Feng Xiong
Papers
1
Total Citations
4
H-Index
1
About
Feng Xiong is a researcher at the forefront of intelligent robotics, specializing in the integration of reinforcement learning, computer vision, and robotic manipulation. His work focuses on developing adaptive control systems that enable robots to perform complex tasks—such as grasping—in dynamic, real-world environments. Xiong’s most cited paper, "Grasping Control of a Vision Robot Based on a Deep Attentive Deterministic Policy Gradient" (2021), introduces a novel deep reinforcement learning framework that combines attention mechanisms with deterministic policy gradients. This approach allows robots to better handle variable target objects and unpredictable working conditions, addressing a critical limitation in traditional robotic grasping. With 4 citations, this work has already contributed to advancing the robustness of vision-based robotic control. Xiong’s research is particularly notable for its practical applications in manufacturing and service robotics, where reliable grasping in cluttered settings is essential. By bridging the gap between simulated training and real-world deployment, Feng Xiong is helping to make autonomous robots more capable and versatile.
Research Focus
Key Achievements
Top Papers
- 1