Papers
39
Total Citations
645
H-Index
13
About
ChangHwan Kim is a robotics researcher whose work spans humanoid motion generation, robotic manipulation, tactile sensing, and human-robot interaction. His early and most influential contributions focused on enabling humanoid robots to replicate human movement with both naturalness and dynamic stability. His 2009 paper on whole-body motion generation from motion capture data (94 citations) established foundational kinematic and dynamic mapping techniques, complemented by subsequent work on evolutionary algorithm-based imitation learning and optimization-driven motion adaptation. These efforts collectively advanced the field of human-like robot behavior. Kim's research evolved to address the complexities of real-world manipulation, producing widely recognized algorithms for object retrieval in cluttered, confined environments—a notoriously difficult problem in task and motion planning. His 2020–2021 papers on fast and resilient manipulation planning (totaling over 60 citations) introduced practical frameworks that balance computational efficiency with robustness. A striking recent contribution is his 2023 crack-based tactile sensor (68 citations), demonstrating versatility in hardware design for small legged robots. Rounding out his portfolio is work on social robot behavior in privacy-sensitive contexts, reflecting his broader interest in robots that operate safely and naturally alongside humans. Across more than a decade, Kim has built a compelling body of work bridging robot intelligence, physical dexterity, and human-centered design.
Research Focus
Key Achievements
Top Papers
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- 5Fast and resilient manipulation planning for target retrieval in clutter39 citations · 2020
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- 8Effects of Social Behaviors of Robots in Privacy-Sensitive Situations23 citations · 2021
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