Chengguang Wang

Ningbo Institute of Industrial Technology

Papers

Total Citations

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About

Chengguang Wang is a robotics researcher whose work focuses on advancing robotic manipulation, particularly in the challenging domain of grasp detection under occluded environments. His key contributions center on developing robust perception frameworks that enable robotic grippers to identify and grasp target objects even when partially hidden. His most cited work, "Grasp Detection under Occlusions Using SIFT Features" (2021), introduces a novel two-step method leveraging scale-invariant feature transforms to distinguish objects amidst visual clutter, a critical step toward generalizing grasping in real-world settings. While his citation count is still growing, this paper has laid foundational groundwork for addressing occlusion—a persistent bottleneck in robotic grasping. Wang’s research bridges computer vision and robotics, offering practical solutions for autonomous systems in warehouses, manufacturing, and assistive robotics. His work is particularly notable for its focus on parallel gripper applications, making it directly applicable to widely used robotic hardware. As the field pushes toward more adaptive and reliable manipulation, Wang’s contributions represent a meaningful step in enabling robots to operate effectively in unstructured, cluttered environments.