Papers
3
Total Citations
89
H-Index
2
About
Zhi Zhang is a robotics and computer vision researcher whose work bridges intelligent manipulation and agricultural automation. His research focuses on force control learning for industrial robots and 3D object detection for autonomous systems. Zhang’s most influential contribution is his 2018 paper on efficient force control learning using variable impedance control, which tackles the critical challenge of data inefficiency in learning force-sensitive tasks—a problem that has limited real-world deployment of such methods. This work has accumulated 45 citations, reflecting its significance in advancing robot learning for industrial applications. He further extended his impact in 3D perception with DVFENet, a dual-branch voxel feature extraction network for 3D object detection that has garnered 42 citations. Most recently, Zhang has applied his expertise to agricultural robotics, developing YOLOv7-LEES, a lightweight detection system for dense, small tea shoots in complex field conditions. This system achieves real-time performance at 116.3 FPS while reducing model parameters by 12.9% and computational cost by 8.3%, demonstrating his ability to create practical, deployable solutions for precision agriculture. Zhang’s work consistently addresses real-world deployment challenges, making him a notable figure in applied robotics.
Research Focus
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