Papers
3
Total Citations
20
H-Index
2
About
Ziyu Li is a robotics researcher whose work bridges neural computation, motion planning, and bio-inspired intelligence. Her research focuses on developing adaptive control and planning algorithms for robotic systems operating in uncertain environments. In her early influential work, Li proposed an effective neural remedy for the drift phenomenon in redundant robot manipulators, using a quadratic performance index to stabilize a three-link planar robot arm—a study that has garnered 15 citations and laid groundwork for neural-based robotic control. More recently, Li has pioneered brain-inspired approaches to motion planning. Her 2025 paper on BrainyMP introduces a graph neural network framework modeled after the brain’s spatial relational memory, achieving efficient motion planning for transportation robots. Complementing this, her 2024 work presents a harmonized learning system with concurrent arbitration, enabling robots to navigate fuzzy, uncertain environments by dynamically selecting optimal strategies. Though these newer papers are still accumulating citations (3 and 2 respectively), they represent a significant step toward more adaptive, human-like robotic intelligence. Li’s work is notable for its interdisciplinary fusion of neuroscience, fuzzy logic, and graph neural networks, positioning her at the forefront of next-generation autonomous systems.
Research Focus
Key Achievements
Top Papers
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