Generating Spatial Referring Expressions in a Social Robot: Dynamic vs. Non-ambiguous

Frontiers in Robotics and AI

Séverin Lemaignan¹  Emmanuel Senft²  Christopher D. Wallbridge²  Tony Belpaeme³ 

[1] Bristol Robotics Laboratory, University of West England, Bristol, United Kingdom;CRNS, School of Computing, Electronics and Mathematics, University of Plymouth, Plymouth, United Kingdom;IDlab - imec, Ghent University, Ghent, Belgium;

关键词: Human Robot Interaction;    natural language;    spatial referring expressions;    dynamic description;    machine learning;    user study;   

DOI  :  10.3389/frobt.2019.00067

来源: DOAJ

【 摘 要 】

Generating spatial referring expressions is key to allowing robots to communicate with people in an environment. The focus of most algorithms for generation is to create a non-ambiguous description, and how best to deal with the combination explosion this can create in a complex environment. However, this is not how people naturally communicate. Humans tend to give an under-specified description and then rely on a strategy of repair to reduce the number of possible locations or objects until the correct one is identified, what we refer to here as a dynamic description. We present here a method for generating these dynamic descriptions for Human Robot Interaction, using machine learning to generate repair statements. We also present a study with 61 participants in a task on object placement. This task was presented in a 2D environment that favored a non-ambiguous description. In this study we demonstrate that our dynamic method of communication can be more efficient for people to identify a location compared to one that is non-ambiguous.

【 授权许可】

Unknown