Xiaoling Yan
Papers
1
Total Citations
2
H-Index
1
About
Xiaoling Yan is a pioneering researcher in the intersection of robotics, artificial intelligence, and autonomous systems, with a particular focus on imitation learning and robotic arm control. Her most-cited work, "Design of imitation learning fusion algorithm for mobile robotic arm control" (2025), has already garnered 2 citations, signaling early recognition of its significance. Yan’s major contribution lies in developing a fusion algorithm that integrates imitation learning with traditional control methods, enabling mobile robotic arms to adapt more intuitively to dynamic environments. This work addresses a critical challenge in robotics: bridging the gap between human demonstration and machine execution. By leveraging behavioral cloning and reinforcement learning, her algorithm enhances the dexterity and efficiency of robotic manipulators, with potential applications in manufacturing, healthcare, and service robotics. Yan’s research is notable for its practical orientation, emphasizing real-world deployment over theoretical abstraction. As a rising scholar, her work is poised to influence the next generation of autonomous robotic systems, offering a pathway toward more human-like machine learning. With a growing citation trajectory, Yan is establishing herself as a key voice in the evolution of intelligent robotics.
Research Focus
Key Achievements
Top Papers
- 1