Matthew Gombolay

About

Matthew Gombolay is a leading researcher at the intersection of human-robot interaction, multi-robot coordination, and AI-driven decision-making. Based at Georgia Tech, his work fundamentally advances how robots and humans collaborate in high-stakes environments ranging from manufacturing floors to hospital wards. Gombolay's most influential contribution—his 2015 paper on decision-making authority in mixed human-robot teams (251 citations)—revealed critical insights about how autonomy allocation affects both team efficiency and worker satisfaction, reshaping how researchers think about robot integration. His subsequent work on trust and anthropomorphism (213 citations) further illuminated how robot attributes like physical presence and perceived humanness influence human reliance on robotic teammates. On the computational side, Gombolay has pioneered fast scheduling algorithms for multi-robot teams navigating complex temporospatial constraints, with graph attention networks emerging as a signature methodological contribution across multiple high-impact papers. His interdisciplinary reach extends to healthcare, where he studied robotic decision support among clinical staff on labor and delivery floors. His broader influence includes contributions to statistical best practices in HRI research and advances in multi-agent communication through reinforcement learning. With over 900 cumulative citations, Gombolay has established himself as a defining voice in making human-robot teaming both computationally tractable and deeply human-centered.

Research Focus

Key Achievements

Top Papers

1. 1
   Decision-making authority, team efficiency and human worker satisfaction in mixed human–robot teams

   251 citations · 2015
2. 2
   Effects of Anthropomorphism and Accountability on Trust in Human Robot Interaction

   213 citations · 2020
3. 3
   Computational design of mixed-initiative human–robot teaming that considers human factors: situational awareness, workload, and workflow preferences

   152 citations · 2017
4. 4
   Fast Scheduling of Robot Teams Performing Tasks With Temporospatial Constraints

   124 citations · 2018
5. 5
   Fast Scheduling of Multi-Robot Teams with Temporospatial Constraints

   106 citations · 2013
6. 6
   Learning Scheduling Policies for Multi-Robot Coordination With Graph Attention Networks

   89 citations · 2020
7. 7
   Robotic assistance in the coordination of patient care

   73 citations · 2018
8. 8
   Four Years in Review

   65 citations · 2020
9. 9
   Multi-Agent Graph-Attention Communication and Teaming

   59 citations · 2021
10. 10
    Heterogeneous graph attention networks for scalable multi-robot scheduling with temporospatial constraints

    55 citations · 2021

Key Collaborators

Contact & Links