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HTML
1077 lines
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25 KiB
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<title>Contribution Guidelines - Brittle Star Project</title>
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Brittle Star Project
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<span class="md-ellipsis">
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Contribution Guidelines
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Initializing search
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Brittle Star Project
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Home
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<span class="md-ellipsis">
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Design & Architecture
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</span>
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<span class="md-nav__icon md-icon"></span>
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Design & Architecture
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</label>
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<ul class="md-nav__list" data-md-scrollfix>
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<li class="md-nav__item">
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<a href="../design/actor-critic/" class="md-nav__link">
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<span class="md-ellipsis">
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Actor-Critic Architecture
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</span>
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</a>
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<li class="md-nav__item">
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<a href="../design/communication/" class="md-nav__link">
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<span class="md-ellipsis">
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Communication Scheme
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</span>
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</a>
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<li class="md-nav__item">
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<a href="../design/controllers/" class="md-nav__link">
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<span class="md-ellipsis">
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Modularity & Topology
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</span>
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</a>
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<li class="md-nav__item">
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<a href="../design/input_action_spaces/" class="md-nav__link">
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<span class="md-ellipsis">
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Input & Action Spaces
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</span>
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</a>
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<li class="md-nav__item">
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<a href="../design/learning_algorithm/" class="md-nav__link">
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<span class="md-ellipsis">
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Reinforcement Learning Algorithm
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</span>
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</a>
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</li>
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<li class="md-nav__item">
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<a href="../design/reward_function/" class="md-nav__link">
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<span class="md-ellipsis">
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Reward Function & Observation Space
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</span>
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</a>
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</li>
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</ul>
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</nav>
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</li>
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<li class="md-nav__item md-nav__item--nested">
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<input class="md-nav__toggle md-toggle " type="checkbox" id="__nav_3" >
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<label class="md-nav__link" for="__nav_3" id="__nav_3_label" tabindex="0">
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<span class="md-ellipsis">
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API Reference
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</span>
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<span class="md-nav__icon md-icon"></span>
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</label>
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<nav class="md-nav" data-md-level="1" aria-labelledby="__nav_3_label" aria-expanded="false">
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<label class="md-nav__title" for="__nav_3">
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<span class="md-nav__icon md-icon"></span>
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API Reference
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</label>
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<ul class="md-nav__list" data-md-scrollfix>
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<li class="md-nav__item">
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<a href="../api/environment/" class="md-nav__link">
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<span class="md-ellipsis">
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Brittle Star Environment
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</span>
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</a>
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</li>
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<li class="md-nav__item">
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<a href="../api/training/" class="md-nav__link">
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<span class="md-ellipsis">
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Training Models
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</span>
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</a>
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<li class="md-nav__item">
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<a href="../api/tracking/" class="md-nav__link">
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<span class="md-ellipsis">
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Tracking & Monitoring
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</span>
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</a>
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<li class="md-nav__item">
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<a href="../api/evaluation/" class="md-nav__link">
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<span class="md-ellipsis">
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Checkpoint & Model Evaluation
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</span>
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</a>
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<li class="md-nav__item">
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<a href="../api/simulation/" class="md-nav__link">
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<span class="md-ellipsis">
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Interactive Simulation & Visualization
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</span>
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</a>
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<li class="md-nav__item">
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<a href="../api/analysis/" class="md-nav__link">
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<span class="md-ellipsis">
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Analysis & Plotting Tools
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</span>
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</a>
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<li class="md-nav__item">
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<a href="../api/reproduction/" class="md-nav__link">
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<span class="md-ellipsis">
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Results & Reproduction
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</span>
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</a>
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</li>
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</ul>
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</nav>
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<h1 id="contribution-guidelines">Contribution Guidelines</h1>
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<p>This document outlines the contribution protocols for the scientific software engineering project focusing on bio-inspired control architectures for brittle-star-like robots. The primary objective of this project is to produce scientific insight, rather than a commercial product.</p>
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<h2 id="1-scientific-context-methodology">1. Scientific Context & Methodology</h2>
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<ul>
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<li><strong>Research Focus:</strong> The goal is to study how controller modularity affects learning speed, coordination, and fault tolerance in brittle-star locomotion.</li>
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<li><strong>Hypothesis-Driven Design:</strong> Clear hypotheses must dictate a structured methodology and rigorous evaluation. All design decisions must be formally documented prior to implementation.</li>
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<li><strong>Scaffolding Approach:</strong> Development must start with simple setups before progressively increasing the complexity of environments and morphologies.</li>
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<li><strong>Evaluation of Results:</strong> Negative results possess scientific validity when thoroughly analyzed. If a controller fails to learn locomotion, providing a comprehensive analysis of the failure is considered a strong scientific contribution.</li>
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<li><strong>Reproducibility:</strong> Contributors must utilize fixed library versions. Configuration systems (such as json, gin, or yaml) must be employed to ensure reproducible runs.</li>
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</ul>
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<h2 id="2-clean-code-code-quality">2. Clean Code & Code Quality</h2>
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<p>Code readability is paramount, as code is read far more frequently than it is written.</p>
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<ul>
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<li><strong>Naming Conventions:</strong> Variables and functions must utilize consistent, intention-revealing names. A long, descriptive name is strictly preferred over a short name accompanied by a comment.</li>
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<li><strong>Function Design:</strong> Functions must be modular and adhere to the single responsibility principle. Arguments must be minimized, and boolean flag arguments controlling behavior should be avoided.</li>
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<li><strong>Commenting:</strong> Code must document the "how," while comments are strictly reserved for documenting the "why". Commented-out code is prohibited and must be deleted via version control.</li>
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<li><strong>YAGNI:</strong> Contributors must adhere to the "You Aren't Gonna Need It" (YAGNI) principle and actively avoid premature optimization.</li>
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<li><strong>Notebooks:</strong> Jupyter Notebooks are strictly limited to quick prototyping, tutorials, demonstrations, or post-processing analysis. They are explicitly forbidden for general software development because they discourage modularity.</li>
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</ul>
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<h2 id="3-version-control-repository-structure">3. Version Control & Repository Structure</h2>
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<ul>
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<li><strong>Git Practices:</strong> Commits must be frequent and small. Each commit should relate to exactly one piece of functionality.</li>
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<li><strong>Branching Strategy:</strong> The <code>dev</code> branch serves as the integration branch for pushing and merging code. Only stable releases may be pushed to the <code>main</code> branch.</li>
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<li><strong>Artifact Management:</strong> Data files, trained models, and large datasets must never be committed directly to Git. Git Large File Storage (LFS) must be used for tracking large files. <strong>All developers must have <code>git-lfs</code> installed locally</strong> (see <a href="../DEVELOPMENT/">DEVELOPMENT.md</a> for setup).</li>
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<li><strong>Repository Layout:</strong> The repository must maintain the following core directories: <code>src/</code> for algorithms, <code>env/</code> for MuJoCo wrappers, <code>config/</code> for experiment configurations, <code>experiments/</code> for scripts, <code>docs/</code> for Doxygen or ReadTheDocs documentation, and <code>tests/</code> for unit tests.</li>
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</ul>
|
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<h2 id="4-architecture-tooling">4. Architecture & Tooling</h2>
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<ul>
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<li><strong>Algorithms & Frameworks:</strong> Proximal Policy Optimization (PPO) is the recommended baseline algorithm. CleanRL should be used as a starting point and adapted for continuous action spaces. All Artificial Neural Network (ANN) controller architectures must be implemented using Flax.</li>
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<li><strong>Simulation:</strong> The simulation environment utilizes a MuJoCo brittle star. XML MuJoCo structures must remain realistic and respect morphological constraints.</li>
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<li><strong>Experiment Tracking:</strong> Weights & Biases (wandb) must be utilized for tracking and logging all experiments.</li>
|
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<li><strong>Code Styling:</strong> All code must conform to the chosen style guide (Google standard). This is enforced via <code>uv</code> using <strong>ruff</strong> and pre-commit hooks.</li>
|
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</ul>
|
|
<h2 id="5-ai-assisted-development-code-review">5. AI-Assisted Development & Code Review</h2>
|
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<p>This project supports AI-assisted development to enhance productivity, but contributors must take full responsibility for all AI-generated outputs.</p>
|
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<ul>
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<li><strong>Self-Review Requirement:</strong> Contributors must thoroughly self-review all AI-assisted code, documentation, and configurations before requesting peer review. This includes verifying correctness, adherence to project standards, scientific validity, and integration with existing code.</li>
|
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<li><strong>Quality Standards:</strong> AI-generated content must meet the same rigorous standards as manually written code, including proper testing, documentation, and alignment with the scientific methodology outlined in Section 1.</li>
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<li><strong>Available Skills:</strong> This project provides specific AI skills for common tasks (located in <code>.agents/skills/</code>), including linting and testing workflows. Contributors should leverage these skills to maintain consistency and quality.</li>
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<li><strong>Transparency:</strong> When using AI assistance for complex algorithmic decisions or scientific design choices, contributors should document the rationale in commit messages or code comments where appropriate.</li>
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</ul>
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