Papers
4
Total Citations
138
H-Index
4
About
Yingying Xiao is a leading researcher at the intersection of intelligent manufacturing, robotics, and artificial intelligence, with a primary focus on revolutionizing production line scheduling and robotic manipulation through deep reinforcement learning (DRL). Her seminal 2020 work, "Intelligent scheduling of discrete automated production line via deep reinforcement learning," has garnered 106 citations and addresses a critical gap in manufacturing by incorporating real-world complexities such as production line layouts and robotic transfer units—moving beyond traditional methods that assume fixed processing times. Xiao further advanced noncyclic scheduling for multi-cluster tools in semiconductor manufacturing, using Pareto optimization to tackle the challenge of multi-robot coordination with residency constraints. In the realm of robotics, she has pioneered DRL-based frameworks for industrial robot training in cloud manufacturing and developed a policy guidance mechanism for robotic grasping, overcoming issues like large search spaces and slow network convergence. Her work not only enhances adaptability and flexibility in automated production but also bridges the gap between theoretical AI and practical industrial applications, making her a pivotal figure in the evolution of smart manufacturing and autonomous robotics.
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