Abstract
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.
DOI
10.3389/frobt.2019.00067
Publication Date
2019-08-02
Publication Title
Frontiers in Robotics and AI
Volume
6
Embargo Period
2020-09-12
Organisational Unit
School of Engineering, Computing and Mathematics
Recommended Citation
Wallbridge, C., Lemaignan, S., Senft, E., & Belpaeme, T. (2019) 'Generating Spatial Referring Expressions in a Social Robot: Dynamic vs. Non-ambiguous', Frontiers in Robotics and AI, 6. Available at: https://doi.org/10.3389/frobt.2019.00067
