Max Robotics — Global Service Robot Directory + US Certification

About

Yuyin Guan is a leading researcher in robotic perception and manipulation, with a focus on enabling robots to interact intelligently with complex, real-world environments. Their work centers on three key areas: transparent object perception, category-level 6-D pose estimation, and efficient robotic grasping in cluttered scenes. Guan’s major contributions include the development of TCRNet, a novel cascade refinement network for transparent object depth completion that addresses the unique challenges posed by transparent materials—objects notoriously difficult for standard RGB-based systems to detect. This work has garnered 20 citations since 2024. In category-level 6-D pose estimation, Guan tackled the critical synthetic-to-real domain gap, proposing a shape deformation approach that enhances robotic grasp detection, a paper with 12 citations. Their research on efficient pushing and grasping methods in cluttered environments (9 citations) and the SGNet suction grasp detection system with multiscale attention (4 citations) further demonstrates their impact. Guan’s work is notable for bridging the gap between simulation and real-world application, advancing the reliability and accuracy of robotic manipulation in industrial sorting, assembly, and handling tasks.