Papers
2
Total Citations
10
H-Index
1
About
Sheng Zeng is a researcher in intelligent robotics and control systems, with a focus on integrating fuzzy logic, neural networks, and reinforcement learning for autonomous manipulation. Their early foundational work introduced a fuzzy neural network closed-loop control system for intelligent BLED arm manipulators navigating dynamic obstacles, addressing core challenges in unstructured environments such as autonomous planning and real-time adaptation. This paper, published in 2005, has garnered 9 citations and laid groundwork for nonlinear electromechanical system control. More recently, Zeng has advanced robotic grasping through offline reinforcement learning, proposing an improved QT-Opt algorithm that overcomes distribution shift and local optima—common pitfalls in traditional online methods. This 2025 work, already cited once, demonstrates ongoing innovation in adaptive grasping strategies. Zeng’s contributions bridge classical intelligent control with modern deep reinforcement learning, offering practical solutions for industrial and service robotics. Their research continues to influence the development of robust, autonomous robotic systems capable of operating in complex, real-world environments.
Research Focus
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Top Papers
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