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About

Jae Hee Lee is a researcher at the intersection of robotics, artificial intelligence, and cognitive science, with a primary focus on enabling robots to learn and reason about language and space in human-like ways. His work centers on three key areas: robotic language learning, occlusion reasoning, and qualitative spatial reasoning. Lee’s most notable contribution is the development of a Language-Model-Based Paired Variational Autoencoder for robotic language learning, inspired by how human infants acquire language through environmental interaction—a paper that has garnered 9 citations since 2022. He has also advanced robotic perception by introducing occlusion reasoning for efficient object existence prediction, allowing robots to reason about hidden objects in cluttered environments (5 citations). In earlier work, Lee explored the computational complexity of qualitative reasoning about relative directions, providing practical algorithms for spatial navigation. His research bridges symbolic reasoning and neural learning, demonstrating how robots can flexibly translate between actions and language descriptions. Lee’s interdisciplinary approach, combining insights from developmental psychology, linguistics, and computer science, positions him as a rising voice in creating more adaptive, cognitively-inspired robotic systems.