A safety guaranteed neural network controller for autonomous systems
There are three simple control demos (traffic merging, 2D and 3D robot control) and one vision-based end-to-end autonomous driving demo.
conda create -n bnet python=3.8
conda activate bnet
pip install torch==1.10.1+cu113 torchvision==0.11.2+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
pip install pytorch-lightning==1.5.8 opencv-python==4.5.2.54 matplotlib==3.5.1 ffio==0.1.0 descartes==1.1.0 pyrender==0.1.45 pandas==1.3.5 shapely==1.7.1 scikit-video==1.1.11 scipy==1.6.3 h5py==3.1.0
pip install qpth cvxpy cvxopt
Install vista (please install the VISTA version provided in this repo, not from others).
conda activate bnet
cd driving/vista
pip install -e .
There are two models in this example: barrier_net and state_net. The state_net is used to infer the states of the ego vehicle and obstacles from the image observations, and the barrier_net (using the predicted state from state_net model) predicts safe controls from image observations. The differentiable CBFs are implemented in barrier_net. You may adapt the corresponding safety constraints in your examples. For more details, please refer to the paper listed at the end.
Download training data: https://drive.google.com/file/d/1cV9ByK0qGtRDv32uhPMqgRbQUP1hYQPZ/view?usp=drive_link
conda activate bnet
cd driving
bash scripts/run_example.sh --data-src ./your_path_to_data/data
Download trained Barrier_Net: https://drive.google.com/file/d/1hyiUeyr1l9-239MT3YMxNDXvwgjj7ghF/view?usp=drive_link
Download trained state_net: https://drive.google.com/file/d/1YnWFuD7Xse_dgERC0gq3lEDDruYwiD_R/view?usp=drive_link
Download testing trace: https://drive.google.com/file/d/1z3BN9DbDDc0o1qir7VF64Rf5t-4F6czW/view?usp=drive_link
Download carpack01 (for VISTA): https://drive.google.com/file/d/13ENk6GEKGUfIkVc4Na0CiWlUCMJRqew5/view?usp=drive_link
bash scripts/eval_example.sh --trace-paths ./fakepath/20220113-131922_lexus_devens_outerloop_reverse --ckpt ./fakepath/version_5/checkpoints/epoch\=9-step\=29239.ckpt --state-net-ckpt ./fakepath/version_1/checkpoints/epoch=7-step=36374.ckpt --mesh-dir ./fakepath/carpack01/
Note: if state_net is missing, then the barrier_net uses the ground truth states of the ego vehicle and obstacles (available in VISTA).
If you find this useful, please cite our work:
@article{xiao2023bnet,
title={BarrierNet: Differentiable Control Barrier Functions for Learning of Safe Robot Control},
author={Wei Xiao and Tsun-Hsuan Wang and Ramin Hasani and Makram Chahine and Alexander Amini and Xiao Li and Daniela Rus},
journal={IEEE Transactions on Robotics},
year={2023},
publisher={IEEE}
}