Embodied Moving-Target Seeking with Prediction and Planning

  • Noelia Oses
  • Matej Hoffmann
  • Randal A. Koene
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6077)

Abstract

We present a bio-inspired control method for moving-target seeking with a mobile robot, which resembles a predator-prey scenario. The motor repertoire of a simulated Khepera robot was restricted to a discrete number of ‘gaits’. After an exploration phase, the robot automatically synthesizes a model of its motor repertoire, acquiring a forward model. Two additional components were introduced for the task of catching a prey robot. First, an inverse model to the forward model, which is used to determine the action (gait) needed to reach a desired location. Second, while hunting the prey, a model of the prey’s behavior is learned online by the hunter robot. All the models are learned ab initio, without assumptions, work in egocentric coordinates, and are probabilistic in nature. Our architecture can be applied to robots with any physical constraints (or embodiment), such as legged robots.

Keywords

bio-inspired control forward model inverse model prediction planning egocentric coordinates 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Webb, B.: Neural mechanisms for prediction: do insects have forward models? Trends in Neurosciences 27, 278–282 (2004)CrossRefGoogle Scholar
  2. 2.
    Wolpert, D.M., Miall, R.C., Kawato, M.: Internal models in the cerebellum. Trends in Cognitive Sciences 2, 338–347 (1998)CrossRefGoogle Scholar
  3. 3.
    Meltzoff, A.N., Moore, M.K.: Explaining facial imitation: a theoretical model. Early Development and parenting 6(2), 157, 1–14 (1997)Google Scholar
  4. 4.
    Pfeifer, R., Scheier, C.: Understanding intelligence. The MIT Press, Cambridge (2001)Google Scholar
  5. 5.
    Demiris, Y., Dearden, A.: From motor babbling to hierarchical learning by imitation: a robot developmental pathway. In: Proceedings of the Fifth International Workshop on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems. Lund University Cognitive Studies, vol. 123, pp. 31–37 (2005)Google Scholar
  6. 6.
    Mitchell, T.M.: Machine learning. McGraw-Hill, New York (1997)MATHGoogle Scholar
  7. 7.
    Russel, S., Norvig, P.: Artificial Intelligence: A Modern Approach. Prentice Hall, Englewood Cliffs (1995)Google Scholar
  8. 8.
    Thrun, S., Burgard, W., Fox, D.: Probabilistic robotics. The MIT Press, Cambridge (2005)MATHGoogle Scholar
  9. 9.
    Vazquez, A.: Incremental learning for motion prediction of pedestrians and vehicles. PhD thesis, Institut National Polytechnique de Grenoble (2007)Google Scholar
  10. 10.
    Michel, O.: Webots: Professional Mobile Robot Simulation. International Journal of Advanced Robotic Systems 1(1), 39–42 (2004)Google Scholar
  11. 11.
    Braitenberg, V.: Vehicles Experiments in Synthetic Psychology. The MIT Press, Cambridge (1986)Google Scholar
  12. 12.
    Brooks, R.A.: Intelligence Without Representation. Artificial Intelligence Journal 47, 139–159 (1991)CrossRefGoogle Scholar
  13. 13.
    Pezzulo, G.: Anticipation and Future-Oriented Capabilities in Natural and Artificial Cognition. In: Lungarella, M., Iida, F., Bongard, J.C., Pfeifer, R. (eds.) 50 Years of Aritficial Intelligence. LNCS (LNAI), vol. 4850, pp. 258–271. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  14. 14.
    Duro, R.J., Graña, M., de Lope, J.: On the potential contributions of hybrid intelligent approaches to multicomponent robotic system development. Information Sciences (in press, 2010)Google Scholar
  15. 15.
    Clark, A., Grush, R.: Towards a Cognitive Robotics. Adaptive Behavior 7(1), 5–16 (1999)CrossRefGoogle Scholar
  16. 16.
    Grush, R.: The emulation theory of representation: motor control, imagery, and perception. Behavioral and Brain Sciences 27, 377–442 (2004)Google Scholar
  17. 17.
    Schomaker, L.: Anticipation in cybernetic systems: A case against mindless anti-representationalism. In: IEEE International Conference on Systems, Man and Cybernetics, The Hague, Netherlands (2004)Google Scholar
  18. 18.
    Tarsitano, M.: Route selection by a jumping spider (Portia labiata) during the locomotory phase of a detour. Animal Behavior 72, 1437–1442 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Noelia Oses
    • 1
  • Matej Hoffmann
    • 2
  • Randal A. Koene
    • 1
  1. 1.Fundación FATRONIK-TecnaliaDonostia-San SebastiánSpain
  2. 2.Artificial Intelligence Laboratory, Department of InformaticsUniversity of ZurichZurichSwitzerland

Personalised recommendations