Papers
4
Total Citations
33
H-Index
3
About
Been Kim is a pioneering researcher in the field of human-robot collaboration and interpretable machine learning. Her work focuses on bridging the gap between autonomous systems and human teams, particularly in time-critical domains such as military operations and disaster response. Kim's major contributions center on developing generative modeling approaches that enable robots to infer task plans directly from human team meetings, reducing the burden of programming autonomous systems for complex, dynamic environments. Her research introduces logic-based priors to model human decision-making, allowing robots to understand and anticipate team strategies without explicit programming. With over 30 citations across her key papers, Kim's work has laid the foundation for more intuitive human-machine team planning. Notably, her 2015 paper "Inferring Team Task Plans from Human Meetings" and its 2013 predecessor represent significant advances in applying human-inspired techniques to autonomous systems, demonstrating how insights from human teamwork can be translated into computational models. Kim's research continues to shape how robots learn from and collaborate with people in high-stakes scenarios.
Research Focus
Key Achievements
Top Papers
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- 4Human-Inspired Techniques for Human-Machine Team Planning2 citations · 2012