Learning sensorimotor control structures with XCSF: Redundancy exploitation and dynamic control

Proceedings of the 11th Annual Genetic and Evolutionary Computation Conference, GECCO-2009

Volumn , Issue , 2009, Pages 1171-1178

  Butz, Martin V ^a   Pedersen, Gerulf K M ^a   Stalph, Patrick O ^a  

^a UNIVERSITY OF WÜRZBURG   (Germany)

Author keywords

Dynamic systems; Function approximation; Learning classifier systems; LWPR; Recursive least squares; XCS

Indexed keywords

ARM POSTURE; CLASSIFICATION ,; COMPLETE SYSTEM; DYNAMIC CONTROLS; DYNAMIC SYSTEMS; FUNCTION APPROXIMATION; INVERSE CONTROL; LEARNING CLASSIFIER SYSTEM; MOTOR COMMANDS; ON CURRENTS; ON THE FLIES; PD CONTROLLERS; PHYSICS ENGINE; RECURSIVE LEAST SQUARES; SENSORIMOTOR CONTROL; THREE DEGREES OF FREEDOM;

CLASSIFIERS; DYNAMIC PROGRAMMING; DYNAMICAL SYSTEMS; DYNAMICS; EDUCATION; LEARNING SYSTEMS; ONLINE SYSTEMS; QUALITY ASSURANCE; RECURSIVE FUNCTIONS; REDUNDANCY; REINFORCEMENT;

COGNITIVE SYSTEMS;

EID: 72749102221     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/1569901.1570059     Document Type: Conference Paper

References (17)

1. K. J. Åström and B. Wittenmark. Adaptive Control. Addison-Wesley, second edition, 1995.
2. M. V. Butz and O. Herbort. Context-dependent predictions and cognitive arm control with XCSF. Genetic and Evolutionary Computation Conference, GECCO 2008, pages 1357-1364, 2008.
3. M. V. Butz, O. Herbort, and J. Hoffmann. Exploiting redundancy for flexible behavior: Unsupervised learning in a modular sensorimotor control architecture. Psychological Review, 114:1015-1046, 2007.
4. M. V. Butz, P. L. Lanzi, and S. W. Wilson. Function approximation with XCS: Hyperellipsoidal conditions, recursive least squares, and compaction. IEEE Transactions on Evolutionary Computation, 12:355-376, 2008.
5. G. F. Franklin, J. D. Powell, and A. Emami-Naeini. Feedback Control of Dynamic Systems. Prentice Hall, Upper Saddle River, NJ, 4th edition, 2002.
6. M. S. A. Graziano. The organization of behavioral repertoire in motor cortex. Annual Review of Neuroscience, 29:105-134, 2006.
7. R. Kelly. PD control with desired gravity compensation of robotic manipulators: A review. The International Journal of Robotics Research, 16:660-672, 1997.
8. T. Kovacs. XCS classifier system reliably evolves accurate, complete, and minimal representations for boolean functions. In Roy, Chawdhry, and Pant, editors, Soft computing in engineering design and manufacturing, pages 59-68. Springer-Verlag, Berlin Heidelberg, 1997.
9. KUKA. Kuka kr16, 2008. KUKA Roboter GmbH, http://www.kuka.com/germany/de/ products/ industrial robots/low/kr16/.
10. P. L. Lanzi, D. Loiacono, S. W. Wilson, and D. E. Goldberg. Generalization in the XCSF classifier system: Analysis, improvement, and extension. Evolutionary Computation, 15:133-168, 2007.
11. P. L. Lanzi and S. W. Wilson. Using convex hulls to represent classifier conditions. Genetic and Evolutionary Computation Conference, GECCO 2006, pages 1481-1488, 2006.
12. C. Ltd. Webots reference manual, 2008. www.cyberbotics.com.
13. A. Orriols-Puig and E. Bernadó-Mansilla. Bounding XCS's parameters for unbalanced datasets. Genetic and Evolutionary Computation Conference, GECCO 2006, pages 1561-1568, 2006.
14. P. Stalph and M. V. Butz. Documentation of XCSF-Ellipsoids Java plus visualization. MEDAL Report 2008008, Missouri Estimation of Distribution Algorithms Laboratory, University of Missouri in St. Louis, MO, 2008.
15. S. W. Wilson. Classifier fitness based on accuracy. Evolutionary Computation, 3(2):149-175, 1995.
16. S. W. Wilson. Generalization in the XCS classifier system. Genetic Programming 1998: Proceedings of the Third Annual Conference, pages 665-674, 1998.
17. S. W. Wilson. Classifiers that approximate functions. Natural Computing, 1:211-234, 2002.