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

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 languageAmerican English
Title of host publication2023 IEEE-RAS 22nd International Conference on Humanoid Robots, Humanoids 2023
ISBN (Electronic)9798350303278
DOIs
StatePublished - 1 Jan 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

Conference

Conference22nd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2023
Country/TerritoryUnited States
CityAustin
Period12/12/2314/12/23

All Science Journal Classification (ASJC) codes

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Hardware and Architecture
  • Human-Computer Interaction
  • Electrical and Electronic Engineering

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