Ziang Xie

Papers

Total Citations

H-Index

About

Ziang Xie is a roboticist whose research centers on perception and manipulation in unstructured environments, with a particular focus on object recognition and deformable object handling. His work bridges computer vision and motion planning to enable robots to interact with the physical world more intelligently. Xie’s most cited paper, “Multimodal Blending for High-Accuracy Instance Recognition” (2013, 56 citations), introduces a novel approach that fuses depth and visual data from sensors like the Microsoft Kinect to achieve robust instance recognition in cluttered tabletop settings—a critical step toward practical robotic perception. In parallel, his paper “A Constraint-Aware Motion Planning Algorithm for Robotic Folding of Clothes” (2013, 22 citations) tackles the notoriously difficult problem of manipulating deformable objects, presenting a planner that accounts for material constraints to successfully fold garments. These contributions have influenced subsequent work in both robotic grasping and domestic automation. Xie’s research demonstrates a commitment to solving real-world challenges, from identifying objects with high accuracy to performing complex manipulation tasks, making his work valuable for students and researchers advancing autonomous systems in everyday environments.