This is a PyTorch implementation of the NeurIPS-22 paper: https://arxiv.org/abs/2206.12403

Arjun Majumdar*, Gunjan Aggarwal*, Bhavika Devnani, Judy Hoffman and Dhruv Batra

Georgia Institute of Technology, Meta AI

We present a scalable approach for learning open-world object-goal navigation (ObjectNav) – the task of asking a virtual robot (agent) to find any instance of an object in an unexplored environment (e.g., “find a sink”). Our approach is entirely zero-shot – i.e., it does not require ObjectNav rewards or demonstrations of any kind.

Model Architecture for ZSON.

All the required data can be downloaded from here.

1. Create a conda environment:

   conda create -n zson python=3.7 cmake=3.14.0
   

   conda activate zson
   

2. Install pytorch version 1.10.2:

   conda install pytorch==1.10.2 torchvision==0.11.3 cudatoolkit=11.3 -c pytorch -c conda-forge
   

3. Install habitat-sim:

   conda install habitat-sim-challenge-2022 headless -c conda-forge -c aihabitat
   

4. Install habitat-lab:

   git clone --branch challenge-2022 https://github.com/facebookresearch/habitat-lab.git habitat-lab-challenge-2022
   

   cd habitat-lab-challenge-2022
   

   pip install -r requirements.txt
   

   python setup.py develop --all # install habitat and habitat_baselines
   

   cd ..
   

1. Setup steps

   git clone [email protected]:gunagg/zson.git
   

   cd zson
   

   pip install -r requirements.txt
   

   python setup.py develop
   

2. Follow the instructions here to set up the data/scene_datasets/ directory. gibson scenes can be found here.

3. Download the HM3D ImageNav training dataset:

   wget https://huggingface.co/gunjan050/ZSON/resolve/main/imagenav_hm3d.zip
   

   unzip imagenav_hm3d.zip
   

   rm imagenav_hm3d.zip  # clean-up
   

4. Download the MP3D objectnav dataset.

   wget https://dl.fbaipublicfiles.com/habitat/data/datasets/objectnav/m3d/v1/objectnav_mp3d_v1.zip
   

   mkdir -p data/datasets/objectnav/mp3d/v1
   

   unzip objectnav_mp3d_v1.zip -d data/datasets/objectnav/mp3d/v1
   

   rm objectnav_mp3d_v1.zip  # clean-up
   

5. Download the HM3D objectnav dataset.

   wget https://dl.fbaipublicfiles.com/habitat/data/datasets/objectnav/hm3d/v1/objectnav_hm3d_v1.zip
   

   unzip objectnav_hm3d_v1.zip -d data/datasets/objectnav/
   

   rm objectnav_hm3d_v1.zip  # clean-up
   

6. Download the trained checkpoints zson_conf_A.pth and zson_conf_B.pth, and move to data/checkpoints.

7. To train policies using OVRL pretrained RGB encoder, download the model weights from here and move to data/models/. More details on the encoder can be found here.

8. Setup data/goal_datasets using the script tools/extract-goal-features.py. This caches CLIP goal embeddings for faster training.

   Your directory structure should now look like this:

   .
   +-- habitat-lab-v0.2.1/
   |   ...
   +-- zson/
   |   +-- data/
   |   |   +-- datasets/
   |   |   |   +-- objectnav/
   |   |   |   +-- imagenav/
   |   |   +-- scene_datasets/
   |   |   |   +-- hm3d/
   |   |   |   +-- mp3d/
   |   |   +-- goal_datasets/
   |   |   |   +-- imagenav/
   |   |   |   |   +-- hm3d/
   |   |   +-- models/
   |   |   +-- checkpoints/
   |   +-- zson/
   |   ...
   

sbatch scripts/imagenav-v1-hm3d-ovrl-rn50.sh

sbatch scripts/imagenav-v2-hm3d-ovrl-rn50.sh

To evaluate a checkpoint trained using ZSON checkpoint use the following command:

sbatch scripts/objnav-eval-$DESIRED-CONFIGURATION$-$DATASET$.sh

If you use this code in your research, please consider citing:

@inproceedings{majumdar2022zson,
  title={ZSON: Zero-Shot Object-Goal Navigation using Multimodal Goal Embeddings},
  author={Majumdar, Arjun and Aggarwal, Gunjan and Devnani, Bhavika and Hoffman, Judy and Batra, Dhruv},
  booktitle={Neural Information Processing Systems (NeurIPS)},
  year={2022}
}