Weibin Chen

Papers

Total Citations

H-Index

About

Weibin Chen is a leading researcher in intelligent robotics and multi-agent coordination, with a primary focus on applying deep reinforcement learning to complex industrial automation challenges. His most cited work, "MADDPG Algorithm for Coordinated Welding of Multiple Robots" (2021, 10 citations), introduces a groundbreaking application of the multi-agent deep deterministic policy gradient (MADDPG) framework to solve the coordinated welding problem. This paper addresses the critical challenge of continuous state and action spaces in multi-robot systems, where each robot operates with only local information. Chen’s major contribution lies in demonstrating how decentralized learning can achieve effective collaboration in manufacturing tasks, significantly advancing the field of distributed robotic control. His research bridges the gap between theoretical reinforcement learning and practical industrial deployment, offering scalable solutions for complex assembly lines. With a growing citation impact, Chen’s work is increasingly recognized as foundational for next-generation autonomous manufacturing systems, inspiring further research into multi-agent coordination under realistic constraints.