Papers
9
Total Citations
315
H-Index
6
About
Youngmok Yun is a leading researcher in rehabilitation robotics and human–robot interaction, with a primary focus on developing intelligent, force-controlled exoskeletons for restoring upper-limb function after neurological injury. His most influential work, an index finger exoskeleton with series elastic actuation (183 citations), introduced a novel design that enables precise force control for hand rehabilitation—a critical capability for restoring independence in daily activities. Yun also made foundational contributions to the characterization of Bowden cable friction (59 citations), developing a systematic experimental method that remains essential for designing cable-driven wearable robots. His research extends to odometry calibration for mobile robots, where he pioneered the use of terminal iterative learning control to correct systematic navigation errors. Notably, Yun developed READAPT, a hand-wrist exoskeleton for coordinated movement training, and advanced finger pose estimation using motion capture systems for real-time, robust tracking. He also introduced the concept of “Control in the Reliable Region of a Statistical Model” (CRROS), a novel algorithm that ensures safe control by avoiding regions where model predictions are uncertain. With a body of work spanning rehabilitation, human motion estimation, and statistical control, Yun’s research has directly shaped the design and control of next-generation wearable robotic systems.
Research Focus
Key Achievements
Top Papers
- 1
- 2Experimental characterization of Bowden cable friction59 citations · 2014
- 3
- 4
- 5Odometry calibration using home positioning function for mobile robot14 citations · 2008
- 6Accurate, Robust, and Real-Time Pose Estimation of Finger9 citations · 2014
- 7
- 8Control in the Reliable Region of a Statistical Model4 citations · 2016
- 9Arm kinematics estimation with the harmony exoskeleton4 citations · 2017