Neural Identification for Control

Repository for our work Neural Identification for Control

If you find our work useful, please don't forget to cite.

@ARTICLE{9384260,
  author={P. {Saha} and M. {Egerstedt} and S. {Mukhopadhyay}},
  journal={IEEE Robotics and Automation Letters}, 
  title={Neural Identification for Control}, 
  year={2021},
  volume={},
  number={},
  pages={1-1},
  doi={10.1109/LRA.2021.3068099}}

Installation

Compatible with Python 3.5 and Pytorch 1.1.0

1. Create a virtual environment by python3 -m venv env
2. Source the virtual environment by source env/bin/activate
3. Install requirements by pip install -r ./requirements.txt

Usage

Go to nLinkPendulum directory: cd nLinkPendulum

Dataset generation

To generate dataset, run:
python dataGen.py --set <train/val> --savepath <path to save dataset>

Training

To train NN_g, run:
python train_NNg.py --dataset <path to dataset> --savepath <path to save models>

To train NN_P and NN_pi, run:
python train_NNpiP.py --NNg <path to trained NN_g> --dataset <path to dataset> --savepath <path to save models>

Evaluation

Pretrained models for 2-link pendulum are provided in saved_models directory

For a demo, run:
python test.py --modelpath <path to a trained model NI4C> --savepath <path to save result>

To verify a learned control law, run:
python controller_verification.py --modelpath <path to a trained model NI4C> --savepath <path to save result>