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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 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011) (pp. 1548-1553). Piscataway, NJ, USA: IEEE.

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 Urheber:
Boularias, A1, Autor           
Kroemer, O1, Autor           
Peters, J1, 2, Autor           
Amato, N.M., Herausgeber
Affiliations:
1Department Empirical Inference, Max Planck Institute for Biological Cybernetics, Max Planck Society, ou_1497795              
2Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society, ou_1497647              

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 Zusammenfassung: 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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 Datum: 2011-09
 Publikationsstatus: Erschienen
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 Identifikatoren: ISBN: 978-1-61284-454-1
URI: http://www.iros2011.org/
DOI: 10.1109/IROS.2011.6094888
BibTex Citekey: BoulariasKP2011_2
 Art des Abschluß: -

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Titel: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)
Veranstaltungsort: San Francisco, CA, USA
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Titel: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)
Genre der Quelle: Konferenzband
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Ort, Verlag, Ausgabe: Piscataway, NJ, USA : IEEE
Seiten: - Band / Heft: - Artikelnummer: - Start- / Endseite: 1548 - 1553 Identifikator: -