Papers
3
Total Citations
12
H-Index
3
About
Hyunwoo Kim is a robotics and machine learning researcher whose work spans autonomous navigation, 3D spatial mapping, and intelligent human motion analysis. His research bridges the gap between traditional sensor-based robotics and modern data-driven approaches, making meaningful contributions to both fields. Among his most recognized contributions is his 2023 work on gait phase prediction, which garnered 5 citations and introduced a novel semi-supervised deep domain adaptation framework combined with pseudo-labeling to build personalized gait models without the need for costly experimental data collection — a significant practical advancement for rehabilitation engineering and wearable robotics. His earlier work on 3D map building for mobile robots navigating slanted surfaces, cited 4 times, addressed a critical limitation in prior mapping systems that assumed flat terrain, expanding the real-world applicability of autonomous robots. His lane detection algorithm using laser range finders, cited 3 times, further demonstrates his commitment to robust, sensor-driven solutions for autonomous vehicle safety. Collectively, Kim's research reflects a career dedicated to making autonomous systems smarter, safer, and more adaptable — whether navigating complex physical environments or interpreting nuanced human movement patterns.
Research Focus
Key Achievements
Top Papers
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