Title :
Prediction of Human Behavior Patterns based on Spiking Neurons for A Partner Robot
Author :
Kubota, Naoyuki ; Nishida, Kenichiro
Author_Institution :
Dept. of Syst. Design, Tokyo Metropolitan Univ.
Abstract :
This paper discusses prediction of human behavior patterns for natural communication between a partner robot and a human. The prediction is very important to extract the perceptual information for the natural communication with a human in the future. Therefore we propose a prediction-based perceptual system based on spiking neurons. The proposed method is composed of four layers: the input layer, clustering layer, prediction layer, and perceptual module selection layer. In the clustering layer, an unsupervised learning method is used to perform the clustering of human behavior patterns. We use unsupervised learning because the human behavior patterns to be paid attention change by the other and the situation in communication. Furthermore, we show experimental results of the communication between a partner robot and a human based on our proposed method
Keywords :
bioelectric phenomena; man-machine systems; pattern recognition; robots; unsupervised learning; human behavior patterns prediction; natural communication; partner robot; prediction-based perceptual system; spiking neurons; unsupervised learning; Cognitive robotics; Data mining; Human robot interaction; Humanoid robots; Infrared sensors; Neurons; Predictive models; Robot sensing systems; Tactile sensors; Unsupervised learning;
Conference_Titel :
Robot and Human Interactive Communication, 2006. ROMAN 2006. The 15th IEEE International Symposium on
Conference_Location :
Hatfield
Print_ISBN :
1-4244-0564-5
Electronic_ISBN :
1-4244-0565-3
DOI :
10.1109/ROMAN.2006.314481