Improving Robot-Human Communication by Integrating Visual Attention and Auditory Localization Using a Biologically Inspired Model of Superior Colliculus

Abstract

Effective agent communication is always been an important modern area of research. This paper focuses on achieving greater precision in common by improving agent-human communication with the help of visual attention and auditory localization based on a simple model of the superior colliculus in the human brain system. The model receives individual visual and auditory sensory stimuli and combines them to generate an integrated stimulus predicting the location of the sound source. This integrated stimulus is used to generate a motor saccade of the visual system to attend to the sound. The computational model is based on a neural network approach with learning and is explored in experiments reflecting varied conditions to determine whether it mimes the performance of superior colliculus in auditory and visual stimuli integration. Finally with a evaluation strategy carried between unimodal and multimodal data, the efficiency of the computational model of Superior Colliculus is determined. Performance of the neural network based computational model has proven effective in terms of learning, the better performance of the integrated response over unimodal response and providing a realistic communication experience.