Learning tracking control with forward models

Proceedings - IEEE International Conference on Robotics and Automation

Volumn , Issue , 2012, Pages 259-264

  Bócsi, Botond ^a   Hennig, Philipp ^b   Csató, Lehel ^a   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

LEARNING SYSTEMS; MANIPULATORS; NAVIGATION; ROBOT APPLICATIONS; ROBOTS;

ADAPTIVE LEARNING ALGORITHM; GAUSSIAN PROCESSES; LOCAL OPTIMIZATIONS; PHYSICAL EXPERIMENTS; REDUNDANT ROBOT MANIPULATORS; SECOND ORDER OPTIMIZATION; TRACKING CONTROL ALGORITHMS; TRACKING CONTROLS;

LEARNING ALGORITHMS;

EID: 84864425385     PISSN: 10504729     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICRA.2012.6224831     Document Type: Conference Paper

References (21)

1. Botond Bócsi, Duy Nguyen-Tuong, Lehel Csató, Bernhard Schoelkopf, and Jan Peters. Learning inverse kinematics with structured prediction. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems, pages 698-703, San Francisco, USA, 2011.
2. John J. Craig. Introduction to Robotics: Mechanics and Control. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 2nd edition, 1989.
3. Lehel Csató and Manfred Opper. Sparse on-line Gaussian processes. Neural Computation, 14(3):641-668, 2002.
4. A. D'Souza, S. Vijayakumar, and S. Schaal. Learning inverse kinematics. In Proceedings of the IEEE International Conference on Intelligent Robots and Systems. Piscataway, NJ: IEEE, 2001.
5. Michael I. Jordan and David E. Rumelhart. Forward models: Supervised learning with a distal teacher. Cognitive Science, 16:307-354, 1992.
6. Neil D. Lawrence, Matthias Seeger, and Ralf Herbrich. Fast sparse Gaussian process methods: The informative vector machine. In Neural Information Processing Systems, pages 609-616. MIT Press, 2002.
7. Jun Nakanishi, Rick Cory, Michael Mistry, Jan Peters, and Stefan Schaal. Operational space control: a theoretical and emprical comparison. (6):737-757, 2008.
8. Duy Nguyen-Tuong and Jan Peters. Using model knowledge for learning inverse dynamics. In Proceedings of the IEEE International Conference on Robotics and Automation, pages 2677-2682, 2010.
9. Duy Nguyen-Tuong, Matthias W. Seeger, and Jan Peters. Model learning with local Gaussian process regression. Advanced Robotics, 23(15):2015-2034, 2009.
10. Manfred Opper. A Bayesian approach to on-line learning, pages 363-378. Cambridge University Press, 1998.
11. Eimei Oyama, Nak Young Chong, Arvin Agah, Taro Maeda, and Susumu Tachi. Inverse kinematics learning by modular architecture neural networks with performance prediction networks. In Proceedings of the IEEE International Conference on Robotics and Automation, pages 1006-1012, 2001.
12. Joaquin Quiñonero Candela and Carl E. Rasmussen. A unifying view of sparse approximate Gaussian process regression. Journal of Machine Learning Research, 6:1939-1959, 2005.
13. Carl E. Rasmussen and Christopher K. I. Williams. Gaussian Processes for Machine Learning. The MIT Press, 2005.
14. Camille Salaün, Vincent Padois, and Olivier Sigaud. Learning forward models for the operational space control of redundant robots. In Olivier Sigaud and Jan Peters, editors, From Motor Learning to Interaction Learning in Robots, volume 264 of Studies in Computational Intelligence, pages 169-192. Springer, 2010.
15. Bernhard Schölkopf and Alex Smola. Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. MITPress, Cambridge, MA, 2002.
16. Lorenzo Sciavicco and Bruno Siciliano. Modelling and Control of Robot Manipulators (Advanced Textbooks in Control and Signal Processing). Advanced textbooks in control and signal processing. Springer, 2nd edition, January 2005.
17. Edward Snelson and Zoubin Ghahramani. Sparse Gaussian processes using pseudo-inputs. In Advances in Neural Information Processing Systems, pages 1257-1264. MIT press, 2006.
18. Jan A. Snyman. Practical Mathematical Optimization: An Introduction to Basic Optimization Theory and Classical and New Gradient-Based Algorithms. Applied Optimization. Springer-Verlag New York, 2005.
19. M.W. Spong and M. Vidyasagar. Robot Dynamics And Control. Wiley India Pvt. Ltd., 2008.
20. Ganghua Sun and Brian Scassellati. Reaching through learned forward model. In Proceedings of the IEEE-RAS/RSJ International Conference on Humanoid Robots, Santa Monica, 2004.
21. Sethu Vijayakumar, Aaron D'Souza, and Stefan Schaal. Incremental online learning in high dimensions. Neural Computation, 17:2602-2634, 2005.