MAIN: A Real-world Multi-agent Indoor Navigation Benchmark for Cooperative Learning

An implementation of “MAIN: A Real-world Multi-agent Indoor Navigation Benchmark for Cooperative Learning”

Architecture Overview

Installing dependencies:

This code is tested on python 3.6.10, pytorch v1.4.0 and CUDA V9.1.85.

Install pytorch from https://pytorch.org/ according to your machine configuration.

This code uses older versions of habitat-sim and habitat-lab. Install them by running the following commands:

Installing habitat-sim:

git clone https://github.com/facebookresearch/habitat-sim.git
cd habitat-sim 
git checkout ae6ba1cdc772f7a5dedd31cbf9a5b77f6de3ff0f
pip install -r requirements.txt; 
python setup.py install --headless # (for headless machines with GPU)
python setup.py install # (for machines with display attached)

Installing habitat-lab:

git clone --branch stable https://github.com/facebookresearch/habitat-lab.git
cd habitat-lab
git checkout 676e593b953e2f0530f307bc17b6de66cff2e867
pip install -e .

Setup

Clone the repository and install the requirements:

git clone https://github.com/ZhuFengdaaa/MAIN.git
cd MAIN
pip install -r requirements.txt

Download MAIN dataset

mkdir data
cd data
mkdir datasets
cd datasets

download dataset from the link below:

https://drive.google.com/file/d/1H3fvyPi_OoXJfzV0HzK-Gix5fiizdM4Z/view?usp=sharing

extract the dataset file:

tar-xf main_data.tar

download the oracle occupancy from the link below:

https://drive.google.com/file/d/1vDY3Wuc8jLSeGwnb2GSlGUzECTgkKo9k/view?usp=sharing

extract the oracle map file:

unzip oracle.zip

Download Matterport3D scenes

The Matterport scene dataset and multiON dataset should be placed in data folder under the root directory (multiON/) in the following format:

MAIN/
  data/
    scene_datasets/
      mp3d/
        1LXtFkjw3qL/
          1LXtFkjw3qL.glb
          1LXtFkjw3qL.navmesh
          ...
    datasets/
      multinav/
        3_ON/
          train/
            ...
          val/
            val.json.gz
        2_ON
          ...
        1_ON
          ...

Download Matterport3D data for Habitat by following the instructions mentioned here.

Training

For training an OracleEgoMap (oracle-ego) agent, run this from the root directory:

python habitat_baselines/run.py --exp-config habitat_baselines/config/multinav/ppo_mamonav.yaml --agent-type oracle-ego --run-type train

For other agent types, the --agent-type argument would change accordingly.

Evaluation

python habitat_baselines/run.py --exp-config habitat_baselines/config/multinav/ppo_mamonav.yaml --agent-type oracle-ego --run-type eval

Citation

Fengda Zhu, Siyi Hu, Yi Zhang, Haodong Hong, Yi Zhu, Xiaojun Chang, Xiaodan Liang, 2021. MAIN: A Multi-agent Indoor Navigation Benchmark for Cooperative Learning

Acknowledgements

This repository is built upon Habitat Lab, Habitat Sim, multiON.