Arbitration Graphs

TL;DR: Replace those rusty state machines with safe and scalable arbitration graphs!

Hierarchical behavior models for complex decision-making and behavior generation in robotics!

• 🌱 Bottom-up
  Combine simple atomic behavior components to generate complex behaviors.
• 🧩 Functional decomposition
  Behavior components address How to do it? and Can we do it?, while Arbitrators decide What to do?
  • 🧠 Meta-framework
    Integrate diverse methods in one decision-making framework. Why not combine optimization-based planning, probabilistic approaches (POMDPs), and machine learning (RL)? Use any approach where it performs best!
• 📈 Scalability
  Stack behavior components in arbitrators to create hierarchical behavior models.
• 🛠️ Maintainability
  Add new behaviors without having to touch existing ones – did we mention strict modularity and functional decomposition?
• 💡 Transparency
  Easily follow and understand the decision-making process.
• 🛡️ Behavior Verification
  Use tightly integrated verifiers to ensure that only safe and valid behavior commands are executed.
• 🪂 Graceful Degradation
  Your behavior is unreliable or unsafe? Arbitrators will gracefully fall back to the next-best option.
• 📦 Header-Only
  Simple integration – just include this header-only C++ library!
• 📜 Permissive License
  Published under MIT license to ensure maximum flexibility for your projects.

Demo

We provide a demo of this library using Pac-Man as an example application.
The arbitration graph controls Pac-Man on its journey to collect tasty dots 🍬

Run the demo with:

git clone https://github.com/KIT-MRT/arbitration_graphs.git
cd arbitration_graphs/demo
docker compose up

Open the GUI with your favorite browser:
http://0.0.0.0:8080

Explanation

You will see the Pacman Agent arbitrator selecting between five behavior options (by priority).
The Eat Dots option is itself an arbitrator with two sub-behaviors (selecting by expected benefit).

In this scene,

• the Chase Ghost and Avoid Ghost behaviors are not applicable (no ghosts in close vicinity) → grey background,
• the Eat Closest Dot and Move Randomly behavior failed verification (our verification showcase) → red background,
• thus, the least-prioritized Stay in Place behavior is being executed → green background.

Tutorial

Follow our Tutorial and learn how to use the Arbitration Graphs library!
It's based on this demo and guides you through all important concepts:

1. Introduction – start here!
2. Implement your first behavior component
3. Extend the arbitration graph with that behavior
4. Learn about nested arbitration graphs
5. Arbitrate based on predicted utility
6. Verify commands and add a fallback strategy

Installation

The arbitration_graphs library consists of two parts

• Core
  This is what it's all about – base classes for arbitrators and behavior components, implementations of various arbitration schemes, behavior verification, …
• GUI optional
  Draws a live visual representation of the arbitration state (full graph, currently active behavior, current costs of behaviors, …) in a WebApp GUI. Supports SVG export as the one above (great for publications).

Development

Contributors

This library and repo has been crafted with ❤️ by

Christoph Burger  

ChristophBurger89  

christoph-burger  

0009-0002-9147-8749
Nick Le Large  

ll-nick  

nick-le-large  

0009-0006-5191-9043
Piotr Spieker  

orzechow  

piotr-spieker  

0000-0002-0449-3741

Christoph and Piotr coded the core in a pair-programming session. Piotr also contributed the GUI and GitHub Page. Nick implemented the awesome PacMan demo and tutorial, with drafting support by Christoph, reviews and finetuning by Piotr.

The repository is maintained by Piotr Spieker 

orzechow and Nick Le Large 

ll-nick .

Citation

If you use arbitration graphs in your research, we would be pleased if you cite our work:

Piotr Spieker, Nick Le Large, and Martin Lauer, “Better Safe Than Sorry: Enhancing Arbitration Graphs for Safe and Robust Autonomous Decision-Making,” Nov. 15, 2024, arXiv: arXiv:2411.10170. doi: 10.48550/arXiv.2411.10170.

@misc{spieker2024ArbitrationGraphs,
  title={Better Safe Than Sorry: Enhancing Arbitration Graphs for Safe and Robust Autonomous Decision-Making}, 
  author={Piotr Spieker and Nick Le Large and Martin Lauer},
  year={2024},
  eprint={2411.10170},
  eprinttype = {arXiv},
  archivePrefix={arXiv},
  primaryClass={cs.RO},
  doi = {10.48550/arXiv.2411.10170},
  url={https://arxiv.org/abs/2411.10170}, 
}