Improving Robot-Human Communication by Integrating Visual Attention and Auditory Localization Using a Biologically Inspired Model of Superior Colliculus
Improving Robot-Human Communication by Integrating Visual Attention and Auditory Localization Using a Biologically Inspired Model of Superior Colliculus
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使用受生物学启发的上丘模型整合视觉注意力和听觉定位来改善机器人与人类的交流
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发表时间：
2011
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通讯作者：
K. Burn
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作者：
K. Ravulakollu;H. Erwin;K. Burn
有效的代理沟通一直是现代研究的一个重要领域。本文的重点是基于人脑系统中上丘的简单模型，借助视觉注意力和听觉定位来改善智能体与人类的交流，从而实现更高的共同精度。该模型接收单独的视觉和听觉感官刺激，并将它们组合起来生成预测声源位置的综合刺激。这种综合刺激用于产生视觉系统的运动扫视以关注声音。该计算模型基于神经网络学习方法，并在反映不同条件的实验中进行探索，以确定它是否模仿上丘在听觉和视觉刺激整合中的表现。最后通过单峰和多峰数据之间的评估策略，确定了上丘计算模型的效率。基于神经网络的计算模型的性能已被证明在学习、综合响应比单峰响应更好的性能以及提供真实的通信体验方面是有效的。
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.