Note on development
This project is still under heavy development and there is a lack of documentation. I, @maxspahn, am committed to improve and maintain that package. However, I rely on people like you to point me to issues and unclear sections of the code. So feel free to leave issues whenever something bugs you.
Fabrics ros-wrapper
The fabrics-ros wrapper will be released very shortly when compatibility is verified.
Geometric Fabrics represent a geometric approach to motion generation for various robot structures. The idea is a next development step after Riemannian Motion Policies and offers increased stability and accessibility.
| Holonomic robots | Non-Holonomic robots |
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Install the package through pip, using
pip3 install ".<options>"or from PyPI using
pip3 install fabricsOptions are [agents] and [tutorials]. Those can be installed using
pip3 install ".[agents]"
pip3 install ".[tutorials]"
Install the package through poetry, using
poetry install --with <option>This repository was used in several publications. The major one being Dynamic Optimization Fabrics for Motion Generation If you are using this software, please cite:
@misc{https://doi.org/10.48550/arxiv.2205.08454,
doi = {10.48550/ARXIV.2205.08454},
url = {https://arxiv.org/abs/2205.08454},
author = {Spahn, Max and Wisse, Martijn and Alonso-Mora, Javier},
keywords = {Robotics (cs.RO), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Dynamic Optimization Fabrics for Motion Generation},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Share Alike 4.0 International}
}Other publications where this repository was used:
https://github.com/maxspahn/optuna_fabrics
@article{https://doi.org/10.48550/arxiv.2302.06922,
doi = {10.48550/ARXIV.2302.06922},
url = {https://arxiv.org/abs/2302.06922},
author = {Spahn, Max and Alonso-Mora, Javier},
keywords = {Robotics (cs.RO), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Autotuning Symbolic Optimization Fabrics for Trajectory Generation},
publisher = {arXiv},
year = {2023},
copyright = {Creative Commons Attribution Share Alike 4.0 International}
}https://github.com/tud-amr/localPlannerBench
@misc{https://doi.org/10.48550/arxiv.2210.06033,
doi = {10.48550/ARXIV.2210.06033},
url = {https://arxiv.org/abs/2210.06033},
author = {Spahn, Max and Salmi, Chadi and Alonso-Mora, Javier},
keywords = {Robotics (cs.RO), FOS: Computer and information sciences, FOS: Computer and information sciences},
title = {Local Planner Bench: Benchmarking for Local Motion Planning},
publisher = {arXiv},
year = {2022},
copyright = {Creative Commons Attribution Share Alike 4.0 International}
}This repository contains brief examples corresponding to the theory presented in "Optimization Fabrics" by Ratliff et al. https://arxiv.org/abs/2008.02399. These examples are named according to the naming in that publication. Each script is self-contained and required software is installed using
pip install ".[tutorials]"The work is based on some works by the NVIDIA Research Labs. Below you find a list of all relevant links: