Gesture Learning Based on A Topological Approach for Human-Robot Interaction

Gesture learning and recognition are essential challenges for developing human friendly robots. Classification methods and machine learning technics have been used to classify gestures and produce motions for robots. However, human behaviors generally differ according to cultures, characteristics of the region, and personality traits of individuals. Thus, the capability of learning in unsupervised manner is required for social robots to adaptively acquire the skills. In this study, we use growing neural gas (GNG) algorithm for the clustering of primitive motion patterns on gesture trajectory. Moreover, we propose a hierarchical learning architecture for decomposing imitative motions and reconstructing gesture movements. Furthermore, we show an experimental example to discuss the effectiveness and applicability of the proposed method.