Sun Baiwei
Papers
1
Total Citations
3
H-Index
1
About
Sun Baiwei is a rising researcher in intelligent robotics, with a primary focus on autonomous navigation and human-robot interaction in complex, dynamic environments. Her key contributions lie in advancing deep reinforcement learning for real-world robotic applications, particularly in warehouse logistics. In her most cited work, "Deep Reinforcement Learning-based Obstacle Avoidance for Robot Movement in Warehouse Environments" (2024, 3 citations), she addresses a critical challenge: enabling mobile robots to safely and adaptively navigate cluttered spaces where goods and pedestrians interact unpredictably. By integrating feedback mechanisms for both inventory and human presence, Baiwei’s approach improves upon traditional trajectory control, offering more responsive and robust obstacle avoidance. Though early in her career, her work signals a significant step toward smarter, safer automation in industrial settings. Her research holds promise for reducing operational risks and enhancing efficiency in logistics, and she is poised to contribute further to the growing field of learning-based robotic control.
Research Focus
Key Achievements
Top Papers
- 1