[A tiny version (unofficial)]
Install MetaUrban (Tiny Version) via:
git clone -b tiny https://github.com/metadriverse/metaurban
cd metaurban
pip install -e .
download assets from
https://drive.google.com/file/d/194pgea_J7mjjlmFD4pzj3KQAWxsJtmeL/view?usp=sharing
unzip the file and organize the folder as
-metaurban
-metaurban
-assets
-assets_pedestrian
-base_class
-...
install ORCA algorithm for trajectory generation
conda install pybind11 -c conda-forge
pip install scikit-image
cd metaurban/orca_algo
rm -rf build
bash compile.sh
install torch and stable-baselines3 for RL training
pip install torch
pip install stable_baselines3
we put a small asset subset of objects and agents under the folder metaurban/assets/models/test/
and metaurban/assets_pedestrian/
. You should change paths in path_config.yaml
.
metaurbanasset: /PATH/TO/ASSETS
parentfolder: /PATH/TO/AdjustedParameters
Note that the program is tested on Linux, Windows and WSL2. Some issues in MacOS wait to be solved.
We provide examples to demonstrate features and basic usages of metaurban after the local installation.
In point navigation environment, there will be only static objects besides the ego agent in the scenario.
Run the following command to launch a simple scenario with manual control. Press W,S,A,D
to control the delivery robot.
python -m metaurban.examples.drive_in_static_env
Press key R
for loading a new scenario. If there is no response when you press W,S,A,D
, press T
to enable manual control.
In social navigation environment, there will be vehicles, pedestrians and some other agents in the scenario.
Run the following command to launch a simple scenario with manual control. Press W,S,A,D
to control the delivery robot.
python -m metaurban.examples.drive_in_dynamic_env
We provide a subset of seeds with selected ORCA reference trajectory for ego agent. You can run the command as below to generate a scenario from these seeds
python -m metaurban.scripts.generate_static_scenario
for PointNav environment
python -m metaurban.scripts.generate_dynamic_scenario
for SocialNav environment