Xiaoxin Feng
Papers
1
Total Citations
4
H-Index
1
About
Xiaoxin Feng is a robotics researcher whose work centers on intelligent control systems for industrial automation, with a particular focus on robot-environment interaction and assembly tasks. His most cited paper, "Reinforcement Learning-Based Impedance Learning for Robot Admittance Control in Industrial Assembly" (2022, 4 citations), addresses a critical challenge in manufacturing: the limitations of fixed-parameter impedance control. Feng proposes a novel framework that integrates reinforcement learning to dynamically adjust impedance parameters, enabling robots to adapt their compliance in real time during assembly. This approach enhances both safety and interactivity, reducing the risk of damage to robots and their surroundings. By bridging machine learning and traditional control theory, Feng’s work offers a practical pathway toward more flexible, autonomous industrial robots. His research is particularly valuable for students and engineers seeking to understand how adaptive control can improve robotic performance in complex, contact-rich tasks. Though early in his career, Feng’s contributions signal a promising direction for the next generation of intelligent manufacturing systems.
Research Focus
Key Achievements
Top Papers
- 1