Tat Hieu Bui
Papers
3
Total Citations
17
H-Index
2
About
Tat Hieu Bui is an emerging researcher specializing in robotic grasping, deep learning, and autonomous manipulation systems, with a particular focus on solving real-world challenges in industrial automation. His work centers on developing intelligent algorithms that enable robots to perceive and interact with cluttered environments — a critical capability for modern logistics and manufacturing driven by the rapid growth of e-commerce. Bui's most notable contribution is his end-to-end framework for 6-DoF antipodal grasp planning from point cloud data, which addresses the notoriously difficult random bin-picking task using single-view depth sensing. This work has garnered 11 citations since its 2023 publication, reflecting strong early interest from the robotics community. He further extended this expertise through CoAS-Net, a context-aware suction network trained on a large-scale domain-randomized synthetic dataset, demonstrating his commitment to bridging the sim-to-real gap in robotic perception. His most recent work explores fusion of deep learning and analytical methods to enhance grasping versatility in complex scenes. Collectively, Bui's research addresses fundamental bottlenecks in robotic pick-and-place systems, offering practical solutions that push the boundaries of autonomous robot manipulation in unstructured, real-world environments.
Research Focus
Key Achievements
Top Papers
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