Yipeng Pang
Papers
1
Total Citations
12
H-Index
1
About
Yipeng Pang is a researcher at the forefront of intelligent robotic control, specializing in reinforcement learning for autonomous systems. His work bridges the gap between classical control theory and modern machine learning, with a particular focus on trajectory-tracking and motion planning for manipulators and mobile robots. In his highly cited 2019 paper, Pang introduced a novel approach using Proximal Policy Optimization (PPO) with Generalized Advantage Estimation (GAE) to solve complex trajectory-tracking problems. By implementing a distributed PPO framework, he significantly improved sample collection speed and reduced transition correlations—a breakthrough that enhances both efficiency and stability in robotic learning. This work, which has garnered 12 citations, demonstrates his ability to tackle fundamental challenges in real-time robotic control. Pang’s contributions are shaping the next generation of adaptive, learning-based robotic systems, offering practical solutions for industrial automation and autonomous navigation. His research continues to inspire students and engineers working at the intersection of robotics and artificial intelligence.
Research Focus
Key Achievements
Top Papers
- 1