Weiming Wang
Papers
1
Total Citations
6
H-Index
1
About
Dr. Weiming Wang is a leading researcher in intelligent robotics and autonomous systems, with a primary focus on integrating deep reinforcement learning into mobile robot control. His most-cited work, "Tracking Control for Mobile Robot Based on Deep Reinforcement Learning" (2019, 6 citations), introduces a novel application of proximal policy optimization (PPO) to solve trajectory tracking challenges. By employing a distributed PPO framework, Dr. Wang significantly accelerates sample collection and reduces transition correlations, enabling more efficient and stable learning for real-time robot navigation. This contribution bridges the gap between advanced reinforcement learning algorithms and practical robotic control, offering a scalable solution for autonomous systems in dynamic environments. His research is pivotal for advancing the robustness and adaptability of mobile robots, with implications for logistics, exploration, and service robotics. Dr. Wang’s work continues to inspire innovations in learning-based control, establishing him as a key figure in the intersection of robotics and artificial intelligence.
Research Focus
Key Achievements
Top Papers
- 1Tracking Control for Mobile Robot Based on Deep Reinforcement Learning6 citations · 2019