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About

Daewon Lee is a robotics researcher whose work sits at the intersection of tactile sensing, proximity detection, and human-robot interaction. His research focuses on equipping robots with rich sensory capabilities that enable safer, more dexterous, and more collaborative operation in dynamic real-world environments. Lee's most influential contribution, "Enabling Low-Cost Full Surface Tactile Skin for Human Robot Interaction" (2022, 31 citations), addresses a long-standing challenge in robotics by realizing affordable, full-coverage tactile sensing for robot bodies — a breakthrough with profound implications for collaborative robotics. Building on this theme, his "AuraSense" system (2021, 20 citations) demonstrated how full-surface proximity detection can be used to preemptively avoid collisions, significantly advancing robot safety. Lee has also explored fine-grained manipulation tasks, including sensor-guided pouring using tactile and proprioceptive feedback, multi-modal learning from human demonstrations, and acoustic-based pre-touch sensing with the innovative SonicFinger gripper. His earlier work on imitation learning further underscores his interest in bridging perception and planning. Collectively, Lee's research pushes robots toward the kind of nuanced, full-body sensory awareness that safe human-robot collaboration demands.