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Controlling a Four Degree of Freedom Arm in 3D Using the XCSF Learning Classifier System

  • Patrick O. Stalph
  • Martin V. Butz
  • Gerulf K. M. Pedersen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5803)

Abstract

This paper shows for the first time that a Learning Classifier System, namely XCSF, can learn to control a realistic arm model with four degrees of freedom in a three-dimensional workspace. XCSF learns a locally linear approximation of the Jacobian of the arm kinematics, that is, it learns linear predictions of hand location changes given joint angle changes, where the predictions are conditioned on current joint angles. To control the arm, the linear mappings are inverted—deriving appropriate motor commands given desired hand movement directions. Due to the locally linear model, the inversely desired joint angle changes can be easily derived, while effectively resolving kinematic redundancies on the fly. Adaptive PD controllers are used to finally translate the desired joint angle changes into appropriate motor commands. This paper shows that XCSF scales to three dimensional workspaces. It reliably learns to control a four degree of freedom arm in a three dimensional work space accurately and effectively while flexibly incorporating additional task constraints.

Keywords

Learning Classifier Systems XCSF LWPR Autonomous Robot Control Dynamic Systems 

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References

  1. 1.
    Craig, J.J.: Introduction to Robotics: Mechanics and Control. Addison-Wesley Longman Publishing Co., Inc., Boston (1989)zbMATHGoogle Scholar
  2. 2.
    Baker, D.R., Wampler II., C.W.: On the inverse kinematics of redundant manipulators. The International Journal of Robotics Research 7(2), 3–21 (1988)CrossRefGoogle Scholar
  3. 3.
    Holland, J.H., Reitman, J.S.: Cognitive systems based on adaptive algorithms. SIGART Bull. 63(63), 49 (1977)CrossRefGoogle Scholar
  4. 4.
    Butz, M.V., Herbort, O.: Context-dependent predictions and cognitive arm control with XCSF. In: GECCO 2008: Proceedings of the 10th annual conference on Genetic and evolutionary computation, pp. 1357–1364. ACM, New York (2008)Google Scholar
  5. 5.
    Butz, M.V., Pedersen, G.K., Stalph, P.O.: Learning sensorimotor control structures with XCSF: Redundancy exploitation and dynamic control. In: GECCO 2009: Proceedings of the 11th annual conference on Genetic and evolutionary computation, pp. 1171–1178 (2009)Google Scholar
  6. 6.
    Wilson, S.W.: Classifiers that approximate functions. Natural Computing 1, 211–234 (2002)MathSciNetCrossRefzbMATHGoogle Scholar
  7. 7.
    Wilson, S.W.: Classifier fitness based on accuracy. Evolutionary Computation 3(2), 149–175 (1995)CrossRefGoogle Scholar
  8. 8.
    Butz, M.V., Lanzi, P.L., Wilson, S.W.: Function approximation with XCS: Hyperellipsoidal conditions, recursive least squares, and compaction. IEEE Transactions on Evolutionary Computation 12, 355–376 (2008)CrossRefGoogle Scholar
  9. 9.
    Lanzi, P.L., Loiacono, D., Wilson, S.W., Goldberg, D.E.: Prediction update algorithms for XCSF: RLS, kalman filter, and gain adaptation. In: GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp. 1505–1512. ACM, New York (2006)Google Scholar
  10. 10.
    Salaün, C., Padois, V., Sigaud, O.: Control of redundant robots using learned models: An operational space control approach. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (submitted)Google Scholar
  11. 11.
    Vijayakumar, S., D’Souza, A., Schaal, S.: Incremental online learning in high dimensions. Neural Computation 17, 2602–2634 (2005)MathSciNetCrossRefGoogle Scholar
  12. 12.
    Vijayakumar, S., Schaal, S.: Locally weighted projection regression: An O(n) algorithm for incremental real time learning in high dimensional space. In: ICML 2000: Proceedings of the Seventeenth International Conference on Machine Learning, pp. 1079–1086. Morgan Kaufmann Publishers Inc., San Francisco (2000)Google Scholar
  13. 13.
    Orriols-Puig, A., Bernadó-Mansilla, E.: Bounding XCS’s parameters for unbalanced datasets. In: GECCO 2006: Proceedings of the 8th annual conference on Genetic and evolutionary computation, pp. 1561–1568. ACM, New York (2006)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Patrick O. Stalph
    • 1
  • Martin V. Butz
    • 1
  • Gerulf K. M. Pedersen
    • 1
  1. 1.Department of Psychology IIIUniversity of WürzburgWürzburgGermany

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