Behavior pattern learning for robot partner based on growing neural networks in informationally structured space

In this paper, we focus on human behavior estimation for human-robot interaction. Human behavior recognition is one of the most important techniques, because bodily expressions convey important and effective information for robots. This paper proposes a learning structure composed of two learning modules for feature extraction and contextual relation modeling, using Growing Neural Gas (GNG) and Spiking Neural Network (SNN). GNG is applied to the feature extraction of human behavior, and SNN is used to associate the features with verbal labels that robots can get through human-robot interaction. Furthermore, we show an experimental result, and discuss effectiveness of the proposed method.