robot-sf

2024-03-28 Under Development. See https://github.com/ll7/robot_sf_ll7/issues.

About

This project provides a training environment for the simulation of a robot moving in a pedestrian-filled space.

The project interfaces with Faram Foundations "Gymnasium" (former OpenAI Gym) to facilitate trainings with various SOTA reinforcement learning algorithms like e.g. StableBaselines3. For simulating the pedestrians, the SocialForce model is used via a dependency on a fork of PySocialForce.

Following video outlines some training results where a robot with e-scooter kinematics is driving at the campus of University of Augsburg using real map data from OpenStreetMap.

Installation

This project currently supports two methods for installation:

1. .devcontainer for Visual Studio Code .devcontainer/readme.md
2. local installation

For 1. follow the instructions in the .devcontainer directory. For 2. follow the instructions below:

Local Installation

Install python >= 3.10 and <= 3.12.

sudo apt-get update && sudo apt-get install -y python3.12 python3-pip
pip install virtualenv

Quickstart

1. Clone Source Code

git clone --recurse-submodules https://github.com/Bonifatius94/robot-sf
cd robot-sf

2. Install Dependencies

python3 -m pip install pip --upgrade
python3 -m pip install -r requirements.txt
python3 -m pip install -r fast-pysf/requirements.txt

3. Register robot_sf and pysocialforce Packages

pushd ./fast-pysf
    python3 -m pip install .
popd
python3 -m pip install .

Note: This needs to be repeated to propagate robot_sf changes on a host deployment. Otherwise the calling scope might run an old version of robot_sf. Dockerized deployments will recognize when a change requires a rebuild.

Alternatively create soft links for fast-pysf. The modules and tests are automatically detected by the interpreter if the calling scope is located at the repository's root directory which is very useful e.g. for debugging.

ln -s fast-pysf/pysocialforce pysocialforce
pushd tests
    # ln -s ../fast-pysf/tests pysf_tests
popd

Note: The outlined command might differ on Windows, e.g. try mklink

4. Run Linter / Tests

python3 -m pytest tests
python3 -m pylint robot_sf

5. Run Visual Debugging of Pre-Trained Demo Models

python3 examples/demo_offensive.py
python3 examples/demo_defensive.py

Visualization

6. Run StableBaselines Training (Docker)

docker compose build && docker compose run \
    robotsf-cuda python ./scripts/training_ppo.py

Note: See this setup to install Docker with GPU support.

Older versions use docker-compose instead of docker compose.

7. Edit Maps

The preferred way to create maps: SVG Editor

This Method is deprecated:

sudo apt-get update && sudo apt-get install -y python3-tk

python3 -m map_editor

Note: See this documentation on how to use the map editor.

8. Optimize Training Hyperparams (Docker)

docker-compose build && docker-compose run \
    robotsf-cuda python ./scripts/hparam_opt.py

9. Extension: Pedestrian as Adversarial-Agent

The pedestrian is an adversarial agent who tries to find weak points in the vehicle's policy.

The Environment is built according to gymnasium rules, so that multiple RL algorithms can be used to train the pedestrian.

It is important to know that the pedestrian always spawns near the robot.

python3 examples/demo_pedestrian.py

Visualization