Tanguy Navez

Université de Lille, Centre National de la Recherche Scientifique, École Centrale de Lille

Papers

Total Citations

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About

Tanguy Navez is a rising researcher in soft robotics, whose work is pioneering new methods for the design, modeling, and control of these flexible machines. His core research areas include design optimization, computational modeling, and the integration of machine learning with finite element methods (FEM) for soft robotic systems. Navez’s major contribution is the development of a learning-based approach that condenses complex FEM models, making them fast enough for real-time control and accessible to non-specialists—a significant leap forward in practical soft robotics. His most cited work, “An Open Source Design Optimization Toolbox Evaluated on a Soft Finger” (2023, 9 citations), introduces a transformative open-source toolbox that promises to reshape how soft robot designs are shared and adopted. Further impact is seen in his 2025 article (6 citations), which details embedded control using learned condensed FEM models, and his 2024 study on “Design Optimization of a Soft Gripper Using Self-Contacts” (4 citations), which creatively uses self-contact to balance softness and stiffness. With a growing citation record and a focus on open-source tools, Navez is establishing himself as a key figure in making soft robotics more efficient, accessible, and computationally tractable.