Dataset for "Learning Visually Interpretable Oscillator Networks for Soft Continuum Robots from Video"

View Dataset
Krauss, Henrik;Licher, Johann

Description

This dataset was used to learn visually interpretable oscillator networks in "Learning Visually Interpretable Oscillator Networks for Soft Continuum Robots from Video" (DOI: https://doi.org/10.48550/arXiv.2511.18322). Please cite this paper when using the dataset. The implementation of the visually interpretable oscillator networks and their training is published as a GitHub repository (https://github.com/UThenrik/visual_oscillators_for_SCR).The dataset includes:Pressure and video raw data of a soft pneumatic robot during dynamic planar movements based on step and oscillatory inputsData processing script for data loading, synchronization, subsampling and croppingProcessed data used for training of networks in the paper

Citations (0)

Mentions (0)

Metrics

FAIR Score

85%

Citations

0

Mentions

0

Metrics Over Time

Publication Details

DOI

Publisher

Zenodo

Assigned Domain

Subfield

Computer Vision and Pattern Recognition

Field

Computer Science

Domain

Physical Sciences

Confidence Score

57%

Source

Scholar Data Model

Keywords

Modeling, Control, and Learning for Soft RobotsVisual LearningModel Learning for ControlRepresentation LearningKoopman Theory