Xinyang Gao
Papers
1
Total Citations
2
H-Index
1
About
Xinyang Gao is a robotics researcher whose work centers on advancing precision in robotic perception and manipulation, with a particular focus on hand-eye calibration and uncertainty quantification. His most-cited paper, "A Self-Correctional Hand-Eye Calibration Regime Using Extensive Pose-Image Pairs with Quantifiable Gaussian Errors from Homogeneous Matrices Decomposition" (2022, 2 citations), tackles a fundamental challenge in robotics: accurately aligning a robot's coordinate frame with its camera's view. While traditional eye-to-hand calibration methods rely on 10-20 pose pairs to solve the AX=XB equation, Gao’s key contribution lies in introducing a self-correcting framework that quantifies Gaussian errors through homogeneous matrix decomposition. By leveraging extensive pose-image pairs, his approach not only improves calibration accuracy but also provides measurable uncertainty estimates—a critical step for reliable autonomous systems. This work bridges the gap between theoretical robotics and practical deployment, offering a robust solution for applications in industrial automation and surgical robotics. Though early in his career, Gao’s emphasis on error quantification signals a promising trajectory toward more trustworthy and adaptive robotic systems.
Research Focus
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Top Papers
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