Papers
32
Total Citations
565
H-Index
14
About
Young-Dae Hong is a leading figure in bipedal robotics and humanoid locomotion, whose work bridges the gap between theoretical control and real-world hardware. His core research areas include kinematic calibration, walking pattern generation, and stability control for legged robots. Hong’s most influential contribution is the development of the Modifiable Walking Pattern Generator (MWPG), which enables bipedal robots to independently adjust step length, walking period, and direction on uneven or inclined terrain—a critical advance over rigid, pre-planned gaits. His 2011 paper on laser-based kinematic calibration (109 citations) provides a systematic method for correcting manipulator errors, while his 2013 work on evolutionary-optimized central pattern generators (47 citations) demonstrates how bio-inspired neural networks can produce stable, dynamic walking with vertical center-of-mass motion. More recently, Hong has extended his expertise into wearable robotics, developing a single-leg knee exoskeleton for intention detection, and into stretchable electronics for human-machine interfaces. With over a decade of highly cited publications, Hong’s research has directly improved the autonomy, stability, and adaptability of bipedal systems, making him a key innovator in the field of robotic locomotion.
Research Focus
Key Achievements
Top Papers
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- 33-D Command State-Based Modifiable Bipedal Walking on Uneven Terrain45 citations · 2012
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