MINOS Simulator

MINOS is a simulator designed to support the development of multisensory models for goal-directed navigation in complex indoor environments. MINOS leverages large datasets of complex 3D environments and supports flexible configuration of multimodal sensor suites.

Live demos

Try our live web client demos!

MINOS for SUNCG

MINOS for Matterport3D

Use the I/J/K/L keys to step and turn, the arrow keys to strafe and look up or down, the R key to respawn, and the O key to update the agent observations panel on the right.

Installing

0. MINOS can be used on Linux and MacOS systems. Use under Windows is possible but not recommended.

1. Request and download the SUNCG and Matterport3D datasets. Please indicate "use with MINOS simulator" in your request email. For Matterport3D, use the invocation download_mp.py --task_data minos -o . with the provided download script. This will download a 5.1GB zip archive which expands to approximately 6.3GB. For SUNCG, if you are already a registered user, please send another email to request the additional MINOS data packages. The SUNCG data package is a 5.0GB zip archive which expands to approximately 8.8GB.

2. Install node.js using the Node Version Manager (nvm).

   curl -o- https://raw.githubusercontent.com/creationix/nvm/v0.33.7/install.sh | bash
   source ~/.bashrc
   nvm install node
   

   If you use zsh instead of bash, replace all instances of bash with zsh. Confirm node installation is succesful using node -v at the terminal.

3. Build the MINOS server modules by running npm install inside the server directory. This process will download and compile all server module dependencies and might take a few minutes. There are some prerequisites for the build process in Ubuntu, they can be installed using sudo apt-get install build-essential libxi-dev libglu1-mesa-dev libglew-dev libvips.

4. Make sure you have a Python3 installation with pip3 available on your command line. The following steps can be carried out in a virtualenv, or with the system python installation.

5. Install the minos Python module by running pip3 install -e . in the root of the repository.

6. Extract the SUNCG and/or MP3D data packages under $HOME/work/suncg and/or $HOME/work/mp3d, or under another parent directory of your choice. If you choose a directory other than $HOME/work as the dataset parent directory remember to run all remaining commands with the prefix NODE_BASE_URL=path/to/data_parent_dir.

7. Check that everything works by running the interactive client through python3 -m minos.tools.pygame_client, invoked from the root of the repository. You should see a live view which you can control with the W/A/S/D keys and the arrow keys. This client can be configured through various command line arguments. Run with the --help argument for an overview and try some of these other examples:

   • python3 -m minos.tools.pygame_client --empty_room : navigation in empty SUNCG environments
   • python3 -m minos.tools.pygame_client --source mp3d --scene_ids 17DRP5sb8fy --task point_goal : an example Matterport3D environment
   • python3 -m minos.tools.pygame_client --agent_config agent_gridworld : discrete navigation agent
   • python3 -m minos.tools.pygame_client --depth -s normal -s objectId -s objectType -s map --navmap --width 128 --height 128 : multimodal agent with depth, normals, object instance and category frames

Documentation

See the API document for an overview of the Simulator API and a reference of the available configuration parameters. Please also refer to the FAQ document for answers to common questions and resolutions for common errors.

OpenAI gym example code

We provide a demo gym wrapper of MINOS in gym/demo.py. Install by running pip3 install -e . in the gym directory. Run through python3 demo.py. Various configuration options are available and documented in the --help information. For example, you can run the room goal task in small Matterport3D environments using:

python3 demo.py --env_config roomgoal_mp3d_s

Or run the object goal (door target) task in medium furnished SUNCG environments using:

python3 demo.py --env_config objectgoal_suncg_mf

Available tools and scripts

We provide a collection of scripts that are useful for setting up experiments, benchmarking, and visualizing agent episodes. The scripts provide --help documentation.

Presampling episodes for reproducible testing

• minos/tools/generate_episodes.py - Presamples agent episodes (start states and goals) and produces a CSV format file that can be used as a parameter to the states_file task configuration option.

• minos/server/check_episode_states.js - Checks presampled agent episodes to verify navigability.

Exporting precomputed resources

• minos/server/export_navgrid.js - Exports precomputed navigation maps (will need to be computed and exported for scene sets other than the ones provided in the repo):

  NODE_BASE_URL=~/work ./export_navgrid.js --config_file ../config/visualize_traces.json --output_dir navgrid --ids 004667ffa2594fa780e84d7b979f25ab
  

  Use --arch_only or --empty_room to generate navigation maps for architecture-only and empty room variants.

Visualization

• minos/server/visualize_path.js - Visualizes presampled episodes and shortest paths:

  NODE_BASE_URL=~/work node --max-old-space-size=2048 visualize_path.js --compress_png --config_file config/visualize_traces.json --color beige --output_dir simpath --input ~/code/sim/data/episode_states.test.csv --ids dc251799d2b7cbc1fee86fbf524f2bec --allow_diag false
  

• minos/server/visualize_traces.js - Visualizes prerecorded action traces.

Benchmarking

Benchmarking scripts to test the performance of the simulator:

• minos/server/benchmark.js - Use to benchmark the javascript server component in standalone mode.

• minos/tools/benchmark.py - Use to benchmark client-server communication through SocketIO. Allows for specification of sceneIds and episodes per scene to go through, various simulator configurations (sensory inputs to enable, rendering options) and for running with multiple simulators.

News

• 2017-12-11 MINOS beta release!

Roadmap

We are committed to continuously improving MINOS and supporting the research community. We will post a detailed roadmap and contribution guidelines in the near future.

Citation

If you use MINOS please cite our technical report:

@article{savva2017minos,
  author = {Manolis Savva and Angel X. Chang and Alexey Dosovitskiy and Thomas Funkhouser and Vladlen Koltun},
  title = {{MINOS}: Multimodal Indoor Simulator for Navigation in Complex Environments},
  journal = {arXiv:1712.03931},
  year = {2017}
}

License

MINOS specific code is distributed under the MIT License.