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About

Yingfu Xu is an emerging researcher at the intersection of neuromorphic computing, autonomous robotics, and deep learning-based navigation. His work spans two interconnected frontiers: bio-inspired perception and control for aerial robots, and intelligent path planning for mobile systems. Xu's most celebrated contribution is his pioneering research on fully neuromorphic vision and control for autonomous drone flight, which leverages event-based cameras and spiking neural networks to achieve low-latency, energy-efficient aerial navigation — a landmark paper that has already garnered 76 citations since its 2024 publication, signaling strong community impact. His earlier work on Deep Q-Network (DQN)-based path planning for mobile robots, published in 2018 and accumulating 75 citations, demonstrated a practical reinforcement learning approach to optimal navigation in dense environments, establishing him early as a contributor to autonomous systems research. More recently, Xu developed CUAHN-VIO, a content-and-uncertainty-aware homography network for visual-inertial odometry, addressing robust ego-motion estimation in agile micro aerial vehicles. Collectively, his research advances the field toward robots that perceive and act more like biological systems — efficiently, adaptively, and in real-world conditions.