Xiaofeng Yue
Papers
2
Total Citations
70
H-Index
2
About
Xiaofeng Yue is a leading researcher in robotics and intelligent systems, specializing in 3D perception and autonomous navigation. His work bridges computer vision and reinforcement learning to solve critical challenges in robotic manipulation and path planning. Yue’s most influential contribution is a coarse-fine point cloud registration method that combines local point-pair features with the iterative closest point algorithm, achieving robust alignment in cluttered environments—a paper that has garnered 47 citations and become a reference for 3D mapping applications. More recently, he advanced robot path planning with a modified dueling DQN algorithm that integrates priority experience replay and artificial potential fields, demonstrating significant improvements in convergence speed and collision avoidance (23 citations). This work, published in 2025, showcases his ability to merge deep reinforcement learning with classical control theory. Yue’s research has direct implications for autonomous vehicles, warehouse robotics, and augmented reality. His achievements reflect a commitment to developing practical, computationally efficient solutions that push the boundaries of how machines perceive and interact with the physical world.
Research Focus
Key Achievements
Top Papers
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