Yazhe Luo
Papers
4
Total Citations
22
H-Index
3
About
Yazhe Luo is a robotics researcher whose work bridges computer vision, soft robotics, and intelligent manipulation. Luo’s research centers on three key areas: visual SLAM for autonomous navigation, assistive rehabilitation robotics, and dexterous robotic grasping. In visual SLAM, Luo developed a lightweight convolutional neural network for loop closure detection that significantly reduces cumulative positioning errors in indoor environments—a critical advance for long-duration autonomous navigation. For rehabilitation, Luo designed a multifunctional robotic glove with active-passive training modes, offering a versatile solution for hand rehabilitation and assistance that addresses the limitations of single-mode devices. More recently, Luo has focused on grasp estimation and hand-eye coordination, proposing a 6-DoF grasp estimation method that fuses RGB-D data using external attention mechanisms, and a coordinated grasping approach for textured targets in unstructured dynamic scenes. With over 20 cumulative citations across these works, Luo’s contributions are shaping more robust, adaptive, and human-centered robotic systems, from assistive devices to autonomous manipulation in complex environments.
Research Focus
Key Achievements
Top Papers
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