BRNE: Fast Mixed Strategy Nash Equilibrium Algorithm for Social Navigation

This repository contains relevant code for BRNE (Bayes' Rule Nash Equilibrum), an algorithm that computes mixed strategy Nash equilibrium for real-time human-aware navigation in dense crowds. It contains PyTorch-based tutorial and a fully ROS integrated social navigation framework powered by BRNE, implemented in both PyTorch and C++.

A journal manuscript is currently under review, an arXiv preprint can be found here.

Video demonstrations of the algorithm deployed on an Unitree Go1 quadruped can be found at the project website and Katie's portfolio post. You can also find more details on the project from our invited presentation at NVIDIA GTC 2024.

To cite this work:

@article{sun2024mixed,
  title={Mixed-Strategy Nash Equilibrium for Crowd Navigation},
  author={Sun, Muchen and Baldini, Francesca and Hughes, Katie and Trautman, Peter and Murphey, Todd},
  journal={arXiv preprint arXiv:2403.01537},
  year={2024}
}

The structure of the repo is as follow:

• tutorials contains Python tutorials to illustrate the principle of the BRNE algorithm and intuitive PyTorch implementation for simple hallway navigation scenarios.
• brne_nav contains the fully integrated ROS social navigation framework. If you are interested in deploying BRNE, you should copy this directory as a ROS package.

Contact: Please contact Muchen Sun (muchen@u.northwestern.edu) if you have any question.