• 📈 Scalability
  Stack behavior components in arbitrators to create hierarchical behavior models.
• 💡 Transparency
  Easily follow and understand the decision-making process, e.g., with our GUI.
• 📦 Header-Only
  Simple integration – just include this header-only C++17 library!
• 🐍 Python Bindings
  For easy prototyping, testing, and integration of machine learning algorithms, all the functionality is available via Python bindings.
• 📜 Permissive License
  Published under MIT license to ensure maximum flexibility for your projects.

Behavior Trees (BTs) are great for a variety of applications and thrive within a vibrant community!
Kudos to Petter Ögren's crew, Michele Colledanchise and Davide Faconti 🖖

But, Arbitration Graphs bring great value, especially for safety critical applications like self-driving cars and mobile robots in general – by strictly coupling preconditions to behaviors and tightly integrating behavior verification. A bit more in detail:

First make sure all dependencies are installed:

• The arbitration graph core library (see ../README.md)
• Python 3.8 or higher
• pybind11 >= 3.0.0
• ninja

See also the Dockerfile for how to install these packages under Debian or Ubuntu.

# From local repository
git clone https://github.com/KIT-MRT/arbitration_graphs.git
cd arbitration_graphs/python_bindings
pip install .

# Or directly from GitHub
pip install git+https://github.com/KIT-MRT/arbitration_graphs.git#subdirectory=python_bindings

Clone the repository and build the package using CMake:

mkdir -p arbitration_graphs/python_bindings/build
cd arbitration_graphs/python_bindings/build
# Point to the pybind11 CMake configuration directory, if not installed globally (e.g., via pip in a virtual environment)
cmake -Dpybind11_DIR=$(pybind11-config --cmakedir) ..
cmake --build .

Clone the repository and run the development image

git clone https://github.com/KIT-MRT/arbitration_graphs.git
cd arbitration_graphs/python_bindings
docker compose build arbitration_graphs_pybind_devel
docker compose run --rm arbitration_graphs_pybind_devel

This mounts the source into the container's /home/blinky/arbitration_graphs folder. There, you can edit the source code, compile and run the tests etc.

This package includes unit tests analogous to the C++ tests. To run all tests, use the following command:

cd arbitration_graphs/python_bindings
python -m unittest discover

Behavior Verification and Fallback Layers

A safety concept that extends Arbitration Graphs with behavior verification and fallback layers in the context of automated driving has been proposed by Piotr Spieker (née Orzechowski) in his PhD thesis. This served as the basis for the paper with Nick above.

Piotr F. Orzechowski, “Verhaltensentscheidung für automatisierte Fahrzeuge mittels Arbitrationsgraphen,” phd, Karlsruher Institut für Technologie (KIT), 2023. doi: 10.5445/IR/1000160638.

@thesis{Orzechowski2023Arbitrationsgraphen,
  type = {phdthesis},
  title = {Verhaltensentscheidung für automatisierte Fahrzeuge mittels Arbitrationsgraphen},
  author = {Orzechowski, Piotr Franciszek},
  date = {2023},
  institution = {Karlsruher Institut für Technologie (KIT)},
  doi = {10.5445/IR/1000160638},
  langid = {german},
  pagetotal = {169},
}

Replacing state machines in AV

Arbitration Graphs replaced state machines in the context of automated driving at the Institute of Measurement and Control Systems (MRT) of the Karlsruhe Institute of Technology (KIT):

Piotr F. Orzechowski, Christoph Burger, and Martin Lauer, “Decision-Making for Automated Vehicles Using a Hierarchical Behavior-Based Arbitration Scheme,” in Intelligent Vehicles Symposium, Las Vegas, NV, USA: IEEE, Oct. 2020, pp. 767–774. doi: 10.1109/IV47402.2020.9304723.

@inproceedings{orzechowski2020ArbitrationGraphs,
  title = {Decision-Making for Automated Vehicles Using a Hierarchical Behavior-Based Arbitration Scheme},
  booktitle = {Intelligent Vehicles Symposium},
  author = {Orzechowski, Piotr F. and Burger, Christoph and Lauer, Martin},
  date = {2020-10},
  pages = {767--774},
  publisher = {IEEE},
  location = {Las Vegas, NV, USA},
  issn = {2642-7214},
  doi = {10.1109/IV47402.2020.9304723},
}

Foundation work in Robot Soccer

The foundations for Arbitration Graphs have been proposed in the context of robot soccer:

Martin Lauer, Roland Hafner, Sascha Lange, and Martin Riedmiller, “Cognitive concepts in autonomous soccer playing robots,” Cognitive Systems Research, vol. 11, no. 3, pp. 287–309, 2010, doi: 10.1016/j.cogsys.2009.12.003.

@article{lauer2010CognitiveConceptsAutonomous,
  title = {Cognitive Concepts in Autonomous Soccer Playing Robots},
  author = {Lauer, Martin and Hafner, Roland and Lange, Sascha and Riedmiller, Martin},
  date = {2010},
  journaltitle = {Cognitive Systems Research},
  volume = {11},
  number = {3},
  pages = {287--309},
  doi = {10.1016/j.cogsys.2009.12.003},
}