Yun‐Sheng Chen
Papers
1
Total Citations
5
H-Index
1
About
Yun-Sheng Chen is a researcher whose work primarily focuses on industrial robotics and intelligent measurement systems. Although his most-cited paper, "A full freedom pose measurement method for industrial robot based on reinforcement learning algorithm" (2021), has garnered 5 citations, it was later retracted, reflecting the complex nature of academic publishing. Despite this, Chen's research contributions lie in advancing pose measurement techniques for industrial robots, aiming to enhance precision and adaptability in automated manufacturing environments. His work explores the integration of reinforcement learning algorithms to solve real-time positioning challenges, a critical area for improving robotic autonomy and efficiency. While his citation impact remains modest, Chen's efforts highlight the ongoing challenges in developing robust, data-driven solutions for industrial applications. His research underscores the importance of rigorous validation in machine learning-based robotics, offering valuable lessons for students and researchers navigating the intersection of control systems and artificial intelligence.
Research Focus
Key Achievements
Top Papers
- 1