Advertisement

A Two-Level Model of Anticipation-Based Motor Learning for Whole Body Motion

  • Camille Salaün
  • Vincent Padois
  • Olivier Sigaud
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 5499)

Abstract

We present a model of motor learning based on a combination of Operational Space Control and Optimal Control. Anticipatory processes are used both in the learning of the dynamics model of the system and in the coordination between both types of control. In order to illustrate the proposed model and associated control method, we apply these principles to the control of a simplified virtual humanoid performing a stand-up task starting from a crouching posture.

Keywords

Linear Quadratic Regulator Joint Velocity High Level Control Proportional Controller Inverse Dynamic Model 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Bernstein, N.: The Co-ordination and Regulation of Movements. Pergamo, Oxford (1967)Google Scholar
  2. 2.
    Flash, T., Hogan, N.: The Coordination of Arm Movements: An Experimentally Confirmed Mathematical Model. Journal of Neuroscience 5(7), 1688–1703 (1985)Google Scholar
  3. 3.
    Uno, Y., Kawato, M., Suzuki, R.: Formation and control of optimal trajectory in human multijoint arm movement. Biological Cybernetics 61(2), 89–101 (1989)CrossRefGoogle Scholar
  4. 4.
    Kawato, M.: Optimization and learning in neural networks for formation and control of coordinated movement. In: Attention and performance XIV (silver jubilee volume): synergies in experimental psychology, artificial intelligence, and cognitive neuroscience, pp. 821–849. MIT Press, Cambridge (1993)Google Scholar
  5. 5.
    Nakano, E., Imamizu, H., Osu, R., Uno, Y., Gomi, H., Yoshioka, T., Kawato, M.: Quantitative examinations of internal representations for arm trajectory planning: Minimum commanded torque change model. Journal of Neurophysiology 81(5), 2140–2155 (1999)Google Scholar
  6. 6.
    Harris, C.M., Wolpert, D.M.: Signal-dependent noise determines motor planning. Nature 394, 780–784 (1998)CrossRefGoogle Scholar
  7. 7.
    Fitts, P.M.: The information capacity of the human motor system in controlling the amplitude of movement. Journal of Experimental Psychology 47(6), 381–391 (1954)CrossRefGoogle Scholar
  8. 8.
    Todorov, E., Jordan, M.: A minimal intervention principle for coordinated movement. In: NIPS, pp. 27–34 (2003)Google Scholar
  9. 9.
    Scholz, J.P., Schöner, G.: The uncontrolled manifold concept: identifying control variables for a functional task. Experimental Brain Research 126(3), 289–306 (1999)CrossRefGoogle Scholar
  10. 10.
    Todorov, E., Jordan, M.I.: Optimal feedback control as a theory of motor coordination. Nature Neurosciences 5(11), 1226–1235 (2002)CrossRefGoogle Scholar
  11. 11.
    Todorov, E.: Optimality principles in sensorimotor control. Nature Neurosciences 7(9), 907–915 (2004)CrossRefGoogle Scholar
  12. 12.
    Guigon, E., Baraduc, P., Desmurget, M.: Computational motor control: Redudancy and invariance. Journal of Neurophysiology 97(1), 331–347 (2007)CrossRefGoogle Scholar
  13. 13.
    Guigon, E., Baraduc, P., Desmurget, M.: Optimality, stochasticity and variability in motor behavior. Journal of Computational Neuroscience 24(1), 57–68 (2008)CrossRefGoogle Scholar
  14. 14.
    Guigon, E., Baraduc, P., Desmurget, M.: Computational motor control: Feedback and accuracy. European Journal of Neuroscience 27(4), 1003–1016 (2008)CrossRefGoogle Scholar
  15. 15.
    Miyamoto, H., Wolpert, D.M., Kawato, M.: Computing the optimal trajectory of arm movement: the TOPS (task optimization in the presence of signal-dependent noise) model. In: Biologically inspired robot behavior engineering, pp. 395–415. Physica-Verlag GmbH, Germany (2003)CrossRefGoogle Scholar
  16. 16.
    Wolpert, D.M., Ghahramani, Z.: Computational principles of movement neuroscience. Nature Neuroscience 3, 1212–1217 (2000)CrossRefGoogle Scholar
  17. 17.
    Wolpert, D.M., Kawato, M.: Multiple paired forward and inverse models for motor control. Neural Networks 11(7-8), 1317–1329 (1998)CrossRefGoogle Scholar
  18. 18.
    Davidson, P.R., Wolpert, D.M.: Widespread access to predictive models in the motor system: a short review. Journal of Neural Engineering 2(3), S313–S319 (2005)CrossRefGoogle Scholar
  19. 19.
    Haruno, M., Wolpert, D.M., Kawato, M.: MOSAIC model for sensorimotor learning and control. Neural Computation 13(10), 2201–2220 (2001)CrossRefzbMATHGoogle Scholar
  20. 20.
    Doya, K., Samejima, K., Katagiri, K., Kawato, M.: Multiple model-based reinforcement learning. Neural Computation 14(6), 1347–1369 (2002)CrossRefzbMATHGoogle Scholar
  21. 21.
    Shadmehr, R., Wise, S.: The Computational Neurobiology of Reaching and Pointing. MIT Press, Cambridge (2005)Google Scholar
  22. 22.
    Khatib, O.: A unified approach for motion and force control of robot manipulators: The operational space formulation. IEEE Journal of Robotics and Automation 3(1), 43–53 (1987)CrossRefGoogle Scholar
  23. 23.
    Sentis, L., Khatib, O.: Control of free-floating humanoid robots through task prioritization. In: IEEE Conference on Robotics and Automation (ICRA), pp. 1718–1723 (April 2005)Google Scholar
  24. 24.
    Chiaverini, S.: Singularity-robust task-priority redundancy resolution for real-time kinematic control of robot manipulators. IEEE Transactions on Robotics and Automation 13(3), 398–410 (1997)CrossRefGoogle Scholar
  25. 25.
    Barthlemy, S., Bidaud, P.: Stability measure of postural dynamic equilibrium based on residual radius. In: RoManSy 2008: 17th CISM-IFToMM Symposium on Robot Design, Dynamics and Control (2008)Google Scholar
  26. 26.
    Vijayakumar, S., DSouza, A., Schaal, S.: LWPR: A scalable method for incremental online learning in high dimensions. Technical report. Press of University of Edinburgh, Edinburgh (2005)Google Scholar
  27. 27.
    Golub, G.H., Van Loan, C.F.: Matrix Computations. Johns Hopkins University Press, Baltimore (1996)zbMATHGoogle Scholar
  28. 28.
    Potts, D., Sammut, C.: Incremental learning of linear model trees. Machine Learning 61(1-3), 5–48 (2005)CrossRefzbMATHGoogle Scholar
  29. 29.
    Sun, G., Scassellati, B.: A fast and efficient model of learning to reach. International Journal of Humanoid Robotics 2(4), 391–414 (2005)CrossRefGoogle Scholar
  30. 30.
    Vijayakumar, S., Schaal, S.: Local dimensionality reduction for locally weighted learning. In: IEEE International Symposium on Computational Intelligence in Robotics and Automation, pp. 220–225 (1997)Google Scholar
  31. 31.
    Tenenhaus, M.: La régression PLS: théorie et pratique. Editions Technip (1998)Google Scholar
  32. 32.
    D’Souza, A., Vijayakumar, S., Schaal, S.: Learning inverse kinematics. In: Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), vol. 1, pp. 298–303 (2001)Google Scholar
  33. 33.
    Wieber, P.B., Billet, F., Boissieux, L., Pissard-Gibollet, R.: The HuMAnS toolbox, a homogeneous framework for motion capture, analysis and simulation. In: Proceedings of the ninth ISB Symposium on 3D analysis of human movement, Valenciennes, France. Academic, San Diego (2006)Google Scholar
  34. 34.
    Sastry, S., Bodson, M., Bartram, J.F.: Adaptive control: Stability, convergence, and robustness. The Journal of the Acoustical Society of America 88, 588 (1990)CrossRefGoogle Scholar
  35. 35.
    Siciliano, B., Khatib, O.: Springer Handbook of Robotics. Springer, New York (2007)zbMATHGoogle Scholar
  36. 36.
    Mitrovic, D., Klanke, S., Vijayakumar, S.: Adaptive optimal control for redundantly actuated arms. In: Asada, M., Hallam, J.C.T., Meyer, J.-A., Tani, J. (eds.) SAB 2008. LNCS, vol. 5040, pp. 93–102. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  37. 37.
    Todorov, E., Li, W.: A generalized iterative LQG method for locally-optimal feedback control of constrained nonlinear stochastic systems. In: Proceedings of the American Control Conference, pp. 300–306 (2005)Google Scholar
  38. 38.
    Nguyen-Tuong, D., Peters, J., Seeger, M., Scholkopf, B.: Learning inverse dynamics: a comparison. Technical report, Max Planck Institute for Biological Cybernetics, Spemannstrae 38, 72076 Tubingen - Germany (2008)Google Scholar
  39. 39.
    Butz, M.V., Herbort, O., Hoffman, J.: Exploiting redundancy for flexible behavior: Unsupervised learning in a modular sensorimotor control architecture. Psychological Review 114(4), 1015–1046 (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2009

Authors and Affiliations

  • Camille Salaün
    • 1
  • Vincent Padois
    • 1
  • Olivier Sigaud
    • 1
  1. 1.Institut des Systèmes Intelligents et de Robotique, CNRS UMR 7222Université Pierre et Marie Curie - Paris6ParisFrance

Personalised recommendations