Papers
3
Total Citations
18
H-Index
3
About
Dr. Zaiyue Yang is a leading researcher in multi-robot systems and decentralized reinforcement learning, whose work bridges foundational theory with practical robotics challenges. His primary research areas include formation control, multi-agent coordination, and motion planning for nonlinear robotic systems. Dr. Yang’s most impactful contribution is a formation control framework that integrates leader-follower protocols with potential fields to achieve obstacle avoidance for second-order multi-robot systems—a critical advancement for real-world deployment in cluttered environments. His work on decentralized temporal-difference learning provides finite-sample analysis for policy evaluation in multi-agent settings, directly supporting applications in networked robotics and swarming drones. More recently, he has developed a trajectory optimization framework using alternating direction method of multipliers (ADMM) to handle the non-convex constraints inherent in nonlinear robotic motion planning. With over 18 citations across his most-cited papers, Dr. Yang’s research is gaining recognition for its practical relevance. His ability to combine rigorous theoretical guarantees with implementable algorithms makes his work essential reading for students and researchers advancing autonomous multi-robot systems.
Research Focus
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Top Papers
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