Papers
3
Total Citations
12
H-Index
2
About
Xi Lan is a rising researcher at the forefront of multi-robot coordination and intelligent manufacturing, specializing in the application of deep reinforcement learning to complex robotic systems. Her work centers on developing hierarchical multi-agent reinforcement learning (HMADRL) frameworks that enable teams of robots to efficiently tackle intricate tasks like pick-and-place operations. A key contribution is her pioneering approach to asynchronous termination in hierarchical learning, which allows individual agents to complete sub-tasks at different times without stalling the overall system—a critical advancement for real-world manufacturing efficiency. Her most-cited paper, "Coordination of a Multi Robot System for Pick and Place Using Reinforcement Learning" (2022, 5 citations), demonstrates how deep RL can solve the coordination challenges of smart manufacturing. Building on this, her 2024 paper on multiagent hierarchical RL with asynchronous termination (5 citations) further refines these methods, offering scalable solutions for industrial automation. Though early in her career, Lan's work is already shaping the next generation of adaptive, autonomous robotic systems, promising to transform how factories and warehouses operate.
Research Focus
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