Youngjae Yu
Papers
3
Total Citations
36
H-Index
3
About
Youngjae Yu is a rising researcher at the intersection of robotics, natural language processing, and human-robot interaction. His work centers on making robotic assistants more intelligent and reliable by enabling them to understand and act upon ambiguous or complex human commands. In his highly cited paper "CLARA: Classifying and Disambiguating User Commands for Reliable Interactive Robotic Agents" (2023, 19 citations), Yu introduces a novel method that uses uncertainty estimation in large language models (LLMs) to determine whether a user’s command is clear, ambiguous, or infeasible—a critical step toward safer and more intuitive robot operation. Building on this, his "Zero-shot Active Visual Search (ZAVIS)" (2023, 12 citations) tackles the challenge of enabling mobile robots to find objects described in free-form text without prior training, a breakthrough for assistive robotics. With a growing citation impact and a focus on practical, real-world applications, Yu is shaping the future of how robots interpret and execute human intentions, making them more adaptable and trustworthy in everyday environments.
Research Focus
Key Achievements
Top Papers
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