Xiaoyu Li

Papers

Total Citations

H-Index

About

Xiaoyu Li is a pioneering researcher in robotics and 3D perception, whose work bridges the gap between autonomous systems and human-robot interaction. Li’s primary research areas include 3D multi-object tracking (MOT) and intuitive robot teaching, with a focus on enabling mobile robots to navigate and interact intelligently with dynamic environments. Their most impactful contribution is the development of **Poly-MOT**, a polyhedral framework for 3D multi-object tracking that revolutionizes how robots perceive and predict motion trajectories of surrounding objects. By addressing the limitations of single-metric data association in existing methods, Poly-MOT (52 citations) empowers robots to make better-informed motion planning and navigation decisions—a critical advancement for autonomous driving and warehouse logistics. In earlier work, Li explored human-robot collaboration through an intuitive robot teaching method that uses hand-gesture recognition via depth cameras and Support Vector Machine (SVM) classification (2016, 6 citations). This research laid the groundwork for more accessible robot programming, allowing non-experts to guide robots through demonstration. With a growing citation footprint and a focus on practical, real-world applications, Xiaoyu Li continues to shape the future of intelligent robotics.