Min-Gu Kim
Papers
1
Total Citations
2
H-Index
1
About
Min-Gu Kim is a researcher whose work lies at the intersection of human-robot interaction (HRI), machine learning, and social robotics. His key research focuses on enabling robots to autonomously learn and reproduce social skills by modeling dynamic human behaviors. In his notable 2014 paper, "Learning of social skills for Human-Robot Interaction by hierarchical HMM and interaction dynamics," Kim introduced a framework that segments motion trajectories of both humans and robots, representing social skills through hierarchical hidden Markov models (HMMs). This approach allows robots to adapt their responses in real-time, moving beyond pre-programmed actions toward more fluid, context-aware interactions. While his citation count is modest, his work contributes foundational ideas to the growing field of socially intelligent robotics, particularly in how machines can interpret and mirror human interaction dynamics. Kim’s research is especially relevant for students and engineers developing assistive or collaborative robots, as it emphasizes learning from demonstration and adaptive behavior. His contributions highlight the importance of bridging computational models with real-world social cues, paving the way for more natural and effective human-robot collaboration.
Research Focus
Key Achievements
Top Papers
- 1