Papers
12
Total Citations
135
H-Index
8
About
Brian Reily is a robotics and artificial intelligence researcher whose work sits at the intersection of human-robot interaction, multi-agent systems, and machine learning. His research focuses on enabling robots to understand, predict, and respond to human and team behaviors in real time — capabilities essential for collaborative robotics in high-stakes environments such as disaster response, search and rescue, and assisted living. Reily's most cited work, "Skeleton-based bio-inspired human activity prediction for real-time human–robot interaction" (2017, 30 citations), laid an early foundation for his contributions to activity recognition using skeletal data. He has since advanced this area by incorporating multimodal learning, combining human pose estimation with object cues to improve recognition accuracy in dynamic, real-world settings. A recurring theme across his research is the challenge of coordinating heterogeneous multi-robot teams — from intelligent team assignment and sensor coverage optimization to maintaining communication during complex missions. His use of graph representation learning and graph embedding techniques reflects a sophisticated approach to modeling robot team structures and behaviors. With over 120 cumulative citations and a consistent publication record through 2021, Reily has established himself as a meaningful contributor to human-robot teaming, offering practical, learning-driven frameworks for autonomous systems operating alongside humans in unpredictable environments.
Research Focus
Key Achievements
Top Papers
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- 3Leading Multi-Agent Teams to Multiple Goals While Maintaining Communication15 citations · 2020
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- 8Robust Real-Time Group Activity Recognition of Robot Teams8 citations · 2021
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