Habitat-Sim

A flexible, high-performance 3D simulator with configurable agents, multiple sensors, and generic 3D dataset handling (with built-in support for MatterPort3D, Gibson, Replica, and other datasets). 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!

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Table of contents

0. Updates
1. Motivation
2. Citing Habitat
3. Details
4. Performance
5. Quick installation
6. Testing
7. Developer installation and getting started
8. Datasets
9. Examples
10. Common issues
11. Acknowledgments
12. External Contributions
13. License
14. References

Updates

• Urgent Update (4/2/19) There was a bug in the code used to generate the semantic meshes habitat verion of MP3D. If you do not have a README stating this was fixed in your download of this dataset, please redownload using the download_mp.py script.

Motivation

AI Habitat enables training of embodied AI agents (virtual robots) in a highly photorealistic & efficient 3D simulator, before transferring the learned skills to reality. This empowers a paradigm shift from 'internet AI' based on static datasets (e.g. ImageNet, COCO, VQA) to embodied AI where agents act within realistic environments, bringing to the fore active perception, long-term planning, learning from interaction, and holding a dialog grounded in an environment.

Citing Habitat

If you use the Habitat platform in your research, please cite the following technical report:

@article{habitat19arxiv,
  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},
  journal = {arXiv preprint arXiv:1904.01201},
  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.

Details

The Habitat-Sim backend module is implemented in C++ and leverages the magnum graphics middleware library to support cross-platform deployment on a broad variety of hardware configurations. The architecture of the main abstraction classes is shown below. The design of this module ensures a few key properties:

• Memory-efficient management of 3D environment resources (triangle mesh geometry, textures, shaders) ensuring shared resources are cached and re-used
• Flexible, structured representation of 3D environments using SceneGraphs, allowing for programmatic manipulation of object state, and combination of objects from different environments
• High-efficiency rendering engine with multi-attachment render passes for reduced overhead when multiple sensors are active
• Arbitrary numbers of Agents and corresponding Sensors that can be linked to a 3D environment by attachment to a SceneGraph.

Architecture of Habitat-Sim main classes

The Simulator delegates management of all resources related to 3D environments to a ResourceManager that is responsible for loading and caching 3D environment data from a variety of on-disk formats. These resources are used within SceneGraphs at the level of individual SceneNodes that represent distinct objects or regions in a particular Scene. Agents and their Sensors are instantiated by being attached to SceneNodes in a particular SceneGraph.

Example rendered sensor observations

Performance

The table below reports performance statistics for a test scene from the Matterport3D dataset (id 17DRP5sb8fy) on a Xeon E5-2690 v4 CPU and Nvidia Titan Xp. Single-thread performance reaches several thousand frames per second, while multi-process operation with several independent simulation backends can reach more than 10,000 frames per second on a single GPU!

	1 proc			3 procs			5 procs
Sensors / Resolution	128	256	512	128	256	512	128	256	512
RGB	4093	1987	848	10638	3428	2068	10592	3574	2629
RGB + depth	2050	1042	423	5024	1715	1042	5223	1774	1348
RGB + depth + semantics*	439	346	185	502	385	336	500	390	367

Previous simulation platforms that have operated on similar datasets typically produce on the order of a couple hundred frames per second. For example Gibson reports up to about 150 fps with 8 processes, and MINOS reports up to about 167 fps with 4 threads.

*Note: The semantic sensor in MP3D houses currently requires the use of additional house 3D meshes with orders of magnitude more geometric complexity leading to reduced performance. We expect this to be addressed in future versions leading to speeds comparable to RGB + depth; stay tuned.

To run the above benchmarks on your machine, see instructions in the examples section.

Quick installation

1. Clone the repo

2. Install numpy in your python env of choice (e.g., pip install numpy or conda install numpy)

3. Install dependencies in your python env of choice (e.g., pip install -r requirements.txt)

4. Install Habitat-Sim via python setup.py install in your python env of choice (note: python 3 is required)

   Use python setup.py install --headless for headless systems (i.e. without an attached display) or if you need multi-gpu support.

   Note: the build requires cmake version 3.10 or newer. You can install cmake through conda install cmake or download directly from https://cmake.org/download/

Testing

1. Download the test scenes from this link and extract locally.
2. Interactive testing: Use the interactive viewer included with Habitat-Sim

   build/viewer /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 to control gaze direction (look up/down/left/right). Try to find the picture of a woman surrounded by a wreath. Have fun!
3. Non-interactive testing: Run the example script:

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

We also provide a docker setup for habitat-stack, refer to habitat-docker-setup.

Developer installation and getting started

1. Clone the repo.
2. Install numpy in your python env of choice (e.g., pip install numpy or conda install numpy)
3. Install dependencies in your python env of choice (e.g., pip install -r requirements.txt)
4. If you are using a virtual/conda environment, make sure to use the same environment throughout the rest of the build
5. Build using ./build.sh or ./build.sh --headless for headless systems (i.e. without an attached display) or if you need multi-gpu support
6. Test the build as described above (except that the interactive viewer is at build/utils/viewer/viewer).
7. For use with Habitat-API and your own python code, add habitat-sim to your PYTHONPATH (not necessary if you used python setup.py install instead of build.sh). For example modify your .bashrc (or .bash_profile in Mac osx) file by adding the line:

   export PYTHONPATH=$PYTHONPATH:/path/to/habitat-sim/

Datasets

• The full Matterport3D (MP3D) dataset for use with Habitat can be downloaded using the official Matterport3D download script as follows: python download_mp.py --task habitat -o path/to/download/. You only need the habitat zip archive and not the entire Matterport3D dataset. Note that this download script requires python 2.7 to run.
• The Gibson dataset for use with Habitat can be downloaded by agreeing to the terms of use in the Gibson repository

Examples

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

To reproduce the benchmark table from above run examples/benchmark.py --scene /path/to/mp3d/17DRP5sb8fy/17DRP5sb8fy.glb.

Common issues

• Build is tested on Ubuntu 16.04 with gcc 7.1.0, Fedora 28 with gcc 7.3.1, and MacOS 10.13.6 with Xcode 10 and clang-1000.10.25.5. If you experience compilation issues, please open an issue with the details of your OS and compiler versions.
• If you are running on a remote machine and experience display errors when initializing the simulator, ensure you do not have DISPLAY defined in your environment (run unset DISPLAY to undefine the variable)
• If your machine has a custom installation location for the nvidia OpenGL and EGL drivers, you may need to manually provide the EGL_LIBRARY path to cmake as follows. Add -DEGL_LIBRARY=/usr/lib/x86_64-linux-gnu/nvidia-opengl/libEGL.so to the build.sh command line invoking cmake. When running any executable adjust the environment as follows: LD_LIBRARY_PATH=/usr/lib/x86_64-linux-gnu/nvidia-opengl:${LD_LIBRARY_PATH} examples/example.py.

Code style

We use clang-format-8 for linting and code style enforcement of c++ code. Code style follows the Google C++ guidelines. Install clang-format-8 through brew install clang-format on MacOS. For other systems, clang-format-8 can be installed via conda install clangdev -c conda-forge or by downloading binaries or sources from releases.llvm.org/download. For vim integration add to your .vimrc file map <C-K> :%!clang-format<cr> and use Ctrl+K to format entire file. Integration plugin for vscode.

We use black and isort for linting and code style of python code. Install black and isort through pip install -U black isort. They can then be ran via black . and isort.

We also offer pre-commit hooks to help with automatically formatting code. Install the pre-commit hooks with pip install pre-commit && pre-commit install.

Development Tips

1. Install ninja (sudo apt install ninja-build on Linux, or brew install ninja on MacOS) for significantly faster incremental builds
2. Install ccache (sudo apt install ccache on Linux, or brew install ccache on MacOS) for significantly faster clean re-builds and builds with slightly different settings
3. You can skip reinstalling magnum every time buy adding the argument of --skip-install-magnum to either build.sh or setup.py. Note that you will still need to install magnum bindings once.
4. Arguments to build.sh and setup.py can be cached between subsequent invocations with the flag --cache-args on the first invocation.

Acknowledgments

The Habitat project would not have been possible without the support and contributions of many individuals. We would like to thank Xinlei Chen, Georgia Gkioxari, Daniel Gordon, Leonidas Guibas, Saurabh Gupta, Or Litany, Marcus Rohrbach, Amanpreet Singh, Devendra Singh Chaplot, Yuandong Tian, and Yuxin Wu for many helpful conversations and guidance on the design and development of the Habitat platform.

External Contributions

• 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 habitat_sim/agent/controls/pyrobot_noisy_controls.py

License

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

References

1. Habitat: A Platform for Embodied AI Research. Manolis Savva, Abhishek Kadian, Oleksandr Maksymets, Yili Zhao, Erik Wijmans, Bhavana Jain, Julian Straub, Jia Liu, Vladlen Koltun, Jitendra Malik, Devi Parikh, Dhruv Batra. Tech report, arXiv:1904.01201, 2019.