For robust self-localisation in real environments autonomous agents must rely upon multimodal sensory information. The relative importance of a sensory modality is not constant during the agent environment interaction. We study the interrelation between visual and tactile information in a spatial learning task. We adopt a biologically inspired approach to detect multimodal correlations based on the properties of neurons in the superior colliculus. Reward-based Hebbian learning is applied to train an active gating network to weigh individual senses depending on the current environmental conditions. The model is implemented and tested on a mobile robot platform. © Springer-Verlag Berlin Heidelberg 2002.
CITATION STYLE
Strösslin, T., Krebser, C., Arleo, A., & Gerstner, W. (2002). Combining multimodal sensory input for spatial learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 2415 LNCS, pp. 87–92). Springer Verlag. https://doi.org/10.1007/3-540-46084-5_15
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