Yuchen Cong

Papers

Total Citations

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About

Yuchen Cong is a robotics and computer vision researcher whose work centers on real-time color recognition and machine learning applications in autonomous systems. In their most-cited paper, “Color Recognition for Rubik’s Cube Robot” (2019), Cong introduced three innovative methods for solving color recognition challenges in robotic manipulation. Notably, they proposed Scatter Balance & Extreme Learning Machine (SB-ELM), an offline training-based approach that demonstrated the efficiency of machine learning in color classification tasks, alongside two online methods for dynamic environments. This work, with 3 citations, laid foundational insights for integrating extreme learning machines into robotic perception systems. Cong’s contributions highlight a practical focus on bridging algorithmic efficiency with real-world robotic applications, offering valuable methodologies for students and researchers in robotics, computer vision, and embedded AI. Their research underscores the potential of lightweight machine learning models in resource-constrained robotic tasks, inspiring further exploration into efficient color recognition and autonomous decision-making.