Bai Xiangpeng
Papers
1
Total Citations
4
H-Index
1
About
Bai Xiangpeng is a researcher whose work lies at the intersection of robotics, artificial intelligence, and multi-agent systems, with a particular focus on motion coordination and deep reinforcement learning. His most cited paper, "Motion Coordination of Multiple Robots Based on Deep Reinforcement Learning" (2019, 4 citations), addresses the complex challenge of enabling multiple robots to navigate shared environments without collisions. By framing multi-robot coordination as a Markov Decision Process, Bai introduced a deep reinforcement learning framework that allows each robot to learn optimal, collision-free paths toward its destination through sequential decision-making. This contribution is significant for advancing autonomous systems in applications such as warehouse logistics, search-and-rescue, and swarm robotics. While his citation count is modest, the work demonstrates a forward-looking approach to a foundational problem in robotics. Bai’s research is particularly valuable for students and researchers interested in the intersection of control theory and machine learning, offering a practical pathway for developing intelligent, decentralized coordination in multi-robot systems.
Research Focus
Key Achievements
Top Papers
- 1