Yanling Wei
Papers
2
Total Citations
50
H-Index
2
About
Yanling Wei is a leading researcher in intelligent robotics and control systems, with a focus on neural network-based adaptive control and autonomous navigation. Her most-cited work, "Neural network based tracking control for an elastic joint robot with input constraint via actor-critic design" (2020, 46 citations), introduces a groundbreaking reinforcement learning framework that enables precise trajectory tracking for flexible robots under physical constraints—a critical advancement for safe human-robot collaboration. More recently, her 2022 paper "Robot navigation with predictive capabilities using graph learning and Monte Carlo tree search" (4 citations) pioneers a novel integration of graph neural networks with Monte Carlo tree search, allowing robots to predict future states and plan optimal paths in complex dynamic environments. This work directly addresses the challenge of real-time decision-making under uncertainty, a cornerstone for autonomous systems in logistics and exploration. Wei’s contributions bridge theoretical control theory with practical robotic applications, earning recognition for advancing adaptive learning in constrained systems. Her research continues to shape next-generation intelligent robots capable of reasoning and acting in unpredictable real-world settings.
Research Focus
Key Achievements
Top Papers
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