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Learning of 2D grasping strategies from box-based 3D object approximations
KTH, School of Computer Science and Communication (CSC), Computer Vision and Active Perception, CVAP.ORCID iD: 0000-0003-2965-2953
2010 (English)In: Robotics: Science and Systems, MIT Press, 2010, 9-16 p.Conference paper, Published paper (Refereed)
Resource type
Text
Abstract [en]

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
MIT Press, 2010. 9-16 p.
National Category
Electrical Engineering, Electronic Engineering, Information Engineering Computer and Information Science
Identifiers
URN: urn:nbn:se:kth:diva-187601Scopus ID: 2-s2.0-84959451152ISBN: 9780262514637 (print)OAI: oai:DiVA.org:kth-187601DiVA: diva2:933876
Conference
International Conference on Robotics Science and Systems, RSS 2009, 28 June 2009 through 1 July 2009
Note

QC 20160607

Available from: 2016-06-07 Created: 2016-05-25 Last updated: 2016-12-21Bibliographically approved

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Scopushttp://www.roboticsproceedings.org/rss05/p2.pdf

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Citation style
  • apa
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  • vancouver
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Language
  • de-DE
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Output format
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