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  Learning robot grasping from 3-D images with Markov Random Fields

Boularias, A., Kroemer, O., & Peters, J. (2011). Learning robot grasping from 3-D images with Markov Random Fields. In N. Amkato (Ed.), IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011) (pp. 1548-1553). Piscataway, NJ, USA: IEEE.

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Item Permalink: http://hdl.handle.net/11858/00-001M-0000-0013-BA54-7 Version Permalink: http://hdl.handle.net/21.11116/0000-0001-F2E9-7
Genre: Conference Paper

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 Creators:
Boularias, A1, Author              
Kroemer, O1, Author              
Peters, J1, Author              
Affiliations:
1Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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 Abstract: Learning to grasp novel objects is an essential skill for robots operating in unstructured environments. We therefore propose a probabilistic approach for learning to grasp. In particular, we learn a function that predicts the success probability of grasps performed on surface points of a given object. Our approach is based on Markov Random Fields (MRF), and motivated by the fact that points that are geometrically close to each other tend to have similar grasp success probabilities. The MRF approach is successfully tested in simulation, and on a real robot using 3-D scans of various types of objects. The empirical results show a significant improvement over methods that do not utilize the smoothness assumption and classify each point separately from the others.

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 Dates: 2011-09
 Publication Status: Published in print
 Pages: -
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 Rev. Type: -
 Identifiers: DOI: 10.1109/IROS.2011.6094888
BibTex Citekey: BoulariasKP2011_2
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Title: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)
Place of Event: San Francisco, CA, USA
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Title: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)
Source Genre: Proceedings
 Creator(s):
Amkato, NM, Editor
Affiliations:
-
Publ. Info: Piscataway, NJ, USA : IEEE
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 1548 - 1553 Identifier: ISBN: 978-1-61284-454-1