Dingzhe Li

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About

Dingzhe Li is a robotics researcher whose work bridges the critical gap between perception and manipulation for challenging real-world objects. His primary research areas include robotic grasping, 3D reconstruction, and surgical robotics, with a particular focus on handling transparent and specular objects—a notoriously difficult problem in computer vision and robotics. Li’s most impactful contribution is **ASGrasp**, the first generalizable 6-DoF grasp detection network designed specifically for transparent objects using RGB-D active stereo cameras. This work, published in 2024 with 14 citations, addresses the fundamental limitation of depth cameras failing to recover accurate geometry from reflective surfaces, opening new possibilities for industrial and service robotics. In the surgical domain, Li has also advanced **oscillating saw calibration for mandibular osteotomy robots**, achieving precise tool center position and plane orientation calibration essential for accurate bone cutting. This work, though early in its citation trajectory, demonstrates his commitment to translating perception innovations into high-stakes medical applications. Li’s research stands out for tackling underexplored yet practically significant challenges, positioning him as an emerging leader in perception-driven robotic manipulation.