Structured learning for partner robots based on natural communication

Abstract

This paper discusses the structured learning based on associative memory of 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 intentional interactions with a human. Therefore, we propose a method for associative learning based on intentional interaction 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 spatiotemporal patterns of human hand motions, and relationship among perceptual information. 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.