Michael Guilfoyle
Papers
5
Total Citations
17
H-Index
3
About
Michael Guilfoyle is a leading researcher at the intersection of robotics, artificial intelligence, and human factors engineering, with a primary focus on **human–robot collaboration (HRC)** and **human–robot teaming (HrT)**. His work addresses the critical challenge of enabling safe, productive, and intuitive interactions between humans and autonomous systems in industrial and collaborative environments. A major contribution is his development of a **safety-driven deep reinforcement learning (DRL) framework** that integrates ISO 10218 safety constraints directly into robotic training, bridging the simulation-to-reality (Sim2Real) gap. He has also pioneered research into **mutual performance monitoring** for action recognition in teams, and established a roadmap for **data quality standards in collaborative intelligence (CI)**. Guilfoyle’s work on quantifying the **safety-productivity trade-off** in cobot applications has been foundational for factory-floor implementations. His studies on **programming by demonstration (PbD)** explore how non-expert users can effectively transfer skills to robots, emphasizing human factors. With over 17 citations across his most recent high-impact papers (2022–2024), Guilfoyle’s research is shaping the next generation of trustworthy, human-aware autonomous systems.
Research Focus
Key Achievements
Top Papers
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