Title :
Structured learning for partner robots based on natural communication
Author :
Kubota, Naoyuki ; Yorita, Akihiro
Author_Institution :
Dept. of Syst. Design, Tokyo Metropolitan Univ., Hino
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.
Keywords :
face recognition; human-robot interaction; learning (artificial intelligence); object detection; associative learning; associative memory; conversation; hand motion; human face detection; human interaction; image processing; intentional interaction; natural communication; object detection; partner robots; spatiotemporal patterns; spiking neural networks; steady state genetic algorithm; structured learning; Associative memory; Face detection; Genetic algorithms; Human robot interaction; Image converters; Image processing; Neural networks; Object detection; Spatiotemporal phenomena; Steady-state; Associative Learning; Cognitive Development; Computational Intelligence; Partner Robots;
Conference_Titel :
Soft Computing in Industrial Applications, 2008. SMCia '08. IEEE Conference on
Print_ISBN :
978-1-4244-3782-5
Electronic_ISBN :
978-4-9904-2590-6
DOI :
10.1109/SMCIA.2008.5045979