Stochastic-Neural CBF: Learning a Formally Verified Control Barrier Function in Stochastic Environment
Manan Tayal, Hongchao Zhang, Pushpak Jagtap, Andrew Clark, Shishir Kolathaya
IISc, Bangalore and WashU, St. Louis
The official repository for Paper "Learning a Formally Verified Control Barrier Function in Stochastic Environment", accepted for presentation at CDC'24 Milan, Italy.
- Clone the repository:
git clone https://github.com/tayalmanan28/Stochastic-NCBF
- Install Pytorch
In main.py, define the system you are interested in:
system = 'uni' # 'ip' for inverted pendulum, 'di' for double integrator or 'uni' for unicycle model.
In hyper_para.py define the hyperparameters:
N_H_B = 1 # the number of hidden layers for the barrier
D_H_B = 20 # the number of neurons of each hidden layer for the barrier
###########################################
#Barrier function conditions
#########################################
gamma=0; #first condition <= 0
lamda=0.00001; #this is required for strict inequality >= lambda
############################################
# set loss function definition
############################################
TOL_SAFE = 0.0 # tolerance for safe and unsafe conditions
TOL_UNSAFE = 0.000
TOL_LIE = 0.000 #tolerance for the last condition
TOL_DATA_GEN = 1e-16 #for data generation
############################################
#Lipschitz bound for training
lip_h= 1
lip_dh= 1
lip_d2h= 2
############################################
# number of training epochs
############################################
EPOCHS = 500
############################################
############################################
ALPHA=0.1
BETA = 0 # if beta equals 0 then constant rate = alpha
GAMMA = 0 # when beta is nonzero, larger gamma gives faster drop of rate
#weights for loss function
DECAY_LIE = 1 # decay of lie weight 0.1 works, 1 does not work
DECAY_SAFE = 1
DECAY_UNSAFE = 1
Finally, run main.py to start training Stochastic Neural CBF:
python3 main.py
If you find our work useful in your research, please cite:
@inproceedings{tayal2024learning,
title={Learning a Formally Verified Control Barrier Function in Stochastic Environment},
author={Tayal, Manan and Zhang, Hongchao and Jagtap, Pushpak and Clark, Andrew and Kolathaya, Shishir},
booktitle={2024 63rd IEEE Conference on Decision and Control (CDC)},
year={2024}
}
If you have any questions, please feel free to email the authors.