Ka-Ming Hui
Papers
1
Total Citations
34
H-Index
1
About
Ka-Ming Hui is a leading researcher in soft robotics and intelligent control systems, with a focus on developing adaptive manipulation strategies for compliant, continuum structures. His most-cited work, "Localized online learning-based control of a soft redundant manipulator under variable loading" (2018, 34 citations), addresses a critical challenge in soft robotics: maintaining precise control despite unpredictable external loads. Rather than relying on fragile model-based methods, Hui pioneered a localized online learning framework that enables soft manipulators to adapt in real time, preserving their inherent compliance while achieving robust performance. This contribution bridges the gap between the passive adaptability of soft robots and the need for reliable, task-oriented control—a key step toward practical deployment in unstructured environments. Hui’s research has influenced subsequent work on learning-based control for hyper-redundant and continuum robots, and his approach is cited in studies spanning medical robotics, industrial automation, and human-robot interaction. By combining machine learning with soft material dynamics, Hui continues to advance the frontier of robots that can safely and effectively operate alongside humans.
Research Focus
Key Achievements
Top Papers
- 1