Learning object-specific grasp affordance densities

2009 IEEE 8th International Conference on Development and Learning, ICDL 2009

Volumn , Issue , 2009, Pages

  Detry, R ^a   Baseski E ^b   Popovic M ^b   Touati, Y ^b   Kruger N ^b   Kroemer, O ^c   Peters, J ^c   Piater, J ^a  

^a UNIVERSITY OF LIÈGE   (Belgium)

^b UNIVERSITY OF SOUTHERN DENMARK   (Denmark)

^c MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

3D POSITIONS; ACHIEVABILITY; AFFORDANCES; EMPIRICAL DENSITY; LEARNING OBJECTS; OBJECT POSE; POSE ESTIMATION; POSE SPACE; REFERENCE FRAME; ROBOTIC AGENTS; VISUAL CUES; VISUAL MODEL; VISUAL OBJECTS;

EDUCATION; GRIPPERS; PROBABILITY DENSITY FUNCTION; THREE DIMENSIONAL;

GESTURE RECOGNITION;

EID: 71049163738     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/DEVLRN.2009.5175520     Document Type: Conference Paper

References (29)

1. Daniel Aarno, Johan Sommerfeld, Danica Kragic, Nicolas Pugeault, Sinan Kalkan, Florentin Wörgötter, Dirk Kraft, and Norbert Krüger. Early reactive grasping with second order 3D feature relations. In The IEEE International Conference on Advanced Robotics, 2007.
2. A. Bicchi and V. Kumar. Robotic grasping and contact: a review. Robotics and Automation, 2000. Proceedings. ICRA00. IEEE International Conference on, 1, 2000.
3. G. Biegelbauer and M. Vincze. Efficient 3D object detection by fitting superquadrics to range image data for robots object manipulation. In IEEE International Conference on Robotics and Automation, 2007.
4. Ch Borst, M. Fischer, and G. Hirzinger. Grasping the dice by dicing the grasp. In Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems. (IROS 2003)., volume 4, pages 3692-3697, 2003.
5. G. Bouchard and B. Triggs. Hierarchical part-based visual object categorization. In Computer Vision and Pattern Recognition, volume 1, pages 710-715, 2005.
6. C. de Granville and A. H. Fagg. Learning grasp affordances through human demonstration. Submitted to the Journal of Autonomous Robots, 2009.
7. Charles de Granville, Joshua Southerland, and Andrew H. Fagg. Learning grasp affordances through human demonstration. In Proceedings of the International Conference on Development and Learning (ICDL'06), 2006.
8. Renaud Detry, Nicolas Pugeault, and Justus Piater. A probabilistic framework for 3D visual object representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2009.
9. A. Doucet, N. de Freitas, and N. Gordon. Sequential Monte Carlo Methods in Practice. Springer, 2001.
10. Pedro F. Felzenszwalb and Daniel P. Huttenlocher. Efficient matching of pictorial structures. In Conference on Computer Vision and Pattern Recognition (CVPR 2000), pages 2066-, 2000.
11. James J. Gibson. The Ecological Approach to Visual Perception. Lawrence Erlbaum Associates, 1979.
12. R.I. Hartley and A. Zisserman. Multiple View Geometry in Computer Vision. Cambridge University Press, 2000.
13. D.H. Hubel and T.N. Wiesel. Anatomical demonstration of columns in the monkey striate cortex. Nature, 221:747-750, 1969.
14. K. Huebner, S. Ruthotto, and D. Kragic. Minimum volume bounding box decomposition for shape approximation in robot grasping. In Robotics and Automation, 2008. ICRA 2008. IEEE International Conference on, 2008.
15. D. Kraft, N. Pugeault, E. Başeski, M. Popović, D. Kragic, S. Kalkan, F.Wörgötter, and N. Krüger. Birth of the Object: Detection of Objectness and Extraction of Object Shape through Object Action Complexes. Special Issue on "Cognitive Humanoid Robots" of the International Journal of Humanoid Robotics, 2008. (accepted).
16. Danica Kragic, Andrew T. Miller, and Peter K. Allen. Real-time tracking meets online grasp planning. In Proceedings of the 2001 IEEE International Conference on Robotics and Automation, pages 2460-2465, 2001.
17. N. Krüger, M. Lappe, and F. Wörgötter. Biologically Motivated Multimodal Processing of Visual Primitives. The Interdisciplinary Journal of Artificial Intelligence and the Simulation of Behaviour, 1(5):417-428, 2004.
18. James Kuffner. Effective sampling and distance metrics for 3D rigid body path planning. In Proc. 2004 IEEE Intl Conf. on Robotics and Automation (ICRA 2004). IEEE, May 2004.
19. T. S. Lee and D. Mumford. Hierarchical Bayesian inference in the visual cortex. Journal of the Optical Society of America, pages 1434-1448, 7 2003.
20. K. V. Mardia and P. E. Jupp. Directional Statistics. Wiley Series in Probability and Statistics. Wiley, 1999.
21. A. T. Miller, S. Knoop, H. Christensen, and P. K. Allen. Automatic grasp planning using shape primitives. In Proceedings of the IEEE International Conference on Robotics and Automation, 2003, volume 2, pages 1824-1829, 2003.
22. Judea Pearl. Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference. Morgan Kaufmann, 1988.
23. Mila Popovic, Dirk Kraft, Leon Bodenhagen, Emre Baseski, Nicolas Pugeault, Danica Kragic, and Norbert Krüger. A strategy for grasping unknown objects based on co-planarity and colour information. Submitted to RAS.
24. Nicolas Pugeault. Early Cognitive Vision: Feedback Mechanisms for the Disambiguation of Early Visual Representation. Vdm Verlag Dr. Müller, 2008.
25. Mario Richtsfeld and Markus Vincze. Robotic grasping based on laser range and stereo data. In International Conference on Robotics and Automation, 2009.
26. B. W. Silverman. Density Estimation for Statistics and Data Analysis. Chapman & Hall/CRC, 1986
27. Alexander Stoytchev. Toward learning the binding affordances of objects: A behavior-grounded approach. In Proceedings of AAAI Symposium on Developmental Robotics, pages 17-22, Stanford University, Mar 21-23 2005.
28. Alexander Stoytchev. Learning the affordances of tools using a behaviorgrounded approach. In E. Rome et al., editors, Affordance-Based Robot Control, volume 4760 of Lecture Notes in Artificial Intelligence (LNAI), pages 140-158. Springer, Berlin / Heidelberg, 2008.
29. Erik B. Sudderth, Alexander T. Ihler, William T. Freeman, and Alan S. Willsky. Nonparametric belief propagation. In Computer Vision and Pattern Recognition, Los Alamitos, CA, USA, 2003. IEEE Computer Society.