Active learning of visual descriptors for grasping using non-parametric smoothed beta distributions

Robotics and Autonomous Systems

Volumn 60, Issue 3, 2012, Pages 452-462

  Montesano, Luis ^a   Lopes, Manuel ^b  

^a UNIVERSITY OF ZARAGOZA   (Spain)

^b INRIA   (France)

Author keywords

Active Learning; Grasping points; Learning through experience

Indexed keywords

3D RECONSTRUCTION; ACTIVE EXPLORATIONS; ACTIVE LEARNING; BETA DISTRIBUTIONS; DESCRIPTORS; FEATURE VECTORS; GRASPING POINTS; HUMANOID ROBOT; KERNEL APPROACHES; KINEMATIC CONSTRAINTS; LEARNING STRATEGY; LEARNING THROUGH EXPERIENCE; NON-PARAMETRIC; SINGLE IMAGES;

ALGORITHMS; ANTHROPOMORPHIC ROBOTS; FACE RECOGNITION; THREE DIMENSIONAL;

PROBABILITY DISTRIBUTIONS;

EID: 84856022793     PISSN: 09218890     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.robot.2011.07.013     Document Type: Article

References (47)

1. R.M. Murray, Z. Li, and S.S. Sastry A Mathematical Introduction to Robotic Manipulation 1994 CRC
2. A. Bicchi, and V. Kumar Robotic grasping and contact: a review Proc. IEEE International Conference on Robotics and Automation 1 2000 348 353
3. A. Bicchi On the closure properties of robotics grasping International Journal of Robotics Research IJRR 14 4 1995 319 334
4. M. Buss, H. Hashimoto, and J.B. Moore Dextrous hand grasping force optimization IEEE Transactions on Robotics and Automation 12 3 1996 406 418 (Pubitemid 126774409)
5. M. Cutkosky On grasp choice, grasp models, and the design of hands for manufacturingtasks IEEE Transactions on Robotics and Automation 5 3 1989 269 279
6. E. Oztop, N.S. Bradley, and M.A. Arbib Infant grasp learning: a computational model Expositions Brain Research 158 2004 480 503 (Pubitemid 39371393)
7. E. Oztop, M. Kawato, and M. Arbib Mirror neurons and imitation: a computationally guided review Neural Networks 19 3 2006 254 271 (Pubitemid 43728553)
8. M.E. McCarty, R.K. Clifton, D.H. Ashmead, P. Lee, and N. Goubet How infants use vision for grasping objects Child Development 72 4 2001 973 987 (Pubitemid 33654540)
9. A. Saxena, J. Driemeyer, and A.Y. Ng Robotic grasping of novel objects using vision International Journal of Robotics Research IJRR 2008
10. J. Piater, S. Jodogne, R. Detry, D. Kraft, N. Krger, O. Kroemer, and J. Peters Learning visual representations for perception-action systems International Journal of Robotics Research 2010
11. M. Popovic, D. Kraft, L. Bodenhagen, E. Baseski, N. Pugeault, D. Kragic, T. Asfour, and N. Kruger A strategy for grasping unknown objects based on co-planarity and colour information Robotics and Autonomous Systems 58 5 2010 551 565
12. L. Montesano, M. Lopes, A. Bernardino, and J. Santos-Victor Learning object affordances: from sensory-motor coordination to imitation IEEE Transactions on Robotics 24 1 2008 15 26 (Pubitemid 351411194)
13. L. Montesano, M. Lopes, Learning grasping affordances from local visual descriptors, in: IEEE 8TH International Conference on Development and Learning, China, 2009.
14. A. Morales, P.J. Sanz, A.P. del Pobil, Vision-based computation of three-finger grasps on unknown planar objects, in: IEEE/RSJ Intelligent Robots and Systems Conference, 2002.
15. J. Yan-Bin, On computing optimal planar grasps, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, vol. 3, Pittsburgh, PA, USA, 1995, pp. 427434.
16. A. Miller, S. Knoop, H.I. Christensen, P.K. Allen, Automatic grasp planning using shape primitives, in: IEEE International Conference on Robotics and Automation, vol. 2, 2003, pp. 18241829.
17. G. Taylor, L. Kleeman, Grasping unknown objects with a humanoid robot, in: Proc. 2002 Australasian Conference on Robotics and Automation, 2002, pp. 191196.
18. R. Horaud, F. Dornaika, and B. Espiau Visually guided object grasping IEEE Transactions on Robotics and Automation 14 1998 525 532 (Pubitemid 128743530)
19. J. Triesch, J. Wieghardt, E. Mal, and C.Y.D. Malsburg Towards imitation learning of grasping movements by an autonomous robot Proc. 3rd Gesture Workshop, GW'97 Lecture Notes in Artificial Intelligence 1999 Springer 73 84
20. M. Salganicoff, L.H. Ungar, and R. Bajcsy Active learning for vision-based robot grasping Machine Learning 23 2 1996
21. J. Pauli Learning to recognize and grasp objects Autonomous Robots 5 1998 239 258
22. T. Baier-Lowenstein, J. Zhang, Learning to grasp everyday objects using reinforcement-learning with automatic value cut-off, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2007, October 29 2007Nov. 2 2007, pp. 15511556.
23. J. Zhang, and B. Rssler Self-valuing learning and generalization with application in visually guided grasping of complex objects Robotics and Autonomous Systems 47 2004 117 127
24. A. Saxena, L. Wong, A. Ng, Learning grasp strategies with partial shape information, in: Proceedings of the 23rd National Conference on Artificial Intelligence, 2008, pp. 14911494.
25. N. Curtis, J. Xiao, Efficient and effective grasping of novel objects through learning and adapting a knowledge base, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, 2008.
26. J. Tegin, S. Ekvall, D. Kragic, J. Wikander, and B. Iliev Demonstration-based learning and control for automatic grasping Intelligent Service Robotics 2008
27. A. Morales, E. Chinellato, A. Fagg, A. del Pobil, An active learning approach for assessing robot grasp reliability, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2004, 2004.
28. O. Kroemer, R. Detry, J. Piater, and J. Peters Combining active learning and reactive control for robot grasping Robotics and Autonomous Systems 2010 1105 1116
29. J. Hilbe Logistic Regression Models 2009 Chapman & Hall/CRC Press
30. D.W. Hosmer, and S. Lemeshow Applied Logistic Regression 2000 John Wiley and Sons
31. A. Schein, and L.H. Ungar Active learning for logistic regression: an evaluation Machine Learning 68 2007 235 265 (Pubitemid 47312489)
32. T. Hastie, and T. Tibshirani Generalized Additive Models 1990 Chapman & Hall
33. J. Zhu, and T. Hastie Bayesian beta regression: applications to household expenditure data and genetic distance between foot-and-mouth disease viruses Journal of Computational and Graphical Statistics 2005
34. G. Schohn, D. Cohn, Less is more: active learning with support vector machines, in: Proceedings of the Seventeenth International Conference on Machine Learning, 2000.
35. S. Tong, and D. Koller Support vector machine active learning with applications to text classification Journal of Machine Learning Research 2 2001 45 66
36. M. Sugiyama, and S. Nakajima Pool-based agnostic experiment design in linear regression Machine Learning and Knowledge Discovery in Databases 2008 406 422
37. A. Krause, A. Singh, and C. Guestrin Near-optimal sensor placements in gaussian processes: theory, efficient algorithms and empirical studies Journal of Machine Learning Research 9 2008 235 284 (Pubitemid 351469021)
38. J. Bernardo, and A. Smith Bayesian Theory 2000 Wiley & Sons Chichester
39. B. Schlkopf, and A.J. Smola Learning with Kernels 2002 MIT
40. J. Nocedal, and S.J. Wright Numerical Optimization second ed. 2006 Springer-Verlag Berlin, New York
41. J. Mockus On bayesian methods for seeking the extremum Optimization Techniques IFIP TechnicalConference Novosibirs Lecture Notes in Computer Science 27 1975 400 404
42. P. Oudeyer, F. Kaplan, and V. Hafner Intrinsic motivation systems for autonomous mental development IEEE Transactions on Evolutionary Computation 11 2 2007 265 286 (Pubitemid 46547111)
43. A. Kapoor, K. Grauman, R. Urtasun, T. Darrell, Active learning with gaussian processes for object categorization, in: IEEE 11th International Conference on Computer Vision, 2007.
44. M. Lopes, R. Beira, M. Praa, J. Santos-Victor, An anthropomorphic robot torso for imitation: design and experiments, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS'04, Sendai, Japan 2004.
45. M. Fiala, ARTag, A fiducial marker system using digital techniques, National Research Council Canada, Tech. Report. Code at www.hitl.washington.edu/ artoolkit/, 2004.
46. F.S. Melo, M. Lopes, Learning from demonstration using mdp induced metrics, in: Proceedings of the 2010 European Conference on Machine Learning and Knowledge Discovery in Databases: Part II. ECML PKDD'10, 2010, pp. 385401.
47. M. Mason, M. Lopes, Robot self-initiative and personalization by learning through repeated interactions, in: 6th ACM/IEEE International Conference on Human-Robot Interaction, HRI'11, 2011.