DocumentCode :
1739763
Title :
Extracting logical perceptual space for robot learning using factor analysis
Author :
Fung, Wai-Keung ; Liu, Yun-Hui
Author_Institution :
Dept. of Autom. & Comput. Aided Eng., Chinese Univ. of Hong Kong, Shatin, China
Volume :
2
fYear :
2000
fDate :
2000
Firstpage :
873
Abstract :
Factor analysis has been employed for data analysis in behavioral sciences for decades. In this paper, we propose to employ it in robot behavior studies so that important underlying factors that affect the decision-making in robot behavior actions can be extracted. Causal relationships among physical (observed) and logical (unobserved) perceptual dimensions are constructed. Factor analysis provides a simple mean for us to understand what the sensors data, that construct the robot behavioral perceptual space S, are measuring (logical perceptual space extraction). Learning can thus be conducted based on the logical dimensions of the perceptual space, which usually has much lower dimensionality than the original physical perceptual space, of robot behaviors. Analysis of simulated obstacle avoidance behavior is presented
Keywords :
covariance matrices; feature extraction; learning (artificial intelligence); mobile robots; behavioral perceptual space; causal relationships; decision-making; factor analysis; logical perceptual space; logical perceptual space extraction; robot behavior; robot learning; simulated obstacle avoidance behavior; Computer aided engineering; Data analysis; Data mining; Extraterrestrial measurements; Infrared sensors; Orbital robotics; Robot kinematics; Robot sensing systems; Robotics and automation; Tactile sensors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Intelligent Robots and Systems, 2000. (IROS 2000). Proceedings. 2000 IEEE/RSJ International Conference on
Conference_Location :
Takamatsu
Print_ISBN :
0-7803-6348-5
Type :
conf
DOI :
10.1109/IROS.2000.893129
Filename :
893129
Link To Document :
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