DocumentCode :
342625
Title :
Evolving robot vision: increasing performance through shaping
Author :
Perkins, Simon
Author_Institution :
Los Alamos Nat. Lab., NM, USA
Volume :
1
fYear :
1999
fDate :
1999
Abstract :
Automated methods for designing robot controllers based on machine-learning techniques have shown great promise when applied to simple robot tasks, but in order to `scale up´ to more complicated problems they will require assistance from human experts, a process that is often called `robot shaping´. In this paper, the difficult problem of learning how to visually track moving objects is examined. It is shown that through the use of shaping techniques, this intractable learning problem can be made solvable. Controllers are evolved in simulation and then transferred to a real robot
Keywords :
controllers; evolutionary computation; learning (artificial intelligence); learning systems; robot vision; tracking; automated methods; human experts; intractable learning problem; machine-learning techniques; performance; robot controller design; robot shaping; robot vision evolution; simulation; visual moving object tracking; Automatic control; Humans; Learning systems; Machine learning; Neural networks; Robot control; Robot sensing systems; Robot vision systems; Robotics and automation; Shape control;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Evolutionary Computation, 1999. CEC 99. Proceedings of the 1999 Congress on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-5536-9
Type :
conf
DOI :
10.1109/CEC.1999.781950
Filename :
781950
Link To Document :
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