Somil Bansal, Claire Tomlin
University of California, Berkeley

This is the official implementation of the paper "DeepReach: A Deep Learning Approach to High-Dimensional Reachability".

You can then set up a conda environment with all dependencies like so:

conda env create -f environment.yml
conda activate siren

The code is organized as follows:

• dataio.py loads training and testing data.
• training.py contains a generic training routine.
• modules.py contains layers and full neural network modules.
• utils.py contains utility functions.
• diff_operators.py contains implementations of differential operators.
• loss_functions.py contains loss functions for the different experiments.
• ./experiment_scripts/ contains scripts to reproduce experiments in the paper.
• ./validation_scripts/ contains scripts to reproduce figures in the paper.

The directory experiment_scripts contains one script per experiment in the paper.

To monitor progress, the training code writes tensorboard summaries into a "summaries"" subdirectory in the logging_root.

To start training DeepReach for air3D, you can run:

CUDA_VISIBLE_DEVICES=0 python experiment_scripts/train_hji_air3D.py --experiment_name experiment_1 --minWith target --tMax 1.1 --velocity 0.75 --omega_max 3.0 --angle_alpha 1.2 --num_src_samples 10000 --pretrain --pretrain_iters 10000 --num_epochs 120000 --counter_end 110000

This will regularly save checkpoints in the directory specified by the rootpath in the script, in a subdirectory "experiment_1".

We also provide pre-trained checkpoints that can be used to visualize the results in the paper. The checkpoints can be downloaded from

https://drive.google.com/file/d/18VkOTctkzuYuyK2GRwQ4wmN92WhdXtvS/view?usp=sharing

To visualize the trained BRTs, please run:

CUDA_VISIBLE_DEVICES=0 python validation_scripts/air3D_valfunc_and_BRT.py 

If you find our work useful in your research, please cite:

@inproceedings{bansal2020deepreach,
    author = {Bansal, Somil
              and Tomlin, Claire},
    title = {{DeepReach}: A Deep Learning Approach to High-Dimensional Reachability},
    booktitle = {IEEE International Conference on Robotics and Automation (ICRA)},
    year={2021}
}

If you have any questions, please feel free to email the authors.