Papers
5
Total Citations
25
H-Index
3
About
Jieyi Zhang is a rising researcher at the intersection of robotics, manipulation, and computer vision, with a focus on enabling robots to interact with complex and challenging objects. Their work addresses critical gaps in robotic perception and control, particularly for transparent objects and dexterous manipulation. Zhang’s most-cited papers, each garnering 7 citations, showcase their innovative contributions: “DiPGrasp” introduces a fast, differentiable grasp planner that works with robot grippers of varying degrees of freedom, a significant advance for efficient robotic grasping. “RFTrans” tackles the notoriously difficult problem of transparent object manipulation by leveraging refractive flow for accurate surface normal estimation, overcoming depth camera limitations. Additionally, “FSGlove” presents a novel inertial-based hand tracking system with shape-aware calibration for high-DoF motion capture, and “DexTOG” pioneers a language-guided diffusion framework for task-oriented dexterous grasping. Zhang’s work on “On the Heat-Transfer Effect of Spraying Speed During the Plasma Spraying on Turbine Blade” also demonstrates versatility in engineering applications. With a growing citation impact and a focus on practical, real-world robotic challenges, Jieyi Zhang is a promising voice in advancing autonomous manipulation and human-robot interaction.
Research Focus
Key Achievements
Top Papers
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- 5DexTOG: Learning Task-Oriented Dexterous Grasp With Language Condition2 citations · 2024