Abstract
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.
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Strösslin, T., Krebser, C., Arleo, A., Gerstner, W. (2002). Combining Multimodal Sensory Input for Spatial Learning. In: Dorronsoro, J.R. (eds) Artificial Neural Networks — ICANN 2002. ICANN 2002. Lecture Notes in Computer Science, vol 2415. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-46084-5_15
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DOI: https://doi.org/10.1007/3-540-46084-5_15
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