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Human-Robot Interaction using Intention Recognition

Recognition of human intention is an important issue in human-robot interaction research and allows a robot to respond adequately according to human's wish. In this paper, we discuss how robots can infer human intention by learning affordance, a concept used to represent the relation between an agent and its environment. Learning of the robot, to understand human and its interaction with environment, is achieved within the framework of action-perception cycle. The action-perception cycle explains how an intelligent agent learns and enhances its ability continuously by interacting with its surrounding. The proposed intention recognition and recommendation system includes several key functions such as joint attention, object recognition, affordance model, motion understanding module and so on. The experimental results show high successful recognition performance and the plausibility of the proposed system.