Fast reinforcement learning for vision-guided mobile robots

Martinez-Marin, T. and Duckett, T. (2005) Fast reinforcement learning for vision-guided mobile robots. In: 2005 IEEE International Converence on Robotics and Automation: ICRA - 2005, 18 - 22 April 2005, Barcelona, Spain.

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Item Type:Conference or Workshop contribution (Paper)
Item Status:Live Archive


This paper presents a new reinforcement learning algorithm for accelerating acquisition of new skills by real mobile robots, without requiring simulation. It speeds up Q-learning by applying memory-based sweeping and enforcing the “adjoining property”, a technique that exploits the natural ordering of sensory state spaces in many robotic applications by only allowing transitions between neighbouring states. The algorithm is tested within an image-based visual servoing framework on a docking task, in which the robot has to position its gripper at a desired configuration relative to an object on a table. In experiments, we compare the performance of the new algorithm with a hand-designed linear controller and a scheme using the linear controller as a bias to further accelerate the learning. By analysis of the controllability and docking time, we show that the biased learner could improve on the performance of the linear controller, while requiring substantially lower training time than unbiased learning (less than 1 hour on the real robot).

Keywords:Robot Learning, Machine Learning, Mobile Robotics
Subjects:G Mathematical and Computer Sciences > G700 Artificial Intelligence
Divisions:College of Science > School of Computer Science
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ID Code:29077
Deposited On:06 Nov 2017 16:46

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