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52 lines
2.9 KiB
Markdown
52 lines
2.9 KiB
Markdown
# FC Portugal Codebase <br> for RoboCup 3D Soccer Simulation League
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## About
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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.
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## Documentation
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The documentation is available [here](https://docs.google.com/document/d/1aJhwK2iJtU-ri_2JOB8iYvxzbPskJ8kbk_4rb3IK3yc/edit)
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## Features
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- The team is ready to play!
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- Sample Agent - the active agent attempts to score with a kick, while the others maintain a basic formation
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- Launch team with: **start.sh**
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- Sample Agent supports [Fat Proxy](https://github.com/magmaOffenburg/magmaFatProxy)
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- Launch team with: **start_fat_proxy.sh**
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- Sample Agent Penalty - a striker performs a basic kick and a goalkeeper dives to defend
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- Launch team with: **start_penalty.sh**
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- Skills
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- Get Ups (latest version)
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- Walk (latest version)
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- Dribble v1 (version used in RoboCup 2022)
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- Step (skill-set-primitive used by Walk and Dribble)
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- Basic kick
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- Basic goalkeeper dive
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- Features
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- Accurate localization based on probabilistic 6D pose estimation [algorithm](https://doi.org/10.1007/s10846-021-01385-3) and IMU
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- Automatic head orientation
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- Automatic communication with teammates to share location of all visible players and ball
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- Basics: common math ops, server communication, RoboViz drawings (with arrows and preset colors)
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- Behavior manager that internally resets skills when necessary
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- Bundle script to generate a binary and the corresponding start/kill scripts
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- C++ modules are automatically built into shared libraries when changes are detected
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- Central arguments specification for all scripts
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- Custom A* pathfinding implementation in C++, optimized for the soccer environment
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- Easy integration of neural-network-based behaviors
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- Integration with Open AI Gym to train models with reinforcement learning
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- User interface to train, retrain, test & export trained models
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- Common features from Stable Baselines were automated, added evaluation graphs in the terminal
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- Interactive FPS control during model testing, along with logging of statistics
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- Interactive demonstrations, tests and utilities showcasing key features of the team/agents
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- Inverse Kinematics
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- Multiple agents can be launched on a single thread, or one agent per thread
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- Predictor for rolling ball position and velocity
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- Relative/absolute position & orientation of every body part & joint through forward kinematics and vision
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- Sample train environments
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- User-friendly interface to check active arguments and launch utilities & gyms
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