Neuro-inspired Navigation Strategies Shifting for Robots: Integration of a Multiple Landmark Taxon Strategy

  • Ken Caluwaerts
  • Antoine Favre-Félix
  • Mariacarla Staffa
  • Steve N’Guyen
  • Christophe Grand
  • Benoît Girard
  • Mehdi Khamassi
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7375)


Rodents have been widely studied for their adaptive navigation capabilities. They are able to exhibit multiple navigation strategies; some based on simple sensory-motor associations, while others rely on the construction of cognitive maps. We previously proposed a computational model of parallel learning processes during navigation which could reproduce in simulation a wide set of rat behavioral data and which could adaptively control a robot in a changing environment. In this previous robotic implementation the visual approach (or taxon) strategy was however paying attention to the intra-maze landmark only and learned to approach it. Here we replaced this mechanism by a more realistic one where the robot autonomously learns to select relevant landmarks. We show experimentally that the new taxon strategy is efficient, and that it combines robustly with the planning strategy, so as to choose the most efficient strategy given the available sensory information.


Planning Strategy Spatial Cognition Place Cell Exploration Strategy Place Strategy 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Pfeifer, R., Lungarella, M., Iida, F.: Self-organization, embodiment, and biologically inspired robotics. Science 318, 1088–1093 (2007)CrossRefGoogle Scholar
  2. 2.
    Arbib, M., Metta, G., van der Smagt, P.: Neurorobotics: From vision to action. In: Handbook of Robotics, pp. 1453–1480. Springer, Berlin (2008)CrossRefGoogle Scholar
  3. 3.
    Meyer, J.A., Guillot, A.: Biologically-inspired robots. In: Handbook of Robotics, pp. 1395–1422. Springer, Berlin (2008)CrossRefGoogle Scholar
  4. 4.
    Arleo, A., Gerstner, W.: Spatial cognition and neuro-mimetic navigation: a model of hippocampal place cell activity. Biological Cybernetics 83(3), 287–299 (2000)CrossRefGoogle Scholar
  5. 5.
    Krichmar, J., Seth, A., Nitz, D., Fleischer, J., Edelman, G.: Spatial navigation and causal analysis in a brain-based device modeling cortical hippocampal interactions. Neuroinformatics 3(3), 147–169 (2005)CrossRefGoogle Scholar
  6. 6.
    Meyer, J.-A., Guillot, A., Girard, B., Khamassi, M., Pirim, P., Berthoz, A.: The Psikharpax project: towards building an artificial rat. Robotics and Autonomous Systems 50(4), 211–223 (2005)CrossRefGoogle Scholar
  7. 7.
    Barrera, A., Weitzenfeld, A.: Biologically-inspired robot spatial cognition based on rat neurophysiological studies. Autonomous Robots 25, 147–169 (2008)CrossRefGoogle Scholar
  8. 8.
    Giovannangeli, C., Gaussier, P.: Autonomous vision-based navigation: Goal-oriented action planning by transient states prediction, cognitive map building, and sensory-motor learning. In: Proceedings of the International Conference on Intelligent Robots and Systems, vol. 1, pp. 281–297. University of California Press (2008)Google Scholar
  9. 9.
    Milford, M., Wyeth, G.: Persistent navigation and mapping using a biologically inspired slam system. The International Journal of Robotics Research 29(9), 1131–1153 (2010)CrossRefGoogle Scholar
  10. 10.
    Arleo, A., Rondi-Reig, L.: Multimodal sensory integration and concurrent navigation strategies for spatial cognition in real and artificial organisms. Journal of Integrative Neuroscience 6(3), 327–366 (2007)CrossRefGoogle Scholar
  11. 11.
    Packard, M.G., Knowlton, B.J.: Learning and memory functions of the basal ganglia. Annual Review of Neuroscience 25, 563–593 (2002)CrossRefGoogle Scholar
  12. 12.
    Burgess, N.: Spatial cognition and the brain. Year In Cognitive Neuroscience 2008 1124, 77–97 (2008)Google Scholar
  13. 13.
    O’Keefe, J., Nadel, L.: The Hippocampus as a Cognitive Map. Clarendon Press, Oxford (1978)Google Scholar
  14. 14.
    Johnson, A., Redish, A.D.: Neural ensembles in CA3 transiently encode paths forward of the animal at a decision point. Journal of Neuroscience 27(45), 12176–12189 (2007)CrossRefGoogle Scholar
  15. 15.
    Pearce, J.M., Roberts, A.D., Good, M.: Hippocampal lesions disrupt navigation based on cognitive maps but not heading vectors. Nature 396(6706), 75–77 (1998)CrossRefGoogle Scholar
  16. 16.
    Devan, B.D., White, N.M.: Parallel information processing in the dorsal striatum: Relation to hippocampal function. Journal of Neuroscience 19(7), 2789–2798 (1999)Google Scholar
  17. 17.
    Dollé, L., Sheynikhovich, D., Girard, B., Chavarriaga, R., Guillot, A.: Path planning versus cue responding: a bioinspired model of switching between navigation strategies. Biological Cybernetics 103(4), 299–317 (2010)CrossRefGoogle Scholar
  18. 18.
    Caluwaerts, K., Staffa, M., N’Guyen, S., Grand, C., Dollé, L., Favre-Félix, A., Girard, B., Khamassi, M.: A biologically inspired meta-control navigation system for the psikharpax rat robot. Bioinspiration and Biomimetics (to appear, 2012)Google Scholar
  19. 19.
    Stein, B.E., Meredith, M.A.: The merging of the senses. The MIT Press, Cambridge (1993)Google Scholar
  20. 20.
    Sutton, R., Barto, A.: Reinforcement Learning: An Introduction. MIT Press (1998)Google Scholar
  21. 21.
    Gat, E.: On three-layer architectures. In: Kortenkamp, D., Bonnasso, R.P., Murphy, R. (eds.) Artificial Intelligence and Mobile Robots: Case Studies of Successful Robot Systems, pp. 195–210. AAAI Press (1998)Google Scholar
  22. 22.
    Kortenkamp, D., Simmons, R.: Robotic systems architectures and programming. In: Siciliano, B., Khatib, O. (eds.) Handbook of Robotics, pp. 187–206. Springer (2008)Google Scholar
  23. 23.
    Minguez, J., Lamiraux, F., Laumond, J.: Motion planning and obstacle avoidance. In: Siciliano, B., Khatib, O. (eds.) Handbook of Robotics, pp. 827–852. Springer (2008)Google Scholar
  24. 24.
    Keramati, M., Dezfouli, A., Piray, P.: Speed/accuracy trade-off between the habitual and goal-directed processes. PLoS Computational Biology 7(5), 1–25 (2011)MathSciNetCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Ken Caluwaerts
    • 1
    • 2
  • Antoine Favre-Félix
    • 1
  • Mariacarla Staffa
    • 1
    • 3
  • Steve N’Guyen
    • 1
  • Christophe Grand
    • 1
  • Benoît Girard
    • 1
  • Mehdi Khamassi
    • 1
  1. 1.Institut des Systèmes Intelligents et de Robotique (ISIR) UMR7222Université Pierre et Marie Curie, CNRSParisFrance
  2. 2.Reservoir Lab, Electronics and Information Systems (ELIS) DepartmentGhent UniversityGhentBelgium
  3. 3.Dipartimento di Informatica e SistemisticaUniversità degli Studi di Napoli Federico IINaplesItaly

Personalised recommendations