Mark Murnane

University of Maryland, Baltimore County, University of Maryland, College Park

Papers

Total Citations

H-Index

About

Mark Murnane is a researcher specializing in human-robot interaction (HRI), virtual reality, and simulation-based data collection for robotics systems. His work addresses one of the most pressing challenges in modern robotics: the scalability and reproducibility of HRI studies, which traditionally demand physical robots, prepared environments, and scarce human participants. Murnane's most notable contributions center on developing virtual reality frameworks as practical alternatives to costly, failure-prone physical robot setups. His 2019 paper on VR and photogrammetry for HRI reproducibility demonstrated how immersive simulation environments could overcome traditional data collection bottlenecks, while his complementary work on learning from human-robot interactions in modeled scenes explored how simulated settings could generate valuable machine learning training data. Building on these foundations, his 2021 simulator paper introduced a comprehensive suite of tools enabling researchers to model robots, sensors, and environments entirely within VR, using rigged photogrammetric avatars to replicate real human behavior. With citations accumulating across these interconnected works, Murnane's research sits at a productive intersection of robotics, immersive technology, and artificial intelligence. His contributions are particularly valuable for researchers seeking reproducible, scalable methodologies for studying and improving how robots perceive and interact with humans in real-world settings.