Yu-Lun Liu

National Tsing Hua University, National Yang Ming Chiao Tung University

Papers

Total Citations

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About

Yu-Lun Liu is a leading researcher at the intersection of augmented reality (AR), human-robot collaboration, and robotic manipulation. His work fundamentally advances how humans and robots work together in industrial settings, with a particular focus on assembly tasks. Liu’s most influential contribution is his 2023 study on AR user interface design for human-robot collaborative assembly, which has garnered 58 citations—a strong indicator of its impact on the field. This work provides critical experimental evidence for how intuitive AR interfaces can improve efficiency and safety in shared workspaces. More recently, Liu has pushed the boundaries of robotic precision with his 2024 paper on “Precise Pick-and-Place using Score-Based Diffusion Networks.” In this innovative work, he introduces a coarse-to-fine continuous pose diffusion method that leverages diffusion networks to dramatically enhance object pose perception. This approach promises to solve one of robotics’ most persistent challenges: achieving the high precision needed for reliable pick-and-place operations in unstructured environments. Liu’s research is not only technically rigorous but also practically oriented, directly addressing real-world manufacturing needs. His growing body of work positions him as a key voice in shaping the future of human-robot interaction and autonomous manipulation.