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Neural anticipative architecture for expectation driven perception

In this paper we present a biologically inspired neural architecture for visual perception based on anticipation. The main goal of this work is to demonstrate, that anticipation is a central key to improve the perception performance of technical systems. The presented approach is able to increase the robustness of the perception process against noise or sensory dropouts. We demonstrate these perceptional improvements through our architecture at the level of local navigation behavior of the miniature robot Khepera. We claim that perception is not an end in itself. Instead it is a sensorimotor process integrating the generation of behavior.