Xinggang Fan

Zhejiang University of Technology, Zhijiang College of Zhejiang University of Technology

Papers

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About

Xinggang Fan is a leading robotics researcher whose work bridges bio-inspired mechanism design and cutting-edge deep learning for autonomous manipulation. His early, highly influential work on the "Development of a miniature self-stabilization jumping robot" (2009, 40 citations) introduced a novel two-mass-spring model that enables a thrown robot to right itself and execute a single, powerful jump. This foundational contribution to mobile sensor networks and field robotics demonstrated a simple yet elegant solution to the challenge of self-righting in small-scale systems. More recently, Fan has advanced the frontier of robotic perception with his work "An Attention-Based Approach for Enhanced Robot Grasp Detection in Unstructured Environments" (2024). By integrating attention mechanisms into grasp detection networks, his research significantly improves a robot's ability to handle occlusion, irregular layouts, and complex backgrounds—critical for real-world deployment. This work underscores his shift toward intelligent, data-driven manipulation. With a career spanning from elegant mechanical design to sophisticated neural architectures, Fan’s research consistently tackles core problems in robot autonomy, making him a notable figure in both field robotics and industrial automation.