Physics-informed Neural Networks to Model and Control Robots: a
Theoretical and Experimental Investigation

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Physics-informed Neural Networks to Model and Control Robots: a Theoretical and Experimental Investigation

9 May 2023

Cosimo Della Santina

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Papers citing "Physics-informed Neural Networks to Model and Control Robots: a Theoretical and Experimental Investigation"

7 / 7 papers shown

Title
Soft yet Effective Robots via Holistic Co-Design Maximilian Stolzle Niccolò Pagliarani F. Stella Josie Hughes Cecilia Laschi Daniela Rus M. Cianchetti Cosimo Della Santina Gioele Zardini 103 1 0 20 Apr 2025
Learning Low-Dimensional Strain Models of Soft Robots by Looking at the Evolution of Their Shape with Application to Model-Based Control Ricardo Valadas Maximilian Stolzle Jingyue Liu Cosimo Della Santina 117 1 0 21 Feb 2025
Metamizer: a versatile neural optimizer for fast and accurate physics simulations Nils Wandel Stefan Schulz Reinhard Klein PINN AI4CE 118 1 0 10 Oct 2024
A Novel and Accurate BiLSTM Configuration Controller for Modular Soft Robots with Module Number Adaptability Zixiao Chen Matteo Bernabei Vanessa Mainardi Xuyang Ren G. Ciuti Cesare Stefanini 62 4 0 19 Jan 2024
Neural Autoencoder-Based Structure-Preserving Model Order Reduction and Control Design for High-Dimensional Physical Systems Marco Lepri Davide Bacciu Cosimo Della Santina AI4CE 79 8 0 11 Dec 2023
A Hybrid Adaptive Controller for Soft Robot Interchangeability Zixiao Chen Xuyang Ren Matteo Bernabei Vanessa Mainardi G. Ciuti Cesare Stefanini 42 7 0 20 Jul 2023
Data-driven Methods Applied to Soft Robot Modeling and Control: A Review Zixiao Chen F. Renda A. L. Gall Lorenzo Mocellin Matteo Bernabei Théo Dangel G. Ciuti M. Cianchetti Cesare Stefanini 83 28 0 20 May 2023