FC Portugal Codebase
for RoboCup 3D Soccer Simulation League

About

The FC Portugal Codebase was mainly written in Python, with some C++ modules. It was created to simplify and speed up the development of a team for participating in the RoboCup 3D Soccer Simulation League. We hope this release helps existing teams transition to Python more easily, and provides new teams with a robust and modern foundation upon which they can build new features.

Documentation

The documentation is available here

Features

• The team is ready to play!
  • Sample Agent - the active agent attempts to score with a kick, while the others maintain a basic formation
    • Launch team with: start.sh
  • Sample Agent supports Fat Proxy
    • Launch team with: start_fat_proxy.sh
  • Sample Agent Penalty - a striker performs a basic kick and a goalkeeper dives to defend
    • Launch team with: start_penalty.sh
• Skills
  • Get Ups (latest version)
  • Walk (latest version)
  • Dribble v1 (version used in RoboCup 2022)
  • Step (skill-set-primitive used by Walk and Dribble)
  • Basic kick
  • Basic goalkeeper dive
• Features
  • Accurate localization based on probabilistic 6D pose estimation algorithm and IMU
  • Automatic head orientation
  • Automatic communication with teammates to share location of all visible players and ball
  • Basics: common math ops, server communication, RoboViz drawings (with arrows and preset colors)
  • Behavior manager that internally resets skills when necessary
  • Bundle script to generate a binary and the corresponding start/kill scripts
  • C++ modules are automatically built into shared libraries when changes are detected
  • Central arguments specification for all scripts
  • Custom A* pathfinding implementation in C++, optimized for the soccer environment
  • Easy integration of neural-network-based behaviors
  • Integration with Open AI Gym to train models with reinforcement learning
    • User interface to train, retrain, test & export trained models
    • Common features from Stable Baselines were automated, added evaluation graphs in the terminal
    • Interactive FPS control during model testing, along with logging of statistics
  • Interactive demonstrations, tests and utilities showcasing key features of the team/agents
  • Inverse Kinematics
  • Multiple agents can be launched on a single thread, or one agent per thread
  • Predictor for rolling ball position and velocity
  • Relative/absolute position & orientation of every body part & joint through forward kinematics and vision
  • Sample train environments
  • User-friendly interface to check active arguments and launch utilities & gyms

Citing the Project

@article{abreu2023designing,
  title={Designing a Skilled Soccer Team for RoboCup: Exploring Skill-Set-Primitives through Reinforcement Learning},
  author={Abreu, Miguel and Reis, Luis Paulo and Lau, Nuno},
  journal={arXiv preprint arXiv:2312.14360},
  year={2023}
}