Prediction of Human Behavior Patterns based on Spiking Neurons for A Partner Robot

N. Kubota, Kenichiro Nishida
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引用次数: 11

Abstract

This paper discusses prediction of human behavior patterns for natural communication between a partner robot and a human. The prediction is very important to extract the perceptual information for the natural communication with a human in the future. Therefore we propose a prediction-based perceptual system based on spiking neurons. The proposed method is composed of four layers: the input layer, clustering layer, prediction layer, and perceptual module selection layer. In the clustering layer, an unsupervised learning method is used to perform the clustering of human behavior patterns. We use unsupervised learning because the human behavior patterns to be paid attention change by the other and the situation in communication. Furthermore, we show experimental results of the communication between a partner robot and a human based on our proposed method
基于脉冲神经元的同伴机器人人类行为模式预测
本文讨论了伙伴机器人与人类之间自然交流的人类行为模式预测。预测对于提取未来与人自然交流的感知信息非常重要。因此,我们提出了一种基于脉冲神经元的基于预测的感知系统。该方法由四层组成:输入层、聚类层、预测层和感知模块选择层。在聚类层,采用无监督学习方法对人类行为模式进行聚类。我们之所以使用无监督学习,是因为要关注的人类行为模式会随着他人和交流中的情况而改变。此外,我们还展示了基于该方法的伙伴机器人与人之间通信的实验结果
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