Learning inverse kinematics with structured prediction

IEEE International Conference on Intelligent Robots and Systems

Volumn , Issue , 2011, Pages 698-703

  Bócsi, Botond ^a   Nguyen Tuong, Duy ^b   Csató, Lehel ^a   Schölkopf, Bernhard ^b   Peters, Jan ^b,c  

^a BABE BOLYAI UNIVERSITY   (Romania)

^b MAX PLANCK INSTITUTE FOR INTELLIGENT SYSTEMS   (Germany)

^c DARMSTADT UNIVERSITY OF TECHNOLOGY   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

CONFIGURATION SPACE; DIRECT FUNCTIONS; GLOBAL CONSISTENCY; GLOBAL MODELS; GLOBAL SOLUTIONS; LEARNING INVERSE KINEMATICS; LEARNING METHODS; LOCAL EXPERTS; LOCAL MODEL; NONUNIQUENESS; REAL WORLD EXPERIMENT; STRUCTURED PREDICTION; TRACKING CONTROLS;

ALGORITHMS; INTELLIGENT ROBOTS; INVERSE PROBLEMS; ROBOTICS;

INVERSE KINEMATICS;

EID: 84455175120     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IROS.2011.6048552     Document Type: Conference Paper

References (20)

1. B. Siciliano, "Kinematic control of redundant robot manipulators: A tutorial," Journal of Intelligent and Robotic Systems, vol. 3, no. 3, pp. 201-212, 1990.
2. L. Sciavicco and B. Siciliano, Modelling and Control of Robot Manipulators (Advanced Textbooks in Control and Signal Processing), 2nd ed., ser. Advanced textbooks in control and signal processing. Springer, January 2005.
3. C. Salaün, V. Padois, and O. Sigaud, "Learning forward models for the operational space control of redundant robots," in From Motor Learning to Interaction Learning in Robots, 2010, pp. 169-192.
4. A. D'Souza, S. Vijayakumar, and S. Schaal, "Learning inverse kinematics,"in IEEE International Conference on Intelligent Robots and Systems (IROS 2001). Piscataway, NJ: IEEE, 2001.
5. V. R. de Angulo and C. Torras, "Learning inverse kinematics: Reduced sampling through decomposition into virtual robots," IEEE Transactions on Systems, Man, and Cybernetics, Part B, vol. 38, no. 6, pp. 1571-1577, 2008.
6. K. Neumann, M. Rolf, J. J. Steil, and M. Gienger, "Learning inverse kinematics for pose-constraint bi-manual movements," in SAB, 2010, pp. 478-488.
7. M. I. Jordan and D. E. Rumelhart, "Forward models: Supervised learning with a distal teacher," Cognitive Science, vol. 16, pp. 307-354, 1992.
8. G. Tevatia and S. Schaal, "Inverse kinematics for humanoid robots," in International Conference on Robotics and Automation (IRCA 2000), 2000, pp. 294-299.
9. E. O. Susumu and S. Tachi, "Inverse kinematics learning by modular architecture neural networks," in in Proc. IEEE International Conference on Robotics and Automation, 2001, 2001, pp. 1006-1012.
10. D. Demers and K. Kreutz-Delgado, "Learning global direct inverse kinematics," in Advances in Neural Information Processing Systems. Morgan Kaufmann, 1992, pp. 589-595.
11. I. Tsochantaridis, T. Joachims, T. Hofmann, and Y. Altun, "Large margin methods for structured and interdependent output variables."Journal of Machine Learning Research, vol. 6, pp. 1453-1484, 2005.
12. C. H. Lampert and M. B. Blaschko, "Structured prediction by joint kernel support estimation," Machine Learning, vol. 77, pp. 249-269, December 2009.
13. G. H. Bakir, T. Hofmann, B. Schölkopf, A. J. Smola, B. Taskar, and S. V. N. Vishwanathan, Eds., Predicting Structured Data, ser. Neural Information Processing. The MIT Press, September 2007.
14. A. McCallum and C. Sutton, "An introduction to conditional random fields for relational learning," in Introduction to Statistical Relational Learning, L. Getoor and B. Taskar, Eds. MIT Press, 2006.
15. B. Taskar, C. Guestrin, and D. Koller, "Max-margin Markov networks,"in Advances in Neural Information Processing Systems 16, S. Thrun, L. Saul, and B. Schölkopf, Eds. Cambridge, MA: MIT Press, 2004.
16. C. M. Bishop, Pattern Recognition and Machine Learning, 1st ed. Springer, October 2007.
17. B. Schölkopf and A. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. Cambridge, MA: MIT-Press, 2002.
18. B. Schölkopf, J. C. Platt, J. C. Shawe-Taylor, A. J. Smola, and R. C. Williamson, "Estimating the support of a high-dimensional distribution," Neural Computation, vol. 13, pp. 1443-1471, July 2001.
19. M. Avriel, Nonlinear Programming: Analysis and Methods. Dover Publishing, 2003.
20. C.-C. Chang and C.-J. Lin, LIBSVM: a library for support vector machines, 2001, software http://www.csie.ntu.edu.tw/~cjlin/libsvm.