Learning robot grasping from 3-D images with Markov random fields

IEEE International Conference on Intelligent Robots and Systems

Volumn , Issue , 2011, Pages 1548-1553

  Boularias, Abdeslam ^a   Kroemer, Oliver ^a,b   Peters, Jan ^a,b  

^a MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS   (Germany)

^b DARMSTADT UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

3-D IMAGE; EMPIRICAL RESULTS; MARKOV RANDOM FIELD; MARKOV RANDOM FIELDS; PROBABILISTIC APPROACHES; REAL ROBOT; ROBOT GRASPING; SURFACE POINTS; UNSTRUCTURED ENVIRONMENTS;

ROBOTICS; THREE DIMENSIONAL;

INTELLIGENT ROBOTS;

EID: 84455203884     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IROS.2011.6048528     Document Type: Conference Paper

References (22)

1. Antonio Bicchi and Vijay Kumar. Robotic Grasping and Contact: A Review. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'00), 2000.
2. Karun Shimoga. Robot Grasp Synthesis Algorithms: A Survey. The International Journal of Robotics Research, 15:230-266, 1996.
3. Charles de Granville, Joshua Southerland, and Andrew Fagg. Learning Grasp Affordances Through Human Demonstration. In Proceedings of the International Conference on Development and Learning, 2006.
4. Ashutosh Saxena, Lawson Wong, and Andrew Ng. Learning grasp strategies with partial shape information. In Proceedings of the 23rd national conference on Artificial intelligence (AAAI'08).
5. Oliver Kroemer, Renaud Detry, Justus Piater, and Jan Peters. Combining Active Learning and Reactive Control for Robot Grasping. Robotics and Autonomous Systems, 58:1105-1116, 2010.
6. Sahar El-Khoury and Anis Sahbani. A New Strategy Combining Empirical and Analytical Approaches for Grasping Unknown 3D Objects. Robotics and Autonomous Systems, 58:497-507, 2010.
7. Andrew T. Miller, Steffen Knoop, Henrik I. Christensen, and Peter K. Allen. Automatic Grasp Planning Using Shape Primitives. In Proceedings of the 2003 IEEE international conference on Robotics and Automation (ICRA'03), pages 1824-1829, 2003.
8. Rafael Pelossof, Andrew Miller, Peter Allen, and Tony Jebara. An SVM Learning Approach to Robotic Grasping. In Proceedings of the 2004 IEEE international conference on Robotics and Automation (ICRA'04), pages 3512-3518.
9. Deepak Rao, Quoc V. Le, Thanathorn Phoka, Morgan Quigley, Attawith Sudsang, and Andrew Y. Ng. Grasping Novel Objects with Depth Segmentation. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS'10), 2010.
10. Yun Jiang Stephen Moseson and Ashutosh Saxena. Learning grasp strategies with partial shape information. In Proceedings of the 2011 IEEE international conference on Robotics and Automation (ICRA'11).
11. Jeannette Bohg and Danica Kragic. Learning Grasping Points with Shape Context. Robotics and Autonomous Systems, 58:362-377, 2010.
12. Dragomir Anguelov, Ben Taskar, Vassil Chatalbashev, Daphne Koller, Dinkar Gupta, Geremy Heitz, and Andrew Ng. Discriminative learning of Markov random fields for segmentation of 3d scan data. In Proceedings of the Conference on Computer Vision and Pattern Recognition (CVPR'05), pages 169-176, 2005.
13. Daniel Munoz, Nicolas Vandapel, and Martial Hebert. Onboard contextual classification of 3-D point clouds with learned high-order Markov random fields. In Proceedings of the 2009 IEEE international conference on Robotics and Automation (ICRA'09), 2009.
14. Pushmeet Kohli, Pawan Kumar, and Philip Torr. P3 and beyond: Solving energies with higher order cliques. In IEEE International Conference on Computer Vision and Pattern Recognition (ICCVPR'07), 2007.
15. Nathan Ratliff, Drew Bagnell, and Martin Zinkevich. Online subgradient methods for structured prediction. In In Eleventh International Conference on Artificial Intelligence and Statistics (AISTATS'07), 2007.
16. Venkat Chandrasekaran, Nathan Srebro, and Prahladh Harsha. Complexity of inference in graphical models. In Proceedings of the Proceedings of the Twenty-Fourth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI'08), pages 70-78, 2008.
17. Ben Taskar, Vassil Chatalbashev, and Daphne Koller. Learning associative markov networks. In Proceedings of the Twenty-First International Conference on Machine Learning (ICML'04), 2004.
18. Yasemin Altun, Alex J. Smola, and Thomas Hofmann. Exponential Families for Conditional Random Fields. In Proceedings of the 20th Annual Conference on Uncertainty in Artificial Intelligence (UAI'04), pages 2-9, 2004.
19. Ben Taskar. Learning Structured Prediction Models: A Large Margin Approach. PhD thesis, Stanford University, CA, USA, 2004.
20. Yuri Boykov, Olga Veksler, and Ramin Zabih. Fast Approximate Energy Minimization via Graph Cuts. IEEE Transactions on Pattern Analysis and Machine Intelligence, 23:2001, 1999.
21. Michael Kazhdan Philip Shilane, Patrick Min and Thomas Funkhouser. The Princeton Shape Benchmark. Shape Modeling International, Genova, Italy, June 2004. http://shape.cs.princeton.edu/benchmark/.
22. Andrew Miller and Peter K. Allen. Graspit!: A Versatile Simulator for Robotic Grasping. IEEE Robotics and Automation Magazine, 11:110-122, 2004. http://www.cs.columbia.edu/ cmatei/graspit/.