Jingcheng Su

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About

Jingcheng Su is a robotics researcher whose work focuses on advancing robotic manipulation in complex, cluttered environments. His key research areas include robotic grasping, perception-driven manipulation, and uncertainty modeling for autonomous systems. Su’s major contributions center on developing novel approaches to enable robots to reliably grasp a wide variety of objects in densely cluttered scenes—a notoriously difficult problem in robotics. His most cited work, “Uncertainty-based Exploring Strategy in Densely Cluttered Scenes for Vacuum Cup Grasping” (2022, 8 citations), introduces a method that models perception uncertainty and applies geometric heuristics to improve grasping success for irregularly shaped objects. In a related paper, “Volumetric-based Contact Point Detection for 7-DoF Grasping” (2022, 2 citations), Su proposes a closed-loop grasp pipeline that leverages truncated signed distance function (TSDF) volumes for contact point detection, enabling more dexterous 7-degree-of-freedom grasping. These works demonstrate Su’s commitment to bridging perception and action in robotics, offering practical solutions for real-world applications like warehouse automation and domestic service robots. His research is particularly valuable for students and researchers interested in the intersection of computer vision, uncertainty quantification, and robotic manipulation.