Associative Learning for Cognitive Development of Partner Robot through Interaction with People

Abstract

This paper discusses associative learning of a partner robots through interaction with people. Human interaction based on gestures is very important to realize the natural communication. The meaning of gestures can be understood through the actual interaction with a human and the imitation of a human. Therefore, we propose a method for associative learning based on imitation and conversation to realize the natural communication. Steady-state genetic algorithms are applied for detecting human face and objects in image processing. Spiking neural networks are applied for memorizing spatio-temporal patterns of human hand motions, and relationship among perceptual information. Furthermore, we conduct several experiments of the partner robot on the interaction based on imitation and conversation with people. The experimental results show that the proposed method can refine the relationship among the perceptual information, and can reflect the updated relationship to the natural communication with a human.