Neuro-inspired Navigation Strategies Shifting for Robots: Integration of a Multiple Landmark Taxon Strategy
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.
KeywordsPlanning Strategy Spatial Cognition Place Cell Exploration Strategy Place Strategy
Unable to display preview. Download preview PDF.
- 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
- 12.Burgess, N.: Spatial cognition and the brain. Year In Cognitive Neuroscience 2008 1124, 77–97 (2008)Google Scholar
- 13.O’Keefe, J., Nadel, L.: The Hippocampus as a Cognitive Map. Clarendon Press, Oxford (1978)Google Scholar
- 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
- 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.Stein, B.E., Meredith, M.A.: The merging of the senses. The MIT Press, Cambridge (1993)Google Scholar
- 20.Sutton, R., Barto, A.: Reinforcement Learning: An Introduction. MIT Press (1998)Google Scholar
- 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.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.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