A Taxonomy for Characterizing Modes of Interactions in Goal-driven, Human-robot Teams
Name
iros2019_preprint-1.pdf
Description
Accepted version
Size
327.49 KB
Format
Adobe PDF
Checksum (MD5)
a90408387193461553de7c56a9e36f5c
Author(s)
Parashar, Priyam
Sanneman, Lindsay M.
Shah, Julie A
Christensen, Henrik I.
Date Issued
January 2020
Journal
IEEE International Conference on Intelligent Robots and Systems
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Parashar, Priyam, Sanneman, Lindsay M., Shah, Julie A and Christensen, Henrik I. 2020. "A Taxonomy for Characterizing Modes of Interactions in Goal-driven, Human-robot Teams." IEEE International Conference on Intelligent Robots and Systems.
Version
Author's final manuscript
Abstract
© 2019 IEEE. As robots and other autonomous agents are increasingly incorporated into complex domains, characterizing interaction within heterogeneous teams that include both humans and machines becomes more necessary. Previous literature has addressed the task of characterizing human-robot interaction from different perspectives and in multiple contexts. However, the numerous factors behind interaction work in conjunction, and the insights gained from one perspective can inadvertently affect another, creating a need for unification of these taxonomies and frameworks within an overarching taxonomy that systematically defines these relationships. In this paper we review existing taxonomies related to human-robot interaction, the behavioral sciences, and social and algorithmic taxonomies, and propose an overarching ontology for the factors from these works. We identify three main components characterizing the structure of an interaction (environment, task, and team), and structure them over two levels: contextual factors and factors driven by local dynamics. Finally, we present an analysis of how these factors affect decisions about levels of robot automation and level of information abstraction in an interaction, and discuss curent gaps in the literature that can motivate future research.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution-Noncommercial-Share Alike
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1109/IROS40897.2019.8967974
