MIT-REALM/certrol

Introduction

Certrol is a tool-box for machine learning-based certifiable controller synthesis. Certrol currently supports the following algorithms:

1. Learning the Control Contraction Metric
2. Safe Control for Multi-agent Systems with Decentralized Control Barrier Functions
3. Safe Control for Black-box Dynamical Systems with Control Barrier Functions
4. Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functions
5. Compositional Neural Certificates for Networked Dynamical Systems

Installation

Certrol relies on a number of submodules. To clone the Certrol repository and each submodule, run

git clone git@github.com:MIT-REALM/certrol.git
git submodule init
git submodule update

Then follow the installation instructions for each submodule you are interested in using.

Licenses

Licenses for each submodule are maintained within each submodule. In summary:

1. C3M has a TODO license
2. macbf has a TODO license
3. sablas has a TODO license
4. neural_clbf has a 3-clause BSD license.
5. neuriss has a MIT license

Citation

If you find this project useful in your research, please consider citing:

@article{sun2020learning,
  title = {Learning certified control using contraction metric},
  author = {Sun, Dawei and Jha, Susmit and Fan, Chuchu},
  booktitle = {Proceedings of the Conference on Robot Learning},
  year = {2020}
}

@article{qin2021learning,
  title={Learning Safe Multi-agent Control with Decentralized Neural Barrier Certificates },
  author={Qin, Zengyi and Zhang, Kaiqing and Chen, Yuxiao and Chen, Jingkai and Fan, Chuchu},
  booktitle={International Conference on Learning Representations},
  year={2021}
}

@ARTICLE{qin2021sablas,
  author={Qin, Zengyi and Sun, Dawei and Fan, Chuchu},
  journal={IEEE Robotics and Automation Letters},
  title={Sablas: Learning Safe Control for Black-Box Dynamical Systems},
  year={2022},
  volume={7},
  number={2},
  pages={1928-1935},
  doi={10.1109/LRA.2022.3142743}
}

@article{dawson2021safe,
  title={Safe Nonlinear Control Using Robust Neural Lyapunov-Barrier Functions},
  author={Charles Dawson, Zengyi Qin, Sicun Gao, Chuchu Fan},
  journal={5th Annual Conference on Robot Learning},
  year={2021}
}

@article{dawson2022perception,
  author={Dawson, Charles and Lowenkamp, Bethany and Goff, Dylan and Fan, Chuchu},
  journal={IEEE Robotics and Automation Letters},
  title={Learning Safe,
  Generalizable Perception-Based Hybrid Control With Certificates},
  year={2022},
  volume={7},
  number={2},
  pages={1904-1911},
  doi={10.1109/LRA.2022.3141657}
}

@inproceedings{zhang2023neuriss,
  title={Compositional Neural Certificates for Networked Dynamical Systems},
  author={Songyuan Zhang and Yumeng Xiu and Guannan Qu and Chuchu Fan},
  booktitle={5th Annual Learning for Dynamics {\&} Control Conference},
  year={2023},
}