Optimizing Resolution for Feature Extraction in Robotic Motion Learning

Author

Kato, Masato ; Kobayashi, Yuichi ; Hosoe, Shigeyuki

Author_Institution

Nagoya Univ.

Volume

2

fYear

2005

fDate

12-12 Oct. 2005

Firstpage

1086

Lastpage

1091

Abstract

This paper presents a feature extraction method for robotic motion learning that optimizes image resolution to the task, thereby minimizing computation time. It utilizes mean-shift algorithms and principal component analysis for feature extraction, reinforcement learning for motion learning, and trial and error for finding the appropriate resolution. When applied to a manipulator pushing an object, the resolution adjustment method reduces the task time from one minute to 21 seconds

Keywords

feature extraction; image motion analysis; image recognition; image resolution; learning (artificial intelligence); manipulators; optimisation; principal component analysis; robot vision; feature extraction; image recognition; image resolution optimization; manipulator; mean-shift algorithm; principal component analysis; reinforcement learning; resolution adjustment method; robotic motion learning; Cognitive robotics; Computational efficiency; Education; Feature extraction; Humans; Image processing; Image recognition; Image resolution; Learning; Robot motion; Feature extraction; image recognition; reinforcement learning;

fLanguage

English

Publisher

ieee

Conference_Titel

Systems, Man and Cybernetics, 2005 IEEE International Conference on

Conference_Location

Waikoloa, HI

Print_ISBN

0-7803-9298-1

Type

conf

DOI

10.1109/ICSMC.2005.1571290

Filename

1571290