Installation

1. Preparing conda env

   Assuming you have conda installed, let's prepare a conda env:

   # We require python>=3.9 and cmake>=3.14
   conda create -n habitat python=3.9 cmake=3.14.0
   conda activate habitat

2. conda install habitat-sim

   • To install habitat-sim with bullet physics

     conda install habitat-sim withbullet -c conda-forge -c aihabitat
     

     Note, for newer features added after the most recent release, you may need to install aihabitat-nightly. See Habitat-Sim's installation instructions for more details.

3. pip install habitat-lab stable version.

   git clone --branch stable https://github.com/facebookresearch/habitat-lab.git
   cd habitat-lab
   pip install -e habitat-lab  # install habitat_lab

   Please notice that download the "habitat-lab" project under the path "$VLN_ROOT/dependencies/".

4. Install habitat-baselines.

   The command above will install only core of Habitat-Lab. To include habitat_baselines along with all additional requirements, use the command below after installing habitat-lab:

   pip install -e habitat-baselines  # install habitat_baselines

5. Install pytorch (assuming cuda 11.3):

   pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 torchaudio==0.12.1 --extra-index-url https://download.pytorch.org/whl/cu113

6. Install detectron2: Install the detectron2 based on:

   https://github.com/facebookresearch/detectron2?tab=readme-ov-file
   

7. Add repository to python path:

   export PYTHONPATH=$PYTHONPATH:$VLN_ROOT
   

Datasets

Please use the datasets of HM3D-v0.2-val_split. You should download both Scenes datasets and the task datasets. Common task and episode datasets used with Habitat-Lab.

Perception Model

Download the image segmentation model [URL] to $VLN_ROOT/dependencies/mask_rcnn/.'

Quick start and quick evaluation

python task_evaluation.py