Zixi CHEN, Federico RENDA, Alexia LE GALL, Lorenzo MOCELLIN, Matteo BERNABEI, Théo DANGEL,
Gastone CIUTI, Matteo CIANCHETTI, and Cesare STEFANINI
the Biorobotics Institute and the Department of Excellence in Robotics and AI, Scuola Superiore Sant’Anna
the Khalifa University Center for Autonomous Robotic Systems, Khalifa University
This website summarizes the papers leveraging data-driven methods for soft robot modeling and control. This is part of the review paper Data-driven Methods Applied to Soft Robot Modeling and Control: A Review. The Jacobian method and physical models are foundations for data-driven methods. The data-driven methods are classified into statistical methods, neural networks, and reinforcement learning. In this review, we make comparisons within and among these methods. A discussion about the advantages and disadvantages of the existing modeling and control approaches is presented, and we forecast the future of such approaches in soft robots.
In this website, we will classify each paper into one category according to the main approach in the paper. We will include our comments and add new papers frequently.
This work was supported in part by the European Union by the Next Generation EU project ECS00000017 ‘Ecosistema dell’Innovazione’ Tuscany Health Ecosystem (THE, PNRR, Spoke 4: Spoke 9: Robotics and Automation for Health.) and in part by the Khalifa University of Science and Technology under Grant CIRA-2020-074 and Grant RC1-2018-KUCARS.
Citation:
If you want to cite our paper or this website:
@article{24ZC,
title={Data-driven Methods Applied to Soft Robot Modeling and Control: A Review},
author={Chen, Zixi and Renda, Federico and Le Gall, Alexia and Mocellin, Lorenzo and Bernabei, Matteo and Dangel, Th{\'e}o and Ciuti, Gastone and Cianchetti, Matteo and Stefanini, Cesare},
journal={arXiv preprint arXiv:2305.12137},
year={2023}
}