Pixel-Based Behavior Learning
ECAI'02: Proceedings of the 15th European Conference on Artificial Intelligence(2002)
摘要
In this paper we address the problem of learning behaviors for autonomous mobile robots. We particularly focus on methods which enable a human user to train a robot in its real destination environment without giving an a-priori model. Using complex visual input typical of real situations in office environments we show that very simple visual features can be used to represent the perception/action relation specific to a given behavior. From this point we propose a learning model relying on a statistical collection of two-pixels features for representing a behavior. We then present the experiments made on a real robot and propose extensions of the model for active-perception and behavior selection.
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