Baiming Chen
Papers
2
Total Citations
125
H-Index
2
About
Baiming Chen is a leading researcher in multi-agent systems, reinforcement learning, and autonomous navigation, with a focus on deploying scalable robotic fleets in complex, dynamic environments. His most significant contribution is the development of MAPPER (Multi-Agent Path Planning with Evolutionary Reinforcement Learning), a decentralized, partially observable framework that integrates evolutionary algorithms with reinforcement learning to enable robots to navigate safely and efficiently in mixed, unpredictable settings—such as warehouses or urban spaces—without centralized control. This work has garnered over 125 citations, underscoring its impact on both academia and industry. Chen’s approach addresses critical challenges in real-world multi-agent coordination, including collision avoidance, scalability, and adaptability to changing conditions. His research bridges the gap between theoretical reinforcement learning and practical robotics, offering solutions that are both robust and computationally feasible. By advancing decentralized path planning, Chen has laid the groundwork for more intelligent, autonomous systems in logistics, transportation, and service robotics, making him a key figure in the evolution of multi-agent navigation.
Research Focus
Key Achievements
Top Papers
- 1
- 2