, Turkey, May 27-29, 2006 (pp76-81)