Combining Multimodal Sensory Input for Spatial Learning

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.