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Rapid Concept Learning for Mobile Robots
Authors:Sridhar Mahadevan  Georgios Theocharous  Nikfar Khaleeli
Affiliation:(1) Department of Computer Science, Michigan State University, East Lansing, MI, 48864;(2) Wind River Systems, Alameda, CA, 94501
Abstract:Concept learning in robotics is an extremely challenging problem: sensory data is often high dimensional, and noisy due to specularities and other irregularities. In this paper, we investigate two general strategies to speed up learning, based on spatial decomposition of the sensory representation, and simultaneous learning of multiple classes using a shared structure. We study two concept learning scenarios: a hallway navigation problem, where the robot has to induce features such as ldquoopeningrdquo or ldquowallrdquo. The second task is recycling, where the robot has to learn to recognize objects, such as a ldquotrash canrdquo. We use a common underlying function approximator in both studies in the form of a feedforward neural network, with several hundred input units and multiple output units. Despite the high degree of freedom afforded by such an approximator, we show the two strategies provide sufficient bias to achieve rapid learning. We provide detailed experimental studies on an actual mobile robot called PAVLOV to illustrate the effectiveness of this approach.
Keywords:robot learning  concept learning  neural networks
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