Atsunobu Kotani

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About

Atsunobu Kotani is a roboticist whose research lies at the intersection of manipulation, learning from demonstration, and human-robot interaction. He is best known for pioneering work in teaching robots to perform delicate, human-like tasks, most notably in his highly cited 2019 paper "Teaching Robots To Draw" (49 citations). In this work, Kotani introduced a novel approach enabling manipulator robots to replicate handwritten characters and line drawings by inferring a drawing plan from a single image of the input. This contribution demonstrated how robots can learn fine motor skills from visual examples, bridging the gap between perception and dexterous control. His research has significant implications for assistive robotics, creative automation, and intuitive human-robot teaching interfaces. Kotani’s work is characterized by its focus on making robots more accessible and capable of reproducing human artistic expression, a challenging domain that requires precise trajectory planning and adaptive control. His contributions continue to inspire researchers exploring how robots can learn from minimal human input, advancing the field toward more fluid and natural human-robot collaboration.