install_habitat.md

Install Habitat

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

   # We fix the habitat-sim version to 0.3.1. 
   # You might have compatibility issue for other versions since habitat is under active development
   conda install habitat-sim=0.3.1 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.

   cd habitat-mas
   pip install -e habitat-lab  # install habitat_lab
   # NOTE: You need to install our modified habitat-lab package rather than the original repo.
   # 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

Testing

1. Let's download some 3D assets using Habitat-Sim's python data download utility:

   • Download (testing) 3D scenes:

     python -m habitat_sim.utils.datasets_download --uids habitat_test_scenes --data-path data/

     Note that these testing scenes do not provide semantic annotations.

   • Download point-goal navigation episodes for the test scenes:

     python -m habitat_sim.utils.datasets_download --uids habitat_test_pointnav_dataset --data-path data/

2. Non-interactive testing: Test the Pick task: Run the example pick task script

   python examples/example.py

   which uses habitat-lab/habitat/config/benchmark/rearrange/skills/pick.yaml for configuration of task and agent. The script roughly does this:

   import gym
   import habitat.gym
   
   # Load embodied AI task (RearrangePick) and a pre-specified virtual robot
   env = gym.make("HabitatRenderPick-v0")
   observations = env.reset()
   
   terminal = False
   
   # Step through environment with random actions
   while not terminal:
       observations, reward, terminal, info = env.step(env.action_space.sample())

   To modify some of the configurations of the environment, you can also use the habitat.gym.make_gym_from_config method that allows you to create a habitat environment using a configuration.

   config = habitat.get_config(
     "benchmark/rearrange/skills/pick.yaml",
     overrides=["habitat.environment.max_episode_steps=20"]
   )
   env = habitat.gym.make_gym_from_config(config)

   If you want to know more about what the different configuration keys overrides do, you can use this reference.

   See examples/register_new_sensors_and_measures.py for an example of how to extend habitat-lab from outside the source code.

3. Interactive testing: Using you keyboard and mouse to control a Fetch robot in a ReplicaCAD environment:

   # Pygame for interactive visualization, pybullet for inverse kinematics
   pip install pygame==2.0.1 pybullet==3.0.4
   
   # Interactive play script
   python examples/interactive_play.py --never-end

   Use I/J/K/L keys to move the robot base forward/left/backward/right and W/A/S/D to move the arm end-effector forward/left/backward/right and E/Q to move the arm up/down. The arm can be difficult to control via end-effector control. More details in documentation. Try to move the base and the arm to touch the red bowl on the table. Have fun!

   Note: Interactive testing currently fails on Ubuntu 20.04 with an error: X Error of failed request: BadAccess (attempt to access private resource denied). We are working on fixing this, and will update instructions once we have a fix. The script works without errors on MacOS.