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

2011

Conference Paper

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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.

Author(s): Boularias, A. and Kroemer, O. and Peters, J.
Pages: 1548-1553
Year: 2011
Month: September
Day: 0
Editors: Amato, N.M.
Publisher: IEEE

Department(s): Empirical Inference
Bibtex Type: Conference Paper (inproceedings)

DOI: 10.1109/IROS.2011.6094888
Event Name: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2011)
Event Place: San Francisco, CA, USA

Address: Piscataway, NJ, USA
Digital: 0
ISBN: 978-1-61284-454-1

Links: PDF
Web

BibTex

@inproceedings{BoulariasKP2011_2,
  title = {Learning robot grasping from 3-D images with Markov Random Fields},
  author = {Boularias, A. and Kroemer, O. and Peters, J.},
  pages = {1548-1553 },
  editors = {Amato, N.M.},
  publisher = {IEEE},
  address = {Piscataway, NJ, USA},
  month = sep,
  year = {2011},
  month_numeric = {9}
}