Perspective-Corrected Spatial Referring Expression Generation for Human–Robot Interaction

Intelligent robots designed to interact with humans in real scenarios need to be able to refer to entities actively by natural language. In spatial referring expression generation (REG), the ambiguity is unavoidable due to the diversity of reference frames, which will lead to an understanding gap between humans and robots. To narrow this gap, in this article, we propose a novel perspective-corrected spatial REG (PcSREG) approach for human–robot interaction (HRI) by considering the selection of reference frames. The task of REG is simplified into the process of generating diverse spatial relation units. First, we pick out all landmarks in these spatial relation units according to the entropy of preference and allow its updating through a stack model. Then, all possible referring expressions are generated according to different reference frame strategies. Finally, we evaluate every expression using a probabilistic referring expression resolution model and find the best expression that satisfies both of the appropriateness and effectiveness. We implement the proposed approach on a robot system and empirical experimental results show that our approach can generate more effective spatial referring expressions for practical applications.