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About

Haoruo Zhang is a leading researcher in robotic perception and manipulation, with a focus on 6D object pose estimation for industrial automation. His work addresses the critical challenge of enabling robots to accurately detect and grasp texture-less industrial parts—a fundamental requirement for bin-picking and assembly tasks. Zhang’s most-cited paper, "Texture-less object detection and 6D pose estimation in RGB-D images" (2017, 29 citations), established a robust framework for handling objects lacking visual features. He advanced this with "Detect in RGB, Optimize in Edge" (2019, 24 citations), which achieves precise pose estimation from a single RGB image by leveraging edge information, directly solving real-world industrial problems. His contributions extend to robotic calibration, as seen in "Robotic hand-eye calibration with depth camera: A sphere model approach" (2018, 21 citations), which simplifies the complex transformation between a depth camera and robot wrist. Additionally, Zhang has explored panoramic visual odometry for navigation and compliant robotic assembly systems using multiple sensors. With over 95 total citations, his work is foundational for researchers and engineers developing autonomous robots for manufacturing, logistics, and beyond.