Chaojie Yan

Beijing University of Technology, Zhejiang University of Technology

Papers

Total Citations

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About

Chaojie Yan is a robotics researcher whose work centers on robotic performance evaluation, human-robot interaction, and intelligent assembly. His most impactful contribution is a comprehensive evaluation framework for robotic global performance using modified principal component analysis (50 citations), which addresses the limitations of traditional linear and nonlinear dimension reduction methods in assessing complex robotic systems. Yan has also advanced the field of contact-rich manipulation through a learning-based approach to peg-in-hole assembly (15 citations), enabling robots to recognize hole position and inclination without visual sensors—a significant step toward human-like tactile intuition in automation. His research on motion similarity evaluation for humanoid robot arms (4 citations) further demonstrates his commitment to developing quantitative metrics for humanoid performance across tasks of varying complexity. By bridging statistical analysis, machine learning, and robotics, Yan’s work provides foundational tools for designing more adaptive and capable robotic systems, with particular relevance to manufacturing and assistive technologies.