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About

Minae Kwon is a robotics and human-robot interaction (HRI) researcher whose work bridges the gap between intelligent robot systems and the complexities of real human behavior. She is best known for her influential 2016 work on human expectations of social robots, which challenged a foundational assumption in HRI — that increasing a robot's social capabilities necessarily improves collaboration. With over 150 citations across versions of that paper, Kwon demonstrated that enhanced robot sociability can create dangerous expectations gaps, reshaping how the field thinks about robot design philosophy. Her research extends into robot learning and collaboration under realistic human conditions. Her 2020 work on risk-aware human modeling (53 citations) advances the idea that robots must account for non-optimal human behavior to collaborate safely and efficiently. More recently, Kwon has pushed into language-guided robot generalization, developing methods for robots to distill corrective human feedback into transferable knowledge, and exploring grounded commonsense reasoning to help robots navigate nuanced real-world scenarios. Her work on human-robot team dynamics further addresses how robots can model emergent group behaviors like leading and following. Across her career, Kwon has consistently worked to make robots more socially intelligent, realistic, and trustworthy partners for humans.