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Stochastic-Neural CBF: Learning a Formally Verified Control Barrier Function in Stochastic Environment

Paper

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.

Installation

  • Clone the repository:
git clone https://github.com/tayalmanan28/Stochastic-NCBF

Usage

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

Citation

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}
}

Contact

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