Perspective-corrected Spatial Referring Expression Generation for Human-Robot Interaction

TL;DR

This paper introduces a perspective-corrected approach for generating spatial referring expressions in human-robot interaction, improving communication clarity by considering reference frames and evaluating expressions for effectiveness.

Contribution

It proposes a novel perspective-corrected method that generates diverse spatial expressions by selecting reference frames and evaluates them to enhance robot-human communication.

Findings

Generated expressions are more effective in practical robot interactions.

The approach narrows understanding gaps in spatial referring expressions.

Empirical results show improved expression quality.

Abstract

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, 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 paper, we propose a novel perspective-corrected spatial referring expression generation (PcSREG) approach for human-robot interaction by considering the selection of reference frames. The task of referring expression generation 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 experiments show that our approach can generate more effective spatial referring expressions for practical applications.