Papers
3
Total Citations
23
H-Index
3
About
Lele Xu is a rising researcher in autonomous systems, specializing in unmanned aerial vehicle (UAV) navigation, multi-modal perception, and deep reinforcement learning (DRL). His work addresses the critical challenge of enabling UAVs to perform complex, real-world tasks—such as simultaneous target tracking and obstacle avoidance—in cluttered, occluded environments. Xu’s most cited paper, “UAV target following in complex occluded environments with adaptive multi-modal fusion” (2022, 12 citations), introduces a novel fusion framework that robustly integrates visual and depth data to maintain target lock despite obstructions. Building on this, his 2023 work “Attention-Based Policy Distillation for UAV Simultaneous Target Tracking and Obstacle Avoidance” (8 citations) pioneers a DRL approach that distills attention mechanisms into a single policy, overcoming the traditional limitation of single-task UAV applications. Most recently, his 2024 paper “DGMem: learning visual navigation policy without any labels by dynamic graph memory” (3 citations) pushes boundaries by eliminating the need for labeled data, using dynamic graph memory to enable zero-shot navigation. Xu’s contributions are shaping the next generation of agile, autonomous drones for search-and-rescue, surveillance, and delivery, with his citation trajectory signaling growing influence in the field.
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