Papers
2
Total Citations
31
H-Index
2
About
Hao Fu is an emerging researcher whose work sits at the intersection of deep reinforcement learning, autonomous robotics, and intelligent path planning. His research focuses on developing advanced algorithms that enable Autonomous Mobile Robots (AMRs) to navigate safely and efficiently through complex, dynamic, and spatially constrained environments — a challenge that sits at the frontier of modern robotics and artificial intelligence. Fu's most notable contributions include novel deep reinforcement learning frameworks designed to overcome critical limitations in traditional path planning methods, such as slow model convergence, poor sample efficiency, and inadequate handling of temporal dependencies. His 2025 paper on deep reinforcement learning for AMR path planning in complicated environments has already garnered 18 citations, demonstrating rapid uptake by the research community. A second landmark study introduced an enhanced TD3-based approach — combining prioritized experience replay with Long Short-Term Memory (LSTM) networks — accumulating 13 citations since publication. Despite being early-stage work, Fu's research has made a measurable impact, collectively attracting over 30 citations within a single year. His contributions are particularly valuable for researchers and engineers working on warehouse automation, search-and-rescue robotics, and autonomous vehicle navigation, where real-world environmental complexity demands robust, adaptive planning solutions.
Research Focus
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Top Papers
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