In this paper, we present a hybrid neural architecture to predict optical flow fields as consequences of real and hypothetical actions. In this architecture, we introduce a neural field-based method to fuse sensory bottom-up and predicted top-down expectations. All subsystems extensively use confidence estimations to reduce disturbances caused by noise. The facilities of this anticipative preprocessing can be demonstrated by means of an optical flow field based local navigation behavior of the miniature robot Khepera. Our anticipative preprocessing enables the robot to bridge gaps of sensory dropouts and, in consequence, to avoid collisions even with very noisy sensory information.