English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
Add to Basket
  Local TagsRelease HistoryDetailsSummary

Released
Conference Paper

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

MPS-Authors
/persons/resource/persons83823

Boularias,  A
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons84027

Kroemer,  O
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

/persons/resource/persons84135

Peters,  J
Dept. Empirical Inference, Max Planck Institute for Intelligent Systems, Max Planck Society;

External Resource

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=6094888
(Publisher version)

Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)
There are no public fulltexts stored in PuRe
Supplementary Material (public)
There is no public supplementary material available
Citation

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.


Cite as: https://hdl.handle.net/11858/00-001M-0000-0013-BA54-7
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.