Human awareness based robot performance learning in a social environment

Abstract

We develop a human awareness Decision Network model for robot performance on decision making. To accomplish more natural and intelligent human robot interaction (HRI), a robot should not only be able to infer the user's intention through recognizing the actions, but also to perform appropriate decisions and to learn from the user's feedback. In traditional approaches, user intention inference and feedback learning are dealt with separately. In this paper, we propose an integrated strategy of human-oriented perception, user modeling and user sensitivity in a social environment. The robot can analyze a user's feedback to adjust its decisions as the user expects through the strategy. The experimental results show the effectiveness of the proposed approach that enables autonomous adaptation of robot's decision to the user desires. Also, we demonstrate a satisfactory performance in terms of successful inference of human intentions, as well as adequacy of the decisions made by the robot for meeting user expectation.