About

Hung Manh La is a prominent robotics researcher whose work spans structural health monitoring, robot manipulation, multi-robot systems, and autonomous navigation. Best known for his pioneering contributions to automated bridge inspection, La developed robotic systems and novel algorithms—including the STRUM crack detection method—that have transformed how civil infrastructure is assessed, replacing error-prone manual inspection with precise, autonomous solutions. His 2014 paper on automated crack detection has garnered over 460 citations, while his robotic crack inspection and mapping systems have collectively reshaped bridge maintenance practices. Beyond infrastructure, La has made significant strides in robot manipulation, proposing dynamic neural network approaches to optimizing manipulability in redundant robotic arms (374 citations), and contributing a widely referenced review of deep reinforcement learning for robot manipulation (252 citations). His research extends into multi-robot coordination, cooperative learning for predator avoidance, and SLAM techniques for autonomous driving. With a body of work exceeding 2,100 cumulative citations across his top publications, La's interdisciplinary expertise bridges robotics, machine learning, and civil engineering, making him an influential figure for researchers tackling real-world automation and infrastructure safety challenges.

Research Focus

Key Achievements

30
H-Index
85
Papers
3,944
Total Citations
46
Avg Citations/Paper
🏆 Most Cited Paper
Automated Crack Detection on Concrete Bridges
461 citations · 2014
📈 Most Prolific Year: 2017 (12 Papers)
🤝 Key Collaborators: 122
🏛 Institutions: University of Nevada, Reno, Rutgers, The State University of New Jersey, Oklahoma State University, University of Technology Sydney, Duy Tan University

Top Papers

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Key Collaborators

Contact & Links

Available for collaboration
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