Learning probabilistic discriminative models of grasp affordances under limited supervision

IEEE/RSJ 2010 International Conference on Intelligent Robots and Systems, IROS 2010 - Conference Proceedings

Volumn , Issue , 2010, Pages 1586-1591

  Erkan, Ayşe Naz ^a,c   Kroemer, Oliver ^a   Detry, Renaud ^b   Altun, Yasemin ^a   Piater, Justus ^b   Peters, Jan ^a  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

^b UNIVERSITY OF LIÈGE   (Belgium)

^c POLYTECHNIC UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

3D MODELS; ACHIEVABILITY; ACTIVE LEARNING; AFFORDANCES; CONDITIONAL PROBABILITIES; DESCRIPTORS; DISCRIMINATIVE MODELS; EXPERIMENTAL EVALUATION; HYPOTHESIS SPACE; KERNEL LOGISTIC REGRESSION; LABELED DATA; PROBABILISTIC MODELS; SEMI-SUPERVISED; SEMI-SUPERVISED LEARNING;

PROBABILITY DISTRIBUTIONS; SUPERVISED LEARNING; THREE DIMENSIONAL;

INTELLIGENT ROBOTS;

EID: 78651517016     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IROS.2010.5650088     Document Type: Conference Paper

References (24)

1. M. T. Mason and J. K. Salisbury, Manipulator grasping and pushing operations. MIT Press, 1985.
2. A. Bicchi and V. Kumar, "Robotic grasping and contact: a review," in IROS, 2000.
3. R. Detry, E. Baseski, M. Popovic, Y. Touati, N. Kruger, O. Kroemer, J. Peters, and J. Piater, "Learning object-specific grasp affordance densities," ICDL, vol. 0, pp. 1-7, 2009.
4. C. de Granville, J. Southerland, and A. H. Fagg, "Learning grasp affordances through human demonstration," in ICDL, 2006.
5. A. Saxena, J. Driemeyer, and A. Y. Ng, "Robotic grasping of novel objects using vision," The International Journal of Robotics Research, vol. 27, no. 2, pp. 157-173, 2008.
6. A. Saxena, L. Wong, and A. Y. Ng, "Learning grasp strategies with partial shape information," in AAAI, 2008.
7. C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and S tatistics). Springer, 2007.
8. J. Zhu and T. Hastie, "Kernel logistic regression and the import vector machine," in NIPS. MIT Press, 2001.
9. G. Bakir, J. Weston, and B. Schölkopf, "Learning to find pre-images," in NIPS. MIT Press, 2003.
10. N. Pugeault, F. Wörgötter, and N. Krüger, "Visual primitives: Local, condensed, and semantically rich visual descriptors and their applications in robotics," International Journal of Humanoid Robotics, 2010.
11. O. Chapelle, B. Schölkopf, and A. Zien, Eds., Semi-Supervised Learning. Cambridge, MA: MIT Press, 2006.
12. N. Krüger, M. Lappe, and F. Wörgötter, "Biologically motivated multimodal processing of visual primitives," Interdisciplinary Journal of Artificial Intelligence the Simulation of Behavious, AISB Journal, vol. 1(5), pp. 417-427, 2004.
13. N. Pugeault, Early Cognitive Vision: Feedback Mechanisms for the Disambiguation of Early Visual Representation. Verlag Dr. Muller, ISBN 978-3-639-09357-5, 2008.
14. J. J. Kuffner, "Effective sampling and distance metrics for 3D rigid body path planning," in ICRA, 2004, pp. 3993-3998.
15. R. Detry, N. Pugeault, and J. H. Piater, "A probabilistic framework for 3D visual object representation," IEEE Transactions on Pattern Analysis and Machine Intelligence, vol. 31, no. 10, pp. 1790-1803, 2009.
16. B. Schölkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond. The MIT Press, December 2001.
17. Y. Altun and A. J. Smola, "Unifying divergence minimization and statistical inference via convex duality," in COLT, 2006, pp. 139-153.
18. D. D. Lewis and J. Catlett, "Heterogeneous uncertainty sampling for supervised learning," in ICML, W. W. Cohen and H. Hirsh, Eds. New Brunswick, US: Morgan Kaufmann Publishers, San Francisco, US, 1994, pp. 148-156.
19. M. Salganicoff, L. H. Ungar, and R. Bajcsy, "Active learning for vision-based robot grasping," Machine Learning, vol. 23, no. 2-3, pp. 251-278, 1996.
20. L. Montesano and M. Lopes, "Learning object-specific grasp affordance densities," ICDL, 2009.
21. A. F. A. Morales, E. Chinellato and A. del Pobil, "An active learning approach for assessing robot grasp reliability," in IROS, 2004, pp. 485-491.
22. X. Zhu, J. Lafferty, and Z. Ghahramani, "Combining active learning and semi-supervised learning using gaussian fields and harmonic functions," in ICML 2003 Workshop on The Continuum from Labeled to Unlabeled Data in Machine Learning and Data Mining, 2003, pp. 58-65.
23. G. Tür, D. H. Tür, and R. Schapire, "Combining active and semi-supervised learning for spoken language understanding," Speech Communication, vol. 45(2), pp. 171-186, 2005.
24. D. Jurafsky and J. Martin, SPEECH and LANGUAGE PROCESSING An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. Prentice Hall, 2000.