Papers
8
Total Citations
110
H-Index
5
About
Jangwon Lee is a leading researcher in human-robot interaction, with a focus on enabling robots to learn from and collaborate with humans through intuitive, vision-based methods. His work spans robot learning from demonstration, human-drone interaction, and activity forecasting. A major contribution is his development of Convolutional Future Regression, a novel approach that allows robots to learn new activities by watching unlabeled first-person human videos—a paradigm that has garnered over 34 citations. Lee has also pioneered gesture forecasting for human-drone interaction, creating systems that anticipate hand movements to make drone responses more natural and responsive. His survey on robot learning from demonstrations for human-robot collaboration, cited 21 times, has become a key reference in the field. Beyond these, Lee has explored behavioral personality in service robots and cognitive robotic architectures, demonstrating a long-standing commitment to making robots more perceptive and socially aware. His work is highly influential, with his most-cited papers collectively accumulating over 100 citations, shaping how robots learn from human behavior and interact seamlessly in dynamic environments.
Research Focus
Key Achievements
Top Papers
- 1
- 2Human-Drone Interaction24 citations · 2018
- 3A survey of robot learning from demonstrations for Human-Robot Collaboration21 citations · 2017
- 4Forecasting Hand Gestures for Human-Drone Interaction12 citations · 2018
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