Integration of Visual Cues for Robotic Grasping

Abstract. In this paper, we propose a method that generates grasping actions for novel objects based on visual input from a stereo camera. We are integrating two methods that are advantageous either in predicting how to grasp an object or where to apply a grasp. The first one recon-structs a wire frame object model through curve matching. Elementary grasping actions can be associated to parts of this model. The second method predicts grasping points in a 2D contour image of an object. By integrating the information from the two approaches, we can gener-ate a sparse set of full grasp configurations that are of a good quality. We demonstrate our approach integrated in a vision system for complex shaped objects as well as in cluttered scenes.