Yangbin Zheng

Zhejiang University of Science and Technology

Papers

Total Citations

H-Index

About

Yangbin Zheng’s research lies at the intersection of robotics, neural networks, and computer vision, with a focus on enabling machines to perceive and interact with complex environments. His early work on biped robot locomotion, particularly the 2003 study on gait synthesis for climbing sloping surfaces using neural networks (20 citations), laid foundational insights into dynamic learning for legged robots. More recently, Zheng has advanced robotic manipulation in medical and industrial settings. His 2023 paper on pixel-level collision-free grasp prediction for sorting medical test tubes on cluttered trays (6 citations) addresses a critical challenge in automating laboratory workflows, demonstrating how vision-based systems can handle unstructured environments with precision. In 2024, he extended his expertise to power systems with a study on ICP registration using SHOT descriptors for arrester point clouds (3 citations), proposing an automated method to overcome the limitations of traditional detection for regular, uniform components. Zheng’s work consistently bridges theoretical neural network models with practical, high-impact applications—from healthcare automation to infrastructure inspection—showcasing a career dedicated to making robots more adaptive and reliable in real-world tasks.