Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agents

This is a PyTorch implementation of our ICCV 2021 paper, Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agents.

Project Webpage: https://shivanshpatel35.github.io/comon/

CoMON Task

In CoMON, an episode involves two heterogeneous agents — a disembodied agent with access to oracle top-down map of the environment and an embodied agent which navigates and interacts with the environment. The two agents communicate and collaborate to perform the MultiON task.

Communication Mechanisms

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 .

For installation issues in habitat, feel free to raise an issue in this repository, or in the corresponding habitat repository.

Setup

Clone the repository and install the requirements:

git clone https://github.com/saimwani/comon
cd comon
pip install -r requirements.txt

Downloading data and checkpoints

To evaluate pre-trained models and train new models, you will need to download the MultiON dataset, including objects inserted into the scenes, and model checkpoints for CoMON. Running download_data.sh from the root directory (CoMON/) will download the data and extract it to appropriate directories. Note that you are still required to download Matterport3D scenes after you run the script (see section on Download Matterport3D scenes below).

bash download_multion_data.sh

Download multiON dataset

You do not need to complete this step if you have successfully run the download_data.sh script above.

Run the following to download multiON dataset and cached oracle occupancy maps:

mkdir data
cd data
mkdir datasets
cd datasets
wget -O multinav.zip "http://aspis.cmpt.sfu.ca/projects/multion/multinav.zip"
unzip multinav.zip && rm multinav.zip
cd ../
wget -O objects.zip "http://aspis.cmpt.sfu.ca/projects/multion/objects.zip"
unzip objects.zip && rm objects.zip
wget -O default.phys_scene_config.json "http://aspis.cmpt.sfu.ca/projects/multion/default.phys_scene_config.json"
cd ../
mkdir oracle_maps
cd oracle_maps
wget -O map300.pickle "http://aspis.cmpt.sfu.ca/projects/multion/map300.pickle"
cd ../

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:

CoMON/
  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.

Usage

Pre-trained models

You do not need to complete this step if you have successfully run the download_data.sh script above.

mkdir model_checkpoints

Download a model checkpoint for Unstructured communication (U-Comm) or Structured communication (S-Comm) setup as shown below.

Evaluation

To evaluate a pretrained S-Comm agent, run this from the root folder (CoMON/):

python habitat_baselines/run.py --exp-config habitat_baselines/config/multinav/comon.yaml --comm-type struc --run-type eval

For U-Comm setup, replace struc with un-struc.

Average evaluation metrics are printed on the console when evaluation ends. Detailed metrics are placed in tb/eval/metrics directory.

Training

For training an S-Comm agent, run this from the root directory:

python habitat_baselines/run.py --exp-config habitat_baselines/config/multinav/comon.yaml --comm-type struc --run-type train

For U-Comm, replace struc with un-struc.

Citation

Shivansh Patel*, Saim Wani*, Unnat Jain*, Alexander Schwing, Svetlana Lazebnik, Manolis Savva, Angel X. Chang. Interpretation of Emergent Communication in Heterogeneous Collaborative Embodied Agents In ICCV 2021. PDF

Bibtex

@inproceedings{patel2021interpretation,
  Author = {Shivansh Patel and Saim Wani and Unnat Jain and Alexander Schwing and 
  Svetlana Lazebnik and  Manolis Savva and Angel X. Chang},
  Title = {Interpretation of Emergent Communication 
  in Heterogeneous Collaborative Embodied Agents},
  Booktitle = {ICCV},
  Year = {2021}
  }

Acknowledgements

This repository is built upon Habitat Lab.

GitHub

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