Learning of Utterance Contents for A Partner Robot Based on the Environmental Perception

Abstract

The expectation to home partner robots has been increasing for the care of aged people and children. However, the communication system of partner robots is based on scenarios designed beforehand, which makes natural communication with people difficult. In this paper, we aim at realizing natural communication between people and robots by the learning of the relationship between perceived information and utterance contents. First, we propose a method based on spiking neural networks for the learning of the relationship. Next, we conducted several experiments on the learning of the relationship between perceived information and time-dependent utterance contents. The experimental results show that the robot can update utterance contents based on the cognitive development according to the interaction with the person.