Collaborative Visual Navigation

Introduction

CollaVN is a visual MARL dataset, created for the development of intelligent, cooperative navigation agents.

Authors: Haiyang Wang, Wenguan Wang, Jifeng Dai, Xizhou Zhu, Liwei Wang

[arxiv]

Latest Updates

[08/17/2021] Release CollaVN V1 dataset

Citation

If you use CollaVN and models, consider citing the following publication:

@article{wang2021collaborative,
  title={Collaborative Visual Navigation},
  author={Wang, Haiyang and Wang, Wenguan and Zhu, Xizhou and Dai, Jifeng and Wang, Liwei},
  journal={arXiv preprint arXiv:2107.01151},
  year={2021}
}

Installation

The code is tested under the following environment:

• Ubuntu 16.04 LTS
• Python 3.6
• Pytorch 1.8.0
• CUDA 11.1
• GCC 7.3

Install required package

conda install pytorch==1.8.0 torchvision==0.9.0 torchaudio==0.8.0 cudatoolkit=11.1 -c pytorch -c conda-forge

pip install scikit-image

Install MAVN

git clone https://github.com/Haiyang-W/MAVN.git

cd MAVN

# if you didn't create the conda environment before:
conda create -y -n MAVN python=3.6
conda activate MAVN

pip install -e . 

Note

If you have problem in openGL, please see [Trouble Shooting]

Presample Datapoint

To make the comparison fair, we pre-generated some data points, which we used in our paper, please see dataset dir. Google Drive

cd MAVN
wget https://drive.google.com/drive/folders/1xYzl5zBYOfTE2ouSIKgp6ZgrQ7lZEe7X?usp=sharing

Run Demo

python demo.py -n1 --auto_gpu_config 1 --total_num_scenes 1 --task_config ./config/CommonGoal_two_locobot.yaml

TODO:

Release CollaVN V2 and method code

Acknowledgments

Our code is heavily based on iGibson. Thanks iGibson Development Team for their awesome codebase.