Leixin Xu

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About

Leixin Xu is a researcher advancing the frontier of intelligent manufacturing through deep reinforcement learning (DRL) for multi-robot coordination. His primary research areas center on multi-agent systems and robotic welding automation, where he addresses the complex challenge of enabling multiple robots to collaborate on continuous, time-varying tasks in dynamic environments. Xu’s major contribution lies in adapting the Multi-Agent Deep Deterministic Policy Gradient (MADDPG) algorithm for industrial applications. His seminal 2021 work, "MADDPG Algorithm for Coordinated Welding of Multiple Robots" (10 citations), introduced a framework where robots with continuous state and action spaces can coordinate welding paths while accounting for obstacles, a critical step toward flexible, autonomous manufacturing. He further expanded this in "Deep Reinforcement Learning Algorithms for Multiple Arc-Welding Robots" (4 citations), demonstrating how DRL can handle real-time task adaptation. By bridging the gap between theoretical multi-agent RL and practical industrial robotics, Xu’s work provides a scalable solution for complex assembly lines, offering a promising alternative to traditional pre-programmed systems. His research is particularly valuable for engineers seeking to deploy adaptive, collision-free robotic teams in high-precision environments.