Papers
1
Total Citations
3
H-Index
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About
Yueqi An is a researcher focused on human factors engineering and human-robot interaction, with a particular emphasis on operator performance in teleoperation systems. Their key research area centers on understanding and predicting how operators allocate visual attention in high-workload digital interfaces, especially during robotic arm teleoperation tasks. An’s major contribution lies in applying and validating the SEEV (salience, effort, expectancy, value) model—a framework that integrates both top-down cognitive goals and bottom-up sensory cues—to predict visual attention allocation in complex control environments. Their most-cited work, “Operator visual attention allocation prediction in a robotic arm teleoperation interface” (2023), demonstrates how this model can be used to design more intuitive interfaces that reduce operator error and improve task efficiency. While still early in their career, with this paper garnering 3 citations, An’s research addresses a critical challenge in teleoperation: ensuring that operators can efficiently gather information under high visual workload. This work has practical implications for improving safety and performance in remote surgery, hazardous material handling, and space exploration applications. An’s contributions are paving the way for more adaptive, human-centered interface designs in advanced robotic systems.
Research Focus
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Top Papers
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