A high-performance physics-enabled 3D simulator with support for:

• 3D scans of indoor/outdoor spaces (with built-in support for HM3D, MatterPort3D, Gibson, Replica, and other datasets)
• CAD models of spaces and piecewise-rigid objects (e.g. ReplicaCAD, YCB, Google Scanned Objects),
• Configurable sensors (RGB-D cameras, egomotion sensing)
• Robots described via URDF (mobile manipulators like Fetch, fixed-base arms like Franka, quadrupeds like AlienGo),
• Rigid-body mechanics (via Bullet).

The design philosophy of Habitat is to prioritize simulation speed over the breadth of simulation capabilities. When rendering a scene from the Matterport3D dataset, Habitat-Sim achieves several thousand frames per second (FPS) running single-threaded and reaches over 10,000 FPS multi-process on a single GPU. Habitat-Sim simulates a Fetch robot interacting in ReplicaCAD scenes at over 8,000 steps per second (SPS), where each ‘step’ involves rendering 1 RGBD observation (128×128 pixels) and rigid-body dynamics for 1/30sec.

Habitat-Sim is typically used with Habitat-Lab, a modular high-level library for end-to-end experiments in embodied AI -- defining embodied AI tasks (e.g. navigation, instruction following, question answering), training agents (via imitation or reinforcement learning, or no learning at all as in classical SensePlanAct pipelines), and benchmarking their performance on the defined tasks using standard metrics.

1. Citing Habitat
2. Installation
3. Testing
4. Documentation
5. Datasets
6. External Contributions
7. License

If you use the Habitat platform in your research, please cite the Habitat 1.0, Habitat 2.0, and Habitat 3.0 papers:

@misc{puig2023habitat3,
      title  = {Habitat 3.0: A Co-Habitat for Humans, Avatars and Robots},
      author = {Xavi Puig and Eric Undersander and Andrew Szot and Mikael Dallaire Cote and Ruslan Partsey and Jimmy Yang and Ruta Desai and Alexander William Clegg and Michal Hlavac and Tiffany Min and Theo Gervet and Vladimír Vondruš and Vincent-Pierre Berges and John Turner and Oleksandr Maksymets and Zsolt Kira and Mrinal Kalakrishnan and Jitendra Malik and Devendra Singh Chaplot and Unnat Jain and Dhruv Batra and Akshara Rai and Roozbeh Mottaghi},
      year={2023},
      archivePrefix={arXiv},
}

@inproceedings{szot2021habitat,
  title     =     {Habitat 2.0: Training Home Assistants to Rearrange their Habitat},
  author    =     {Andrew Szot and Alex Clegg and Eric Undersander and Erik Wijmans and Yili Zhao and John Turner and Noah Maestre and Mustafa Mukadam and Devendra Chaplot and Oleksandr Maksymets and Aaron Gokaslan and Vladimir Vondrus and Sameer Dharur and Franziska Meier and Wojciech Galuba and Angel Chang and Zsolt Kira and Vladlen Koltun and Jitendra Malik and Manolis Savva and Dhruv Batra},
  booktitle =     {Advances in Neural Information Processing Systems (NeurIPS)},
  year      =     {2021}
}

@inproceedings{habitat19iccv,
  title     =     {Habitat: {A} {P}latform for {E}mbodied {AI} {R}esearch},
  author    =     {Manolis Savva and Abhishek Kadian and Oleksandr Maksymets and Yili Zhao and Erik Wijmans and Bhavana Jain and Julian Straub and Jia Liu and Vladlen Koltun and Jitendra Malik and Devi Parikh and Dhruv Batra},
  booktitle =     {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
  year      =     {2019}
}

Habitat-Sim also builds on work contributed by others. If you use contributed methods/models, please cite their works. See the External Contributions section for a list of what was externally contributed and the corresponding work/citation.

Habitat-Sim can be installed in 3 ways:

1. Via Conda - Recommended method for most users. Stable release and nightly builds.
2. [Experimental] Via PIP - pip install . to compile the latest headless build with Bullet. Read build instructions and common build issues.
3. Via Docker - Updated approximately once per year for the Habitat Challenge. Read habitat-docker-setup.
4. Via Source - For active development. Read build instructions and common build issues.

Habitat is under active development, and we advise users to restrict themselves to stable releases. Starting with v0.1.4, we provide conda packages for each release.

1. Preparing conda env

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

   # We require python>=3.9 and cmake>=3.10
   conda create -n habitat python=3.9 cmake=3.14.0
   conda activate habitat

2. conda install habitat-sim

   Pick one of the options below depending on your system/needs:

   • To install on machines with an attached display:

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

   • To install on headless machines (i.e. without an attached display, e.g. in a cluster) and machines with multiple GPUs (this parameter relies on EGL and thus does not work on MacOS):

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

   • [Most common scenario] To install habitat-sim with bullet physics

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

   • Note: Build parameters can be chained together. For instance, to install habitat-sim with physics on headless machines:

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

Conda packages for older versions can installed by explicitly specifying the version, e.g. conda install habitat-sim=0.1.6 -c conda-forge -c aihabitat.

We also provide a nightly conda build for the main branch. However, this should only be used if you need a specific feature not yet in the latest release version. To get the nightly build of the latest main, simply swap -c aihabitat for -c aihabitat-nightly.

1. Let's download some 3D assets using our python data download utility:

   • Download (testing) 3D scenes

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

     Note that these testing scenes do not provide semantic annotations. If you would like to test the semantic sensors via example.py, please use the data from the Matterport3D dataset (see Datasets).

   • Download example objects

     python -m habitat_sim.utils.datasets_download --uids habitat_example_objects --data-path /path/to/data/

2. Interactive testing: Use the interactive viewer included with Habitat-Sim in either C or python:

   #C  
   # ./build/viewer if compiling locally
   habitat-viewer /path/to/data/scene_datasets/habitat-test-scenes/skokloster-castle.glb
   
   #Python
   #NOTE: depending on your choice of installation, you may need to add '/path/to/habitat-sim' to your PYTHONPATH.
   #e.g. from 'habitat-sim/' directory run 'export PYTHONPATH=$(pwd)'
   python examples/viewer.py --scene /path/to/data/scene_datasets/habitat-test-scenes/skokloster-castle.glb

   You should be able to control an agent in this test scene. Use W/A/S/D keys to move forward/left/backward/right and arrow keys or mouse (LEFT click) to control gaze direction (look up/down/left/right). Try to find the picture of a woman surrounded by a wreath. Have fun!

3. Physical interactions: Habitat-sim provides rigid and articulated dynamics simulation via integration with Bullet physics. Try it out now with our interactive viewer functionality in C or python.

   First, download our fully interactive ReplicaCAD apartment dataset (140 MB):

   #NOTE: by default, data will be downloaded into habitat-sim/data/. Optionally modify the data path by adding:  `--data-path /path/to/data/`
   # with conda install
   python -m habitat_sim.utils.datasets_download --uids replica_cad_dataset
   
   # with source (from inside habitat_sim/)
   python src_python/habitat_sim/utils/datasets_download.py --uids replica_cad_dataset

   • Alternatively, 105 scene variations with pre-baked lighting are available via --uids replica_cad_baked_lighting (480 MB).

   Then load a ReplicaCAD scene in the viewer application with physics enabled. If you modified the data path above, also modify it in viewer calls below.

   #C  
   # ./build/viewer if compiling locally
   habitat-viewer --enable-physics --dataset data/replica_cad/replicaCAD.scene_dataset_config.json -- apt_1
   
   #python
   #NOTE: habitat-sim/ directory must be on your `PYTHONPATH`
   python examples/viewer.py --dataset data/replica_cad/replicaCAD.scene_dataset_config.json --scene apt_1

   • Using scenes with pre-baked lighting instead? Use --dataset data/replica_cad_baked_lighting/replicaCAD_baked.scene_dataset_config.json --scene Baked_sc1_staging_00

   The viewer application outputs the full list of keyboard and mouse interface options to the console at runtime.

   Quickstart Example:

   • WASD to move
   • LEFT click and drag the mouse to look around
   • press SPACE to toggle simulation off/on (default on)
   • press 'm' to switch to "GRAB" mouse mode
   • now LEFT or RIGHT click and drag to move objects or open doors/drawers and release to drop the object
   • with an object gripped, scroll the mouse wheel to:
     • (default): move it closer or farther away
     • ( ALT): rotate object fixed constraint frame (yaw)
     • ( CTRL): rotate object fixed constraint frame (pitch)
     • ( ALT CTRL): rotate object fixed constraint frame (roll)

4. Non-interactive testing (e.g. for headless systems): Run the example script:

   python /path/to/habitat-sim/examples/example.py --scene /path/to/data/scene_datasets/habitat-test-scenes/skokloster-castle.glb

   The agent will traverse a particular path and you should see the performance stats at the very end, something like this: 640 x 480, total time: 3.208 sec. FPS: 311.7.

   To reproduce the benchmark table from Habitat ICCV'19 run examples/benchmark.py --scene /path/to/mp3d_example/17DRP5sb8fy/17DRP5sb8fy.glb.

   Additional arguments to example.py are provided to change the sensor configuration, print statistics of the semantic annotations in a scene, compute action-space shortest path trajectories, and set other useful functionality. Refer to the example.py and demo_runner.py source files for an overview.

   Load a specific MP3D or Gibson house: examples/example.py --scene path/to/mp3d/house_id.glb.

   We have also provided an example demo for reference.

   To run a physics example in python (after building with "Physics simulation via Bullet"):

   python examples/example.py --scene /path/to/data/scene_datasets/habitat-test-scenes/skokloster-castle.glb --enable_physics

   Note that in this mode the agent will be frozen and oriented toward the spawned physical objects. Additionally, --save_png can be used to output agent visual observation frames of the physical scene to the current directory.

• If you are running on a remote machine and experience display errors when initializing the simulator, e.g.

   X11: The DISPLAY environment variable is missing
   Could not initialize GLFW

  ensure you do not have DISPLAY defined in your environment (run unset DISPLAY to undefine the variable)

• If you see libGL errors like:

   X11: The DISPLAY environment variable is missing
   Could not initialize GLFW

  chances are your libGL is located at a non-standard location. See e.g. this issue.

Browse the online Habitat-Sim documentation.

Check out our ECCV tutorial series for a hands-on quickstart experience.

Can't find the answer to your question? Try asking the developers and community on our Discussions forum.

HowTo use common supported datasets with Habitat-Sim.

• If you use the noise model from PyRobot, please cite the their technical report.

  Specifically, the noise model used for the noisy control functions named pyrobot_* and defined in src_python/habitat_sim/agent/controls/pyrobot_noisy_controls.py

• If you use the Redwood Depth Noise Model, please cite their paper

  Specifically, the noise model defined in src_python/habitat_sim/sensors/noise_models/redwood_depth_noise_model.py and src/esp/sensor/RedwoodNoiseModel.*

Habitat-Sim is MIT licensed. See the LICENSE for details.

The demo scripts use:

• The King´s Hall by Skokloster Castle (Skoklosters slott) licensed under Creative Commons Attribution
• Van Gogh Room by ruslans3d licensed under Creative Commons Attribution