Local Gaussian process regression for real-time model-based robot control

2008 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS

Volumn , Issue , 2008, Pages 380-385

  Nguyen Tuong, Duy ^a   Peters, Jan ^a  

^a MAX PLANCK INSTITUTE FOR BIOLOGICAL CYBERNETICS   (Germany)

Author keywords

[No Author keywords available]

Indexed keywords

ACCURATE DYNAMICS MODEL; COMPUTATIONAL COSTS; GAUSSIAN PROCESS REGRESSION; GAUSSIAN PROCESSES; LINEAR REGRESSION TECHNIQUES; LOCALLY WEIGHTED PROJECTION REGRESSIONS; MACHINE-LEARNING; MEASURED DATA; NON-LINEARITY; NON-PARAMETRIC REGRESSION; ONLINE LEARNING; REAL-TIME MODELS; REGRESSION METHOD; ROBOT CONTROLS; ROBOT DYNAMICS; ROBOT SYSTEM; SUPPORT VECTOR REGRESSIONS;

COMPUTATIONAL COMPLEXITY; E-LEARNING; GAUSSIAN DISTRIBUTION; GAUSSIAN NOISE (ELECTRONIC); INTELLIGENT ROBOTS; INTERNET; LINEAR REGRESSION; RADAR ANTENNAS; STANDARDS;

EDUCATION;

EID: 69549095956     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/IROS.2008.4650850     Document Type: Conference Paper

References (17)

1. S. Vijayakumar, A. D'Souza, and S. Schaal, "Incremental online learning in high dimensions," Neural Computation, 2005.
2. E. Snelson and Z. Ghahramani, "Local and global sparse gaussian process approximations," Artificial Intelligence and Statistics, 2007.
3. M. W. Spong, S. Hutchinson, and M. Vidyasagar, Robot Dynamics and Control. New York: John Wiley and Sons, 2006.
4. J. Nakanishi, J. A. Farrell, and S. Schaal, "Composite adaptive control with locally weighted statistical learning," Neural Networks, 2005.
5. E. Burdet, B. Sprenger, and A. Codourey, "Experiments in nonlinear adaptive control," International Conference on Robotics and Automation (ICRA), vol.1, pp. 537-542, 1997.
6. D. Nguyen-Tuong, J. Peters, and M. Seeger, "Computed torque control with nonparametric regression models," Proceedings of the 2008 American Control Conference (ACC 2008), 2008.
7. S. Schaal, C. G. Atkeson, and S. Vijayakumar, "Scalable techniques from nonparameteric statistics for real-time robot learning," Applied Intelligence, pp. 49-60, 2002. (Pubitemid 34789897)
8. C. E. Rasmussen and C. K. Williams, Gaussian Processes for Machine Learning. Massachusetts Institute of Technology: MIT-Press, 2006.
9. B. Schölkopf and A. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization and Beyond. Cambridge, MA: MIT-Press, 2002.
10. S. Schaal, "The SL simulation and real-time control software package," Tech. Rep., 2006. [Online]. Available: http://wwwclmc. usc.edu/publications/S/schaal-TRSL.pdf
11. J. Q. Candela and C. E. Rasmussen, "A unifying view of sparse approximate gaussian process regression," Journal of Machine Learning Research, 2005.
12. L. Csato and M. Opper, "Sparse online gaussian processes," Neural Computation, 2002.
13. D. H. Grollman and O. C. Jenkins, "Sparse incremental learning for interactive robot control policy estimation," in IEEE International Conference on Robotics and Automation, Pasadena, CA, USA, 2008.
14. C. E. Rasmussen and Z. Ghahramani, "Infinite mixtures of gaussian process experts," Advances in Neural Information Processing Systems, 2002.
15. S. Schaal, C. G. Atkeson, and S. Vijayakumar, "Real-time robot learning with locally weighted statistical learning," International Conference on Robotics and Automation, 2000.
16. C.-C. Chang and C.-J. Lin, LIBSVM: a library for support vector machines, 2001, http://www.csie.ntu.edu.tw/ cjlin/libsvm.
17. M. Seeger, LHOTSE: Toolbox for Adaptive Statistical Model, 2007, http://www.kyb.tuebingen.mpg.de/bs/people/seeger/lhotse/.