habitat-lab

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
   

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
   

Docker Setup

We provide docker containers for Habitat, updated approximately once per year for the Habitat Challenge. This works on machines with an NVIDIA GPU and requires users to install nvidia-docker. To setup the habitat stack using docker follow the below steps:

1. Pull the habitat docker image: docker pull fairembodied/habitat-challenge:testing_2022_habitat_base_docker

2. Start an interactive bash session inside the habitat docker: docker run --runtime=nvidia -it fairembodied/habitat-challenge:testing_2022_habitat_base_docker

3. Activate the habitat conda environment: conda init; source ~/.bashrc; source activate habitat

4. Run the testing scripts as above: cd habitat-lab; python examples/example.py. This should print out an output like:

   Agent acting inside environment.
   Episode finished after 200 steps.
   

License

Habitat-Lab is MIT licensed. See the LICENSE file for details.

Copyright (c) Meta Platforms, Inc. and affiliates.

The trained models and the task datasets are considered data derived from the correspondent scene datasets.

• Matterport3D based task datasets and trained models are distributed with Matterport3D Terms of Use and under CC BY-NC-SA 3.0 US license.
• Gibson based task datasets, the code for generating such datasets, and trained models are distributed with Gibson Terms of Use and under CC BY-NC-SA 3.0 US license.