Controller Modularity in Brittle-Star Robots
https://selab-3-2026.github.io/SEL3-2026-Groep-4/
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| .devcontainer | ||
| .github/workflows | ||
| .vscode | ||
| configs | ||
| docs | ||
| env/hpc | ||
| scripts | ||
| src | ||
| tests | ||
| .commitlintrc.json | ||
| .env.example | ||
| .gitattributes | ||
| .gitignore | ||
| .pre-commit-config.yaml | ||
| .python-version | ||
| flake.lock | ||
| flake.nix | ||
| pyproject.toml | ||
| README.md | ||
| ruff.toml | ||
| uv.lock | ||
Brittle Star
What is the impact of different levels of controller-modularity on the learning-speed, coordination and tolerance for defects (e.g. amputations) in brittle-star-like robots trained with Reinforcement Learning?
Quick start
Local setup
To set up the UV module, you can run the following command:
uv sync --frozen
Usage
For detailed instructions on how to use the project, please refer to the API Documentation.
Quick Start
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Train a model:
uv run python scripts/train.py ppo.learning_rate=0.001 logging.track=true -
Monitor progress: See Tracking & Monitoring.
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Simulate a trained model: See Simulation & Evaluation.
HPC
See docs/HPC.md for the full guide, including environment setup, cluster selection, interactive debugging, and job submission.
Documentation
Please find all documentation and a starting point for more information in corresponding README.