Combining Multimodal Sensory Input for Spatial Learning

Authors:
Thomas Strösslin;Christophe Krebser;Angelo Arleo;Wulfram Gerstner
Affiliations:
-;-;-;-
Venue:
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Year:
2002

Citing 2
Cited 2

Using Occupancy Grids for Mobile Robot Perception and Navigation

Computer
1994 Special Issue: A model of hippocampal function

Neural Networks - Special issue: models of neurodynamics and behavior

Cognitive Maps in Rats and Robots

Adaptive Behavior - Animals, Animats, Software Agents, Robots, Adaptive Systems
Modelling path integrator recalibration using hippocampal place cells

ICANN'05 Proceedings of the 15th international conference on Artificial Neural Networks: biological Inspirations - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

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 agentenvironment 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.