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Installation (Linux, x64)

1. Download SMPL data & Render human meshes

Follow the instructions in surreal/README.md to correctly install the human meshes.

2. Download environment data

To download our custom curated maps (edited from the SD3DIS models) follow this link to our official online drive and download the stanford_builder_parser_dataset and place it in the sd3dis directory as is.

However, if you'd like to download and configure all the original maps, follow the instructions in sd3dis/README.md to correctly install the building/area meshes.

Note: SocNavBench is independent of the actual indoor office environment and human meshes used. In this work we use human meshes exported from the SURREAL dataset and scans of indoor office environments from the S3DIS dataset.

3. Install system dependencies

sudo apt install gcc g++ libassimp-dev ffmpeg

4. Setup A Virtual Environment

Install Anaconda for Linux: https://docs.anaconda.com/anaconda/install/linux/

conda env create -f environment.yml # this will install all python dependencies
conda activate socnavbench

5. Patch OpenGL and Pyassimp

In the terminal run the following commands.

bash ./socnav/patches/apply_patches.sh

6. Generate intermediate map files

The "traversible" files (.pkl) are generated once per map (building .obj) and should all be initially generated with this script. If you add custom maps you can add their name to the line including the maps (maps = ["DoubleHotel", "ETH", "Hotel", "Univ", "Zara"]) since these are the only ones we provide.

# From the base SocNavBench directory
PYOPENGL_PLATFORM=egl PYTHONPATH='.' python3 tests/generate_traversibles.py

7. [Optional]Run the SocNavBench tests

To get you started we've included tests, which contains the main code example for testing the simulator mechanics (test_socnav.py) as well as testing multiple episodes (test_episodes.py) which can both be configured in schematic or full-render mode.

# From the base SocNavBench directory

# Ensure the unit tests succeed with 
PYTHONPATH='.' python3 tests/all_unit_tests.py 

# test the socnav simulator itself with 
# (the PYOPENGL env var is only used for the 3d renderer)
PYOPENGL_PLATFORM=egl PYTHONPATH='.' python3 tests/test_socnav.py
...
# In a separate shell (as a separate executable):
PYTHONPATH='.' python3 joystick/joystick_client.py

Now what?

To get started with using SocNavBench for your own social navigation benchmarking, see usage.md