CHABANET Sylvain

Courriel

sylvain.chabanet@univ-lorraine.fr

Maitre de conférence

Enseignant-chercheur

Control and Robotics

Research interests

data-driven and knowledge based models for robotic textile manipulation
cable driven robotics
AI in manufacturing

Professional Experience

2025-present : Maitre de conférences, Université de Lorraine, LCFC
2023-2025 : Postdoctoral researcher, Ghent University, MMS team
2020-2023 : Doctorant, Université de Lorraine, CNRS, CRAN

Education

2020-2023 : phd, University of Lorraine,
2018-2019 : Master M2MO, University Paris Diderot, specialization in data science
2016-2019 : Ingénieur civil des Mines de Nancy, Ecole des Mines de Nancy, specialization in mathematical engineering.

Chevrollier-Mehat A, Chabanet S, Kumar A, Raharijaona T. Toward Textile Modeling for Robotic Positioning. 2026..

https://sagip2026.sciencesconf.org/

Chabanet S, Tadjouddine T, Antoine J, Kumar A, Raharijaona T. Toward an incremental Anderson-Darling algorithm for drift detection on cable-driven robots. 2025..

https://remecatronics25.sciencesconf.org/

Chabanet S, Bril El-Haouzi H, Thomas P. Active learning confidence measures for coupling strategies in digital twins integrating simulation and data-driven submodels. Simulation Modelling Practice and Theory. 2025;140:103092.

Chabanet S, Thomas P, Bril El Haouzi H. CoreSelect: a new approach to select landmarks for dissimilarity space embedding. 2023..

Chabanet S. Contributions aux ombres et jumeaux numériques dans l’industrie : proposition d’une stratégie de couplage entre modèles de simulation et d’apprentissage automatique appliquée aux scieries. 2023..

Chabanet S, Zimmermann E, Thomas P, Bril El Haouzi H. An object-oriented architecture to couple simulators and their machine learning surrogates models in the context of digital shadows. 2023..

Chabanet S, Thomas P, Bril El Haouzi H. Active Learning-based Online Coupling of Sawmill Simulators and Their Surrogate Model: The Effect of Sampling Bias on Concept Drift Detection. 2023..

Chabanet S, Thomas P, Bril El-Haouzi H. MLP based on dissimilarity features: An application to wood sawing simulator metamodeling. SN Computer Science. 2023;4(4):408.

Chabanet S, El-Haouzi B, Morin M, Gaudreault J, Thomas P. Toward digital twins for sawmill production planning and control: Benefits, opportunities, and challenges. International Journal of Production Research. 2023;61(7):2190-2213.

Chabanet S, Dumas M, Bril El-Haouzi H, Thomas P. A comparison of wood log dissimilarities to predict sawmill output with k-Nearest Neighbor algorithms. 2022..

Chabanet S, Bril El Haouzi H, Thomas P. Toward a sawmill digital shadow based on coupled simulation and supervised learning models. 2022..

Chabanet S, Bril El Haouzi H, Thomas P. Toward a self-adaptive digital twin based active learning method: an application to the lumber industry. 2022..

Chabanet S, Bril El-Haouzi H, Thomas P. Coupling digital simulation and machine learning metamodel through an active learning approach in Industry4.0 context. Computers in Industry. 2021;133:103529.

Chabanet S, Thomas P, Bril El-Haouzi H. Medoid-based MLP: an application to wood sawing simulator metamodeling. 2021..

Chabanet S, Chazelle V, Thomas P, El-Haouzi H. Dissimilarity to class medoids as features for 3D point cloud classification. 2021;AICT-632:573-581.

Chabanet S, Thomas P, Bril El-Haouzi H, Morin M, Gaudreault J. A kNN approach based on ICP metrics for 3D scans matching: an application to the sawing process. 2021..

Autonomous robotic preparation of textiles for on-demand garment production