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About

Xingkai Yu is a robotics researcher whose work centers on hand-eye calibration and vision-guided robotic systems, with a particular focus on industrial automation. His major contributions include developing novel methods for estimating the geometrical transformation between cameras and robots, which is essential for enabling robots to perceive and interact with their environments accurately. Yu’s 2024 paper on hand-eye parameter estimation using 3-D observation of a single marker has garnered 7 citations, while his work on the SCARA+ bin picking system—also with 7 citations—addresses the practical challenge of automatically picking randomly piled revolution-symmetry objects, offering commercial potential for streamlining production. His 2023 study on simultaneous hand-eye and target estimation via 2D-3D generative point alignment (4 citations) further advances calibration accuracy. Notably, Yu’s 2025 comparative study on nonsingular hand-eye and robot-world calibration for SCARA-type robots (2 citations) tackles the unique calibration challenges posed by four-degree-of-freedom SCARA arms, which are increasingly vital in industry for their speed and precision. Through these contributions, Yu is helping to make vision-guided robotics more reliable and efficient for real-world manufacturing applications.