DocumentCode :
2772111
Title :
Reinforcement Learning for Platform-Independent Visual Robot Control
Author :
Muse, David ; Burn, Kevin ; Wermter, Stefan
Author_Institution :
Univ. of Sunderland, Sunderland
fYear :
0
fDate :
0-0 0
Firstpage :
2459
Lastpage :
2466
Abstract :
This paper proposes a new architecture for robot control. A test scenario is outlined to test the proposed system and enable a comparison with an existing system, which is able to fulfil the scenario and thus be used as a benchmark. The scenario is a navigation task, to allow a robot to approach a specified landmark. The proposed architecture will make use of two control units, one to allow a pan/tilt camera to track the landmark as the robot moves, and a second to control the robots drive motors. These units will be trained via reinforcement learning, and provide the potential for platform-independent robot control.
Keywords :
mobile robots; navigation; robot vision; navigation task; platform-independent robot control; platform-independent visual robot control; reinforcement learning; robots drive motors; Cameras; Control systems; Hybrid intelligent systems; Learning; Navigation; Paper technology; Robot control; Robot kinematics; Robot vision systems; System testing;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Neural Networks, 2006. IJCNN '06. International Joint Conference on
Conference_Location :
Vancouver, BC
Print_ISBN :
0-7803-9490-9
Type :
conf
DOI :
10.1109/IJCNN.2006.247094
Filename :
1716424
Link To Document :
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