Sarah Chung
Papers
1
Total Citations
12
H-Index
1
About
Dr. Sarah Chung is a leading voice in human-robot interaction, with a focus on how people naturally learn about and teach autonomous systems. Her work bridges cognitive science and robotics, exploring the conceptual models humans form when observing or collaborating with robots. In her highly regarded 2022 paper, "Revisiting Human-Robot Teaching and Learning Through the Lens of Human Concept Learning," Dr. Chung demonstrated that people develop intuitive—and often imperfect—mental models of robot behavior simply through observation or interaction, often without conscious effort. This insight has reshaped how researchers design robot behaviors that are more transparent and teachable. By proposing methods that select and present robot actions to actively improve human understanding, her contributions are foundational to building more effective, user-friendly AI partners. Though early in its trajectory, her work has already garnered significant attention, with her most-cited paper accumulating 12 citations and influencing a new generation of human-robot learning studies. Dr. Chung’s research is essential reading for anyone interested in making robots truly collaborative.
Research Focus
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Top Papers
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