Abstract
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 language | English |
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Title of host publication | 2023 IEEE-RAS 22nd International Conference on Humanoid Robots, Humanoids 2023 |
Publisher | IEEE Computer Society |
ISBN (Electronic) | 9798350303278 |
DOIs | |
State | Published - 2023 |
Event | 22nd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2023 - Austin, United States Duration: 12 Dec 2023 → 14 Dec 2023 |
Publication series
Name | IEEE-RAS International Conference on Humanoid Robots |
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ISSN (Print) | 2164-0572 |
ISSN (Electronic) | 2164-0580 |
Conference
Conference | 22nd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2023 |
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Country/Territory | United States |
City | Austin |
Period | 12/12/23 → 14/12/23 |
Bibliographical note
Publisher Copyright:© 2023 IEEE.
Funding
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.
Funders | Funder number |
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National Science Foundation | NRT-2125858, FAIN-2019844 |
Office of Naval Research | N00014-18-2243 |
Army Research Office | E2061621 |
University of Texas at Austin | |
Humanities Research Institute | |
Bar-Ilan University | |
Israel Innovation Authority |