Item Ownership Relationship Semantic Learning Strategy for Personalized Service Robot

In order to satisfy the robotic personalized service requirements that can select exclusive items to perform inference and planning according to different service individuals, the service robots need to have the ability to independently obtain the ownership relationship between humans and their carrying items. In this work, we present a novel semantic learning strategy for item ownership. Firstly, a human-carrying-items detection network based on human posture estimation and object detection model is used. Then, the transferred convolutional neural network is used to extract the characteristics of the objects and the back-end classifier to recognize the object instance. At the same time, the face detection and recognition model are used to identify the service individual. Finally, on the basis of the former two, the active learning of ownership items is completed. The experimental results show that the proposed ownership semantic learning strategy can determine the ownership relationship of private goods accurately and efficiently. The solution of this problem can improve the intelligence level of robot life service.