Artur Cordeiro

INESC TEC, Polytechnic Institute of Porto

Papers

Total Citations

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About

Artur Cordeiro is a robotics researcher specializing in computer vision, deep learning, and autonomous manipulation, with a particular focus on industrial bin-picking applications. His work addresses the critical challenge of bridging the gap between simulated and real-world data for training object segmentation models. Cordeiro’s most cited paper, “Object segmentation dataset generation framework for robotic bin-picking: Multi-metric analysis between results trained with real and synthetic data” (2025, 3 citations), introduces a novel framework for generating synthetic datasets that achieve performance comparable to real data, reducing the need for costly manual annotation. His earlier work, “Object Segmentation for Bin Picking Using Deep Learning” (2022, 3 citations), laid the foundation for applying convolutional neural networks to cluttered industrial environments. Cordeiro also contributed to “Friday: The Versatile Mobile Manipulator Robot” (2025), showcasing his broader interest in integrating perception and mobility for flexible automation. With a growing citation footprint, his research is directly impacting the efficiency and scalability of robotic systems in manufacturing, offering practical solutions that lower deployment barriers. Cordeiro’s work is essential reading for engineers and researchers advancing vision-based robotics in unstructured settings.