Learning of 2D Grasping Strategies from Box-Based 3D Object Approximations
2010 (English)In: Robotics: Science and Systems V, 2010Conference paper (Refereed)
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.
Place, publisher, year, edition, pages
Computer and Information Science
IdentifiersURN: urn:nbn:se:kth:diva-82456ISBN: 0-262-51463-XISBN: 978-0-262-51463-7OAI: oai:DiVA.org:kth-82456DiVA: diva2:498253
2009 Robotics: Science and Systems Conference, University of Washington, Seattle, USA, June 28 - July 1, 2009
QC 201202172012-02-112012-02-112012-02-17Bibliographically approved