Chloe Kuo

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About

Chloe Kuo is a rising researcher at the intersection of human-robot interaction, embodied learning, and explainable AI. Her work centers on making robot programming accessible and intuitive for non-experts, with a particular focus on personalized education and interpretable control architectures. Kuo’s most-cited paper, “Kinesthetic Curiosity: Towards Personalized Embodied Learning with a Robot Tutor Teaching Programming in Mixed Reality” (2021, 12 citations), pioneers a novel approach to programming education by leveraging mixed reality and physical robot interaction to adapt to individual learners’ curiosity and engagement. This work demonstrates how embodied, kinesthetic experiences can transform abstract programming concepts into tangible, personalized learning journeys. In her 2022 paper “RE:BT-Espresso: Improving Interpretability and Expressivity of Behavior Trees Learned from Robot Demonstrations” (12 citations), Kuo addresses a critical challenge in robot task-level planning by enhancing behavior trees—hierarchical control architectures—through improved conversion algorithms from decision trees. Her contributions make complex robot planning more transparent and accessible, empowering non-expert users to understand and customize robot behaviors. Kuo’s research, though early in her career, has already garnered attention for its innovative fusion of education, robotics, and interpretability, positioning her as a promising voice in the future of human-centered AI and robotics.