Exploring the Cost of Interruptions in Human-Robot Teaming

Swathi Mannem, William Macke, Peter Stone, Reuth Mirsky

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Productive and efficient human-robot teaming is a highly desirable ability in service robots, yet there is a fundamental trade-off that a robot needs to consider in such tasks. On the one hand, gaining information from communication with teammates can help individual planning. On the other hand, such communication comes at the cost of distracting teammates from efficiently completing their goals, which can also harm the overall team performance. In this study, we quantify the cost of interruptions in terms of degradation of human task performance, as a robot interrupts its teammate to gain information about their task. Interruptions are varied in timing, content, and proximity. The results show that people find the interrupting robot significantly less helpful. However, the human teammate's performance in a secondary task deteriorates only slightly when interrupted. These results imply that while interruptions can objectively have a low cost, an uninformed implementation can cause these interruptions to be perceived as distracting. These research outcomes can be leveraged in numerous applications where collaborative robots must be aware of the costs and gains of interruptive communication, including logistics and service robots.

Original languageEnglish
Title of host publication2023 IEEE-RAS 22nd International Conference on Humanoid Robots, Humanoids 2023
PublisherIEEE Computer Society
ISBN (Electronic)9798350303278
StatePublished - 2023
Event22nd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2023 - Austin, United States
Duration: 12 Dec 202314 Dec 2023

Publication series

NameIEEE-RAS International Conference on Humanoid Robots
ISSN (Print)2164-0572
ISSN (Electronic)2164-0580


Conference22nd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2023
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 2023 IEEE.


This work has taken place in the Learning Agents Research Group (LARG) at the Artificial Intelligence Laboratory, The University of Texas at Austin. LARG research is supported in part by the National Science Foundation (FAIN-2019844, NRT-2125858), the Office of Naval Research (N00014-18-2243), Army Research Office (E2061621), Bosch, Lockheed Martin, and Good Systems, a research grand challenge at the University of Texas at Austin. The views and conclusions contained in this document are those of the authors alone. Peter Stone serves as the Executive Director of Sony AI America and receives financial compensation for this work. The terms of this arrangement have been reviewed and approved by the University of Texas at Austin in accordance with its policy on objectivity in research. Research conducted in the Goal Optimization using Learning and Decision-making (GOLD) lab at Bar Ilan University is part of the HRI consortium supported by the Israel Innovation Authority.

FundersFunder number
National Science FoundationNRT-2125858, FAIN-2019844
Office of Naval ResearchN00014-18-2243
Army Research OfficeE2061621
University of Texas at Austin
Humanities Research Institute
Bar-Ilan University
Israel Innovation Authority


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