Minsuk Chang
Papers
2
Total Citations
24
H-Index
2
About
Minsuk Chang is a researcher at the forefront of human-robot interaction and natural language processing, specializing in making robotic agents more reliable and intuitive through large language models (LLMs). Their most notable contribution is the CLARA system (Classifying and Disambiguating User Commands for Reliable Interactive Robotic Agents), which addresses a critical challenge in robotics: determining whether a user's command is clear, ambiguous, or infeasible. By developing an uncertainty estimation method for LLMs, Chang's work enables robots to proactively seek clarification or flag impossible requests, significantly enhancing safety and usability in real-world applications. This pioneering research has garnered over 24 citations since 2023, reflecting its immediate impact on the field. Chang's work bridges the gap between advanced AI language models and practical robotic systems, offering a pathway toward more trustworthy and responsive autonomous agents. Their research is particularly valuable for students and engineers working on interactive AI systems, as it provides a robust framework for handling the inherent ambiguity of human communication in robotic contexts.
Research Focus
Key Achievements
Top Papers
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