About

Dongheui Lee is a prominent robotics researcher whose work spans human-robot interaction, imitation learning, motion primitive acquisition, and physical human-robot cooperation. She has made foundational contributions to how robots learn from and collaborate with humans, developing innovative frameworks for incremental learning of full-body motion primitives from human observation — a line of work that has garnered over 220 citations. Her research on kinesthetic teaching, in which robots acquire new skills through direct physical guidance, has significantly advanced intuitive robot programming, reflected in her widely cited work on motion refinement tubes and attentional supervision of structured tasks. Lee's contributions extend to haptic cooperation, where she pioneered experience-driven robotic assistants capable of acquiring human task knowledge to improve physical collaboration. Her risk-sensitive optimal feedback control framework addressed critical safety challenges in uncertain human-robot interaction scenarios, while her adaptive impedance control architecture for bilateral teleoperation has opened new directions for contact-rich remote manipulation. She also contributed meaningfully to humanoid robotics through motion capture-based imitation systems and torque-controlled biped development. With multiple papers exceeding 100 citations and a cumulative body of work recognized across international venues, Lee stands as an influential figure shaping the future of intelligent, human-centered robotic systems.

Research Focus

Key Achievements

29
H-Index
121
Papers
2,885
Total Citations
24
Avg Citations/Paper
🏆 Most Cited Paper
Incremental learning of full body motion primitives and their sequencing through human motion observation
222 citations · 2011
📈 Most Prolific Year: 2022 (11 Papers)
🤝 Key Collaborators: 146
🏛 Institutions: Technical University of Munich, The University of Tokyo, Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR), TU Wien, Augsburg University, Korea Institute of Science and Technology

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
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