We present a system for learning the three DOF fine-positioning task of a robot manipulator (Puma 260) using a gripper mounted camera. Small lateral gripper-target misalignments are corrected in one step. Larger ones employ a previous coarse adjustment move in order to bound the parallax effects of the close camera focus. We build object-specialized, neural network-based pose estimators with a rather small set of Gabor filters. Gabor filters perform a spatially localized frequency analysis and resemble the spatial response profile of receptive fields found in visual cortex neurons. The system demonstrates efficiency w.r.t. speed and accuracy, as well as robustness against changing illumination and object conditions.