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Descripción
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This dataset supports the article titled *“Combined model‑based and data‑driven approach for the control of a soft robotic neck”*, published in *Robotics and Autonomous Systems* in 2025. The study investigates a hybrid control strategy that combines an analytical model of a soft robotic neck with a multilayer perceptron (MLP) neural network trained on experimental data. The goal is to minimize position errors while maintaining a hybrid solution that includes models trained using deep learning techniques, without discarding the purely analytical model of inverse kinematics for the soft robotic neck.
The repository contains processed data used for training the models. The models used have been exported along with the necessary scripts to reproduce the experiments and results.
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Publicación relacionada
| IsSupplementTo: Continelli, N. A., Nagua, L. F., Olmos, P. M., & Monje, C. A. (2025). Combined model-based and data-driven approach for the control of a soft robotic neck. Robotics and Autonomous Systems, 194(105155). https://doi.org/10.1016/j.robot.2025.105155handle: 10016/48114 |
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Notas
| The data is: - Normalized - Divided into pre-training, training, and results (or test) sets - Notebooks where with a single click, all the results from the research paper can be replicated - It is recommended to use Python 3.8 and install the library from the /PySoftCan folder, following the instructions in the README_PySoftCan |
