# Exploring the Cost of Interruptions in Human-Robot Teaming

arXiv: https://arxiv.org/abs/2311.00785 · HTML: https://mlsanity.com/paper/2311.00785/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Nov 2023.

## Built on by (most cited first, 5 of 5)

- [Open-Ended Goal Inference through Actions and Language for Human-Robot Collaboration](https://mlsanity.com/paper/2512.04453/) (2025)
- [Artificial Intelligent Disobedience: Rethinking the Agency of Our Artificial Teammates](https://mlsanity.com/paper/2506.22276/) (2025)
- [Birds of a Different Feather Flock Together: Exploring Opportunities and Challenges in Animal-Human-Machine Teaming](https://mlsanity.com/paper/2504.13973/) (2025)
- [Robots Influencing Humans to Reveal their Goals during Collaboration and Competition](https://mlsanity.com/paper/2609.05519/) (2026)
- [Gap the (Theory of) Mind: Sharing Beliefs About Teammates' Goals Boosts Collaboration Perception, Not Performance](https://mlsanity.com/paper/2505.03674/) (2025)

## Abstract (arXiv)

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.