Human awareness based robot performance learning in a social environment

In this paper, 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.