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

Journal of Systemics, Cybernetics and Informatics. 2009;7(2):29-34


Journal Homepage

Journal Title: Journal of Systemics, Cybernetics and Informatics

ISSN: 1690-4532 (Print); 1690-4524 (Online)

Publisher: International Institute of Informatics and Cybernetics

Society/Institution: HTML Web Page

LCC Subject Category: Technology: Technology (General): Industrial engineering. Management engineering: Information technology | Language and Literature: Philology. Linguistics: Communication. Mass media

Country of publisher: United States

Language of fulltext: English

Full-text formats available: PDF



Naoyuki Kubota


Double blind peer review

Editorial Board

Instructions for authors

Time From Submission to Publication: 12 weeks


Abstract | Full Text

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.