Zixuan Fan

Beijing University of Posts and Telecommunications

Papers

Total Citations

H-Index

About

Zixuan Fan is a robotics researcher whose work sits at the intersection of human intent prediction, assistive robotics, and computer vision. His primary research areas include gaze-based control systems, locomotion intent prediction on rough terrains, and automated industrial manipulation. Fan’s most significant contribution is his pioneering approach to predicting foot placement for assistive walking by fusing sequential 3D gaze data with environmental context—a method that addresses the critical challenge of navigating unstructured, rough terrains, where traditional intent prediction models fail. This work, his most cited with 28 citations, has direct implications for improving the autonomy and safety of exoskeletons and prosthetic devices. He has also advanced the field of Supernumerary Robotic Limbs (SRL) by developing a gaze-signal-based control method that enhances operational efficiency by leveraging task and environmental information. In industrial robotics, Fan proposed a robust workpiece localization method for robotic de-palletizing that uses region growing and Progressive Probabilistic Hough Transform (PPHT) to overcome unstable ambient lighting conditions. His research bridges the gap between human cognitive signals and robotic action, making assistive and industrial robots more responsive in complex, real-world environments.