Cornelia Murko
Papers
3
Total Citations
78
H-Index
3
About
Cornelia Murko is a researcher specializing in human-robot interaction (HRI) and human-robot collaboration (HRC), with a particular focus on situation awareness, human attention processes, and gaze-based behavioral analysis. Her most influential work centers on developing innovative methodologies to measure and predict situation awareness in real-time, leveraging eye tracking technology, 3D gaze analysis, and probabilistic frameworks to optimize how humans and robots work together. Murko's landmark contributions, including her 2017 papers on probabilistic attention frameworks for HRI — each garnering 33 citations — established a rigorous approach to quantifying human awareness of scene objects of interest during robot interactions. By translating gaze features into actionable performance predictions, her research bridges cognitive science and robotics engineering in a meaningful and practical way. Her 2019 study expanded this methodology to incorporate head gaze alongside eye tracking, further refining situation awareness estimation within collaborative human-robot environments, accumulating an additional 12 citations. Collectively, Murko's work has made a tangible impact on the field by providing researchers and engineers with real-time, data-driven tools to evaluate human cognitive states, ultimately advancing the design of safer, more intuitive human-robot systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2
- 3