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

Abstract:

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.

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Bibtex:

@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} 
}