Xiwen Yang
Papers
2
Total Citations
9
H-Index
2
About
Dr. Xiwen Yang is a researcher at the intersection of artificial intelligence and robotics, with key contributions in human-like AI performance and precision robot calibration. His most cited work, "On Human-Like Performance Artificial Intelligence – A Demonstration Using an Atari Game" (2019, 6 citations), explores how AI systems can mimic human cognitive strategies in interactive environments, offering foundational insights into more intuitive machine learning models. More recently, Dr. Yang’s 2025 study, "Searching for an Accurate Robot Calibration via Improved Levenberg–Marquardt and Radial Basis Function System" (3 citations), addresses a critical challenge in industrial robotics: achieving high-precision calibration for tasks like welding and assembly. By integrating an enhanced Levenberg–Marquardt algorithm with radial basis function networks, his work directly improves manufacturing efficiency and quality, reducing manual labor intensity in factories. Though early in his career, Dr. Yang’s research bridges theoretical AI development with practical robotic applications, demonstrating a clear trajectory toward more capable, human-aligned automation systems. His work holds particular relevance for students and researchers in robotics, AI, and industrial engineering, offering both conceptual advances and tangible solutions for real-world manufacturing challenges.
Research Focus
Key Achievements
Top Papers
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- 2