Decision-making authority, team efficiency and human worker satisfaction in mixed human–robot teams
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10514_2015_9457_ReferencePDF.pdf
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Author(s)
Gombolay, Matthew Craig
Gutierrez, Reymundo A.
Clarke, Shanelle G.
Sturla, Giancarlo F.
Shah, Julie A.
Date Issued
July 2015
Journal
Autonomous Robots
Publisher
Springer Nature
Citation
Gombolay, Matthew C., Reymundo A. Gutierrez, Shanelle G. Clarke, Giancarlo F. Sturla, and Julie A. Shah. "Decision-making authority, team efficiency and human worker satisfaction in mixed human–robot teams." Autonomous Robots 39:3 (October 2015), pp. 293-312.
Version
Author's final manuscript
Abstract
In manufacturing, advanced robotic technology has opened up the possibility of integrating highly autonomous mobile robots into human teams. However, with this capability comes the issue of how to maximize both team efficiency and the desire of human team members to work with these robotic counterparts. To address this concern, we conducted a set of experiments studying the effects of shared decision-making authority in human–robot and human-only teams. We found that an autonomous robot can outperform a human worker in the execution of part or all of the process of task allocation (p<0.001 for both), and that people preferred to cede their control authority to the robot $$(p<0.001)$$ (p<0.001). We also established that people value human teammates more than robotic teammates; however, providing robots authority over team coordination more strongly improved the perceived value of these agents than giving similar authority to another human teammate $$(p< 0.001)$$(p<0.001). In post hoc analysis, we found that people were more likely to assign a disproportionate amount of the work to themselves when working with a robot $$(p<0.01)$$(p<0.01) rather than human teammates only. Based upon our findings, we provide design guidance for roboticists and industry practitioners to design robotic assistants for better integration into the human workplace.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Massachusetts Institute of Technology. Department of Aeronautics and Astronautics
Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science
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DOI of Published Version
https://doi.org/10.1007/s10514-015-9457-9
