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.