Seungwon Lim

Papers

Total Citations

H-Index

About

Seungwon Lim is a researcher at the forefront of human-robot interaction, specializing in making robotic agents more reliable and intuitive through the integration of large language models (LLMs). His key research areas include command classification, ambiguity resolution, and uncertainty estimation in interactive robotic systems. Lim’s most notable contribution is the development of **CLARA** (Classifying and Disambiguating User Commands for Reliable Interactive Robotic Agents), a framework that enables robots to intelligently assess whether a user’s command is clear, ambiguous, or infeasible. By leveraging uncertainty estimation methods for LLMs, CLARA allows robots to proactively seek clarification or flag impossible requests, significantly improving the robustness and safety of human-robot collaboration. This work has garnered substantial attention, with his primary paper accumulating **19 citations** and a related publication adding **5 more**, reflecting its growing impact in the robotics and AI communities. Lim’s research addresses a critical bottleneck in deploying LLM-powered robots in real-world settings, paving the way for more trustworthy and responsive autonomous agents. His work is essential reading for anyone interested in bridging the gap between natural language understanding and practical robotic control.