Kyhyun Um
Papers
9
Total Citations
51
H-Index
5
About
Kyhyun Um is a researcher whose work bridges mobile robotics, terrain modeling, and human-robot interaction, with particular emphasis on enabling autonomous and remotely operated robots to navigate and serve users intelligently in complex real-world environments. His most recognized contribution, "Intuitive Terrain Reconstruction Using Height Observation-Based Ground Segmentation and 3D Object Boundary Estimation" (2012, 16 citations), introduced innovative voxel map and textured mesh generation techniques that dramatically improve situational awareness for remote robot operators. Building on this foundation, Um developed real-time traversable ground surface segmentation systems and multi-sensor terrain storage frameworks, significantly advancing mobile mapping capabilities for field robotics applications. Beyond terrain analysis, Um has made meaningful contributions to intelligent robot behavior through reinforcement learning and human-robot interaction. His work on demonstration-based learning combined with Q-learning in pervasive sensing environments, alongside research into dynamic obstacle avoidance and automated space classification for network robots, demonstrates a commitment to making robots genuinely responsive and adaptive in ubiquitous computing settings. His exploration of big data frameworks for robot services further reflects a forward-thinking approach to scalable robotic intelligence. With a cumulative citation record spanning multiple interdisciplinary domains, Um's research offers valuable insights for students and practitioners working at the intersection of robotics, machine learning, and smart environment technologies.
Research Focus
Key Achievements
Top Papers
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- 7Reactive virtual agent learning for NUI-based HRI applications3 citations · 2014
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- 9Robot Service Framework Based on Big Data Technology2 citations · 2014