KangKang Yin

National University of Singapore, Simon Fraser University

Papers

Total Citations

H-Index

About

KangKang Yin is a leading researcher in robotics and computer graphics, whose work bridges the gap between human-like dexterity and autonomous robot control. Her primary research areas include humanoid robot locomotion, dexterous manipulation, and visual navigation for ground vehicles. Yin’s most influential contribution is her pioneering work on direction-changing fall control for humanoid robots, a study that has garnered 43 citations and established foundational theory and experimental methods for preventing catastrophic falls in bipedal systems. More recently, she has tackled the complex challenge of learning dexterous manipulation, exemplified by her 2022 paper on chopsticks-based object relocation. This work, with 25 citations, demonstrates how robots can master intricate, tool-mediated tasks that require delicate hand-object interactions—a long-standing hurdle in both graphics and robotics. Additionally, her research on robust visual teach-and-repeat using 3D semantic maps (10 citations) advances autonomous navigation for unmanned ground vehicles, making them resilient to changes in starting pose. Yin’s work is notable for its practical impact, combining rigorous theory with real-world experimentation, and she continues to push the boundaries of how robots learn and interact with their environments.