Robotics: Science and Systems V

Learning of 2D grasping strategies from box-based 3D object approximations

S. Geidenstam, K. Huebner, D. Banksell and D. Kragic


In this paper, we bridge and extend the approaches of 3D shape approximation and 2D grasping strategies. We begin by applying a shape decomposition to an object, i.e. its extracted 3D point data, using a flexible hierarchy of minimum volume bounding boxes. From this representation, we use the projections of points onto each of the valid faces as a basis for finding planar grasps. These grasp hypotheses are evaluated using a set of 2D and 3D heuristic quality measures. Finally on this set of quality measures, we use a neural network to learn good grasps and the relevance of each quality measure for a good grasp. We test and evaluate the algorithm in the GraspIt! simulator.



@INPROCEEDINGS{ Geidenstam-RSS-09,
    AUTHOR    = {S. Geidenstam AND K. Huebner AND D. Banksell AND D. Kragic},
    TITLE     = {Learning of 2{D} grasping strategies from box-based 3{D} object approximations},
    BOOKTITLE = {Proceedings of Robotics: Science and Systems},
    YEAR      = {2009},
    ADDRESS   = {Seattle, USA},
    MONTH     = {June},
    DOI       = {10.15607/RSS.2009.V.002}