Papers
10
Total Citations
78
H-Index
5
About
Yufei Wei’s research lies at the intersection of swarm robotics, collective cognition, and autonomous navigation, with a focus on enabling decentralized systems to solve complex, real-world tasks. His early work pioneered the use of artificial evolution and deep reinforcement learning—specifically deep Q-learning—to develop end-to-end control policies for robotic swarms, allowing robots with limited local sensing to autonomously allocate tasks and specialize in congested environments. These contributions, including studies on behavioral decomposition and collective cognition for foraging and object identification, have collectively garnered over 70 citations, establishing a foundation for scalable swarm intelligence. More recently, Wei has advanced autonomous navigation with innovative frameworks like BEV-ODOM, which reduces scale drift in monocular visual odometry using bird’s-eye-view representations, and VIVO, a visual-inertial-velocity odometry system with online calibration for challenging conditions. His work on neural uncalibrated visual servoing and demonstration-driven trajectory planning further demonstrates a commitment to robust, adaptive robotics. With a growing portfolio spanning both foundational swarm algorithms and cutting-edge perception systems, Wei’s research is shaping the future of autonomous, collaborative robotic systems.
Research Focus
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Top Papers
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