Ran Qin

Papers

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About

Ran Qin is a leading researcher in robotic manipulation and computer vision, whose work focuses on advancing planar grasp detection—a fundamental task for enabling robots to interact with their environment. His most-cited paper, "RGB-D Grasp Detection via Depth Guided Learning with Cross-modal Attention" (2023, 20 citations), introduces a novel approach that leverages cross-modal attention mechanisms to integrate texture and shape information from RGB-D sensors. This work addresses a critical challenge: depth maps from consumer-grade sensors often suffer from low quality, yet Qin’s method effectively guides learning using depth data to improve grasp detection accuracy. By fusing visual and depth features, his contributions enhance robotic perception and dexterity, directly impacting applications in automation and service robotics. Qin’s research is notable for its practical focus on real-world sensor limitations, and his cross-modal attention framework has been recognized as a key step toward more robust and reliable robotic grasping systems. With growing citation impact, his work continues to influence both academic research and industrial robotics, making him a rising voice in the field.