Hanxi Yin

Papers

Total Citations

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About

Hanxi Yin has made impactful contributions at the intersection of computer vision and robotic manipulation, with a primary focus on RGB-D perception and grasp planning. Her most cited work, "RGB-D Instance Segmentation-based Suction Point Detection for Grasping" (2022, 4 citations), addresses a critical challenge in industrial robotics: reliably evaluating suction positions on objects of varying shapes. By integrating instance segmentation with depth sensing, Yin’s method moves beyond traditional two-stage decoupled approaches, enabling more stable and adaptive suction-based grasping. This work is particularly notable for its practical implications in automated manufacturing and logistics, where suction offers higher reliability than parallel-jaw grippers. Yin’s research demonstrates a keen ability to bridge advanced perception algorithms with real-world robotic systems, and her findings have been recognized for their potential to enhance autonomous picking in unstructured environments. As a researcher, she continues to explore how deep learning can refine robotic interaction with diverse objects, making her a promising voice in the field of intelligent robotics and computer vision.