Lv Yingxiang
Papers
1
Total Citations
6
H-Index
1
About
Dr. Lv Yingxiang is a pioneering researcher at the intersection of robotics, intelligent control, and manufacturing automation, with a specialized focus on adaptive force control for complex industrial tasks. Their most notable contribution is the development of a deep reinforcement learning (DRL)-based variable impedance control framework for robotic grinding of workpieces with intricate geometries. This work, published in 2025 and already garnering 6 citations, introduces a novel approach that enables robots to autonomously learn and adjust impedance parameters in real-time, ensuring stable force tracking even on non-uniform surfaces. By integrating DRL with stability analysis, Dr. Lv addresses a critical challenge in precision manufacturing—achieving consistent material removal without damaging delicate workpieces. This innovation has significant implications for aerospace, automotive, and mold-making industries, where complex geometries are common. Dr. Lv’s research bridges the gap between theoretical reinforcement learning algorithms and practical robotic control, offering a scalable solution for adaptive automation. Their work is particularly notable for its emphasis on safety and stability, ensuring that learned policies are not only effective but also provably stable during deployment. As a rising voice in intelligent manufacturing, Dr. Lv continues to push the boundaries of how robots can learn to handle unstructured environments with precision and reliability.
Research Focus
Key Achievements
Top Papers
- 1