Connor M. Sheehan

The University of Texas at Austin

Papers

1

Total Citations

7

H-Index

1

About

Connor M. Sheehan is a researcher advancing the frontier of human-robot interaction, with a focus on multi-robot systems and autonomous navigation guidance. His work addresses a critical challenge: how humans can intuitively and effectively direct teams of robots using natural language. In his most-cited paper, "Optimal Use of Verbal Instructions for Multi-robot Human Navigation Guidance" (2019), Sheehan explores the design of verbal commands that maximize clarity and efficiency when humans guide multiple robots through complex environments. This contribution bridges robotics, cognitive science, and human factors, offering practical frameworks for deploying robot teams in search-and-rescue, logistics, and collaborative manufacturing. Though early in his career, his research has already garnered attention (7 citations), laying groundwork for more intuitive human-swarm interfaces. Sheehan’s work stands out for its emphasis on real-world applicability and user-centered design, promising to shape how future operators interact with autonomous systems.

Research Focus

Key Achievements

1
H-Index
1
Papers
7
Total Citations
7
Avg Citations/Paper
🏆 Most Cited Paper
Optimal Use of Verbal Instructions for Multi-robot Human Navigation Guidance
7 citations · 2019
📈 Most Prolific Year: 2019 (1 Papers)
🤝 Key Collaborators: 6
🏛 Institutions: The University of Texas at Austin

Top Papers

  1. 1

Key Collaborators

Contact & Links

Available for collaboration
Content generated · 11 days ago