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Habituation is a form of nonassociative learning observed in a variety of species of animals. Arguably, it is the simplest form of learning. Nonetheless, the ability to habituate to certain stimuli implies plastic neural systems and adaptive behaviors. This paper describes how computational models of habituation can be applied to real robots. In particular, we discuss the problem of the oscillatory movements observed when a Khepera robot navigates through narrow hallways using a biologically inspired neurocontroller. Results show that habituation to the proximity of the walls can lead to smoother navigation. Habituation to sensory stimulation to the sides of the robot does not interfere with the robot's ability to turn at dead ends and to avoid obstacles outside the hallway. This paper shows that simple biological mechanisms of learning can be adapted to achieve better performance in real mobile robots.