Jiaming Hu

Contextual Change (United States), Wuhan University of Science and Technology

Papers

Total Citations

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About

Jiaming Hu is a robotics researcher whose work centers on the intersection of robotic manipulation, computer vision, and 3D perception. His primary contributions address critical challenges in enabling robots to interact with objects in unstructured environments. Hu’s most notable work, “Multi-Modal Planning on Regrasping for Stable Manipulation” (2023, 3 citations), tackles the fundamental problem of grasp stability by integrating multi-modal sensory data into regrasping strategies. This research moves beyond simple pick-and-place by planning sequences of grasps to ensure stable manipulation, even when initial grasp poses are suboptimal. In his earlier work, “Fast target recognition and location based on graph model and point cloud model” (2021, 2 citations), Hu addressed the practical limitations of point cloud processing—specifically low accuracy and slow speed—by fusing graph-based models with 3D point cloud data. This approach enhances both the speed and precision of object recognition and localization, which is essential for real-time robotic applications. Through these contributions, Hu is advancing the field toward more autonomous and adaptable robotic systems, with a focus on improving the reliability and efficiency of manipulation in complex, real-world settings.