Papers
12
Total Citations
142
H-Index
6
About
Sangmoon Lee is a leading researcher in robotics and control systems, whose work bridges the gap between intelligent estimation, resilient control, and human-robot interaction. His primary research areas include external torque estimation for safe human-robot collaboration, sampled-data state estimation for neural networks like LSTM, and sound source localization for robotic auditory systems. Lee’s major contributions are exemplified by his highly cited work on higher order sliding-mode observers for robot manipulators (66 citations), which enables real-time external torque monitoring crucial for cooperative tasks. He has also advanced resilient control strategies for complex systems, including fuzzy semi-Markovian jump systems and fractional-order multiagent networks, integrating reinforcement learning and event-triggered mechanisms to counter actuator faults and cyber-attacks. Notably, his recent survey on Embodied AI highlights his forward-looking perspective on active interaction in real-world environments. With over 130 citations across his top papers, Lee’s research has significantly impacted both theoretical control design and practical robotic applications, from imitation learning for writing tasks to robust sound localization, making him a key figure in developing safer, more intelligent autonomous systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Sampled-Data State Estimation for LSTM18 citations · 2024
- 3Estimation of multiple sound source directions using artificial robot ears12 citations · 2013
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- 8Cleansed PHAT GCC based sound source localization4 citations · 2010
- 9Sound source localization in median plane using artificial ear4 citations · 2008
- 10Sampled-Data MPC for Leader-Following of Multi-Mobile Robot System4 citations · 2018