Papers
1
Total Citations
6
H-Index
1
About
Zan Li is a robotics researcher whose work focuses on the intersection of intelligent control systems and underwater robotics, with particular emphasis on applying machine learning techniques to real-world robotic challenges. Li's most notable contribution centers on developing adaptive control frameworks for amphibious spherical robots, a technically demanding domain where conventional control strategies often fall short due to unpredictable underwater environments and variable operating conditions. In a 2021 paper that has garnered 6 citations, Li proposed an innovative two-layer network framework leveraging reinforcement learning to achieve robust vector control for such robots, addressing the longstanding challenge of designing controllers capable of adapting dynamically to adverse underwater conditions. This work demonstrates Li's commitment to bridging theoretical machine learning concepts with practical robotics engineering, pushing the boundaries of autonomous underwater vehicle design. By harnessing the adaptive capabilities of reinforcement learning, Li's research offers promising pathways toward more resilient and self-correcting robotic systems capable of operating reliably in complex, unstructured environments. This contribution positions Li as an emerging voice in intelligent robotics, with research that holds meaningful implications for applications in underwater exploration, environmental monitoring, and amphibious autonomous systems.
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Top Papers
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