Learning fine positioning of a robot manipulator based on Gabor wavelets

Abstract

A system for learning the pre-grasp positioning task for a robot manipulator is presented. The images delivered from a gripper mounted camera are analysed using Gabor filters which resemble the spatial response profiles of receptive fields found in visual cortex neurons. Using a quite small feature set, the system demonstrated efficiency with respect to speed and accuracy, as well as robustness against changing light conditions. Furthermore, we compare it to two other approaches, aiming at the same goal: an appearance-based PCA fuzzy control and a PSOM based Hough-Transform system.