Beom‐Jin Lee

Papers

Total Citations

H-Index

About

Beom-Jin Lee is a leading researcher in social robotics, with a focus on developing intelligent, perception-driven systems that enable robots to interact naturally with humans in dynamic environments. His work centers on three key areas: robust human following, human body orientation estimation, and integrated perception-action-learning architectures. Lee’s most influential contribution is his deep Bayesian trajectory prediction model for home service robots, which allows robots to reliably follow a person even when the target is temporarily lost or the environment changes—a critical advancement for assistive robotics. This work has garnered 38 citations, reflecting its impact on the field. He also pioneered a convolutional neural network approach for estimating human body orientation, enabling robots to anticipate user intent without requiring direct face-to-face positioning, a paper cited 25 times. More recently, Lee developed a unified perception-action-learning system that incorporates state-of-the-art deep learning techniques to solve complex social service tasks, demonstrating fast and robust performance. His research bridges the gap between theoretical AI and practical robot deployment, making him a notable figure in the advancement of socially aware, mobile service robots.