A Taxonomy for Characterizing Modes of Interactions in Goal-driven, Human-robot Teams
© 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 interact...
| Main Authors: | , , , |
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| Other Authors: | |
| Format: | Article |
| Language: | English |
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Institute of Electrical and Electronics Engineers (IEEE)
2021
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| Online Access: | https://hdl.handle.net/1721.1/137316 |
| _version_ | 1811070021466587136 |
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| author | Parashar, Priyam Sanneman, Lindsay M. Shah, Julie A Christensen, Henrik I. |
| author2 | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory |
| author_facet | Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory Parashar, Priyam Sanneman, Lindsay M. Shah, Julie A Christensen, Henrik I. |
| author_sort | Parashar, Priyam |
| collection | MIT |
| description | © 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. |
| first_indexed | 2024-09-23T08:21:10Z |
| format | Article |
| id | mit-1721.1/137316 |
| institution | Massachusetts Institute of Technology |
| language | English |
| last_indexed | 2024-09-23T08:21:10Z |
| publishDate | 2021 |
| publisher | Institute of Electrical and Electronics Engineers (IEEE) |
| record_format | dspace |
| spelling | mit-1721.1/1373162022-09-23T12:27:37Z A Taxonomy for Characterizing Modes of Interactions in Goal-driven, Human-robot Teams Parashar, Priyam Sanneman, Lindsay M. Shah, Julie A Christensen, Henrik I. Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory © 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. 2021-11-03T20:30:08Z 2021-11-03T20:30:08Z 2020-01 2021-05-04T12:22:08Z Article http://purl.org/eprint/type/ConferencePaper https://hdl.handle.net/1721.1/137316 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. en http://dx.doi.org/10.1109/IROS40897.2019.8967974 IEEE International Conference on Intelligent Robots and Systems Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf Institute of Electrical and Electronics Engineers (IEEE) Other repository |
| spellingShingle | Parashar, Priyam Sanneman, Lindsay M. Shah, Julie A Christensen, Henrik I. A Taxonomy for Characterizing Modes of Interactions in Goal-driven, Human-robot Teams |
| title | A Taxonomy for Characterizing Modes of Interactions in Goal-driven, Human-robot Teams |
| title_full | A Taxonomy for Characterizing Modes of Interactions in Goal-driven, Human-robot Teams |
| title_fullStr | A Taxonomy for Characterizing Modes of Interactions in Goal-driven, Human-robot Teams |
| title_full_unstemmed | A Taxonomy for Characterizing Modes of Interactions in Goal-driven, Human-robot Teams |
| title_short | A Taxonomy for Characterizing Modes of Interactions in Goal-driven, Human-robot Teams |
| title_sort | taxonomy for characterizing modes of interactions in goal driven human robot teams |
| url | https://hdl.handle.net/1721.1/137316 |
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