Eunwoo Kim
Papers
9
Total Citations
98
H-Index
5
About
Eunwoo Kim is a leading researcher in autonomous robot navigation and task planning, with a focus on enabling robots to operate safely and intelligently in dynamic, human-centered environments. His early work pioneered the use of Gaussian process motion control for real-time navigation through crowded spaces, introducing the autoregressive Gaussian process motion model (AR-GPMM) to predict pedestrian trajectories from partial observations—a contribution that has garnered over 42 citations. He further advanced nonparametric reactive navigation and leveraged non-stationary Gaussian process regression to incorporate both positive and negative training data, significantly improving robot adaptability in unpredictable settings. More recently, Kim has turned to large language models (LLMs) for long-horizon task planning, developing self-corrective inverse prompting methods that enhance the accuracy of action sequences in complex domains like cooking. His work on time-varying preference bandits also personalizes robot behavior to individual user preferences. With a cumulative citation count exceeding 100 across his most influential papers, Kim’s research bridges probabilistic modeling and modern AI, offering practical solutions for real-world robotic autonomy.
Research Focus
Key Achievements
Top Papers
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- 8Time-Varying Preference Bandits for Robot Behavior Personalization1 citations · 2024
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