Modular Fuzzy Neural Networks for Imitative Learning of A Partner Robot

Abstract

Imitation is a powerful tool for behavior learning and human communication. Basically, imitative learning is composed of model observation and model reproduction. This paper applies a spiking neural network and self-organizing map for model observation, and modular fuzzy neural networks and a steady-state genetic algorithm for model reproduction. The proposed method is applied for a partner robot interacting with a human. Experimental results show that the proposed method enables a robot to learn behaviors through imitation and can interact with a human efficiently.