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‎.github/workflows/ci.yml‎

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name: CI
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on:
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push:
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branches: [ main ]
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pull_request:
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branches: [ main ]
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permissions:
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contents: read
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pages: write
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id-token: write
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jobs:
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test:
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runs-on: ubuntu-latest
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strategy:
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matrix:
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python-version: ["3.11", "3.12", "3.13"]
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env:
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TERM: xterm-256color
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FORCE_COLOR: 1
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steps:
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- uses: actions/checkout@v3
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- name: Set up Python ${{ matrix.python-version }}
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uses: actions/setup-python@v4
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with:
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python-version: ${{ matrix.python-version }}
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install .[tests,docs]
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- name: Check codestyle
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run: make check-codestyle
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- name: Check spelling
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run: make spelling
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- name: Test with pytest
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run: make pytest
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- name: Build docs
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run: make doc
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deploy:
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runs-on: ubuntu-latest
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needs: test
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if: needs.test.result == 'success'
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env:
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TERM: xterm-256color
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FORCE_COLOR: 1
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steps:
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- uses: actions/checkout@v3
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- name: Set up Python 3.13
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uses: actions/setup-python@v4
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with:
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python-version: 3.13
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- name: Install dependencies
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run: |
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python -m pip install --upgrade pip
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pip install .[tests,docs]
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- name: Build docs
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run: make doc
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- name: Setup Pages
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uses: actions/configure-pages@v5
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- name: Upload artifact
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uses: actions/upload-pages-artifact@v3
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with:
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path: './docs/_build/html'
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- name: Deploy to GitHub Pages
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id: deployment
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uses: actions/deploy-pages@v4
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‎.gitignore‎

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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[codz]
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*$py.class
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# C extensions
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*.so
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# Distribution / packaging
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.Python
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build/
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develop-eggs/
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dist/
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downloads/
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eggs/
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.eggs/
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lib/
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lib64/
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parts/
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sdist/
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var/
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wheels/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py.cover
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.hypothesis/
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.pytest_cache/
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cover/
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
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instance/
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.webassets-cache
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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_build/
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# PyBuilder
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.pybuilder/
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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# pipenv
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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# UV
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# Similar to Pipfile.lock, it is generally recommended to include uv.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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#uv.lock
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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#poetry.toml
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# pdm
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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# pdm recommends including project-wide configuration in pdm.toml, but excluding .pdm-python.
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# https://pdm-project.org/en/latest/usage/project/#working-with-version-control
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#pdm.lock
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#pdm.toml
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.pdm-python
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.pdm-build/
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# pixi
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# Similar to Pipfile.lock, it is generally recommended to include pixi.lock in version control.
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#pixi.lock
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# Pixi creates a virtual environment in the .pixi directory, just like venv module creates one
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# in the .venv directory. It is recommended not to include this directory in version control.
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.pixi
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.envrc
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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# Abstra
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# Abstra is an AI-powered process automation framework.
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# Ignore directories containing user credentials, local state, and settings.
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# Learn more at https://abstra.io/docs
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.abstra/
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# Visual Studio Code
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# Visual Studio Code specific template is maintained in a separate VisualStudioCode.gitignore
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# that can be found at https://github.com/github/gitignore/blob/main/Global/VisualStudioCode.gitignore
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# and can be added to the global gitignore or merged into this file. However, if you prefer,
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# you could uncomment the following to ignore the entire vscode folder
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# .vscode/
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# Ruff stuff:
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.ruff_cache/
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# PyPI configuration file
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.pypirc
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# Cursor
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# Cursor is an AI-powered code editor. `.cursorignore` specifies files/directories to
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# exclude from AI features like autocomplete and code analysis. Recommended for sensitive data
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# refer to https://docs.cursor.com/context/ignore-files
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.cursorignore
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.cursorindexingignore
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# Marimo
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marimo/_static/
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marimo/_lsp/
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__marimo__/
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docs/stl_list.rst

‎Makefile‎

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SHELL=/bin/bash
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PATHS=magbotsim/ tests/ setup.py
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pytest:
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python3 -m pytest -v --tb=short
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format:
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ruff format ${PATHS}
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check-codestyle:
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ruff check ${PATHS}
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spelling:
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cd docs && make spelling
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doc:
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cd docs && make html
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clean:
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cd docs && make clean
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commit: format check-codestyle spelling pytest doc

‎README.md‎

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# MagBotSim (Magnetic Robotics Simulation)
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<img src="https://github.com/ubi-coro/MagBotSim/raw/main/docs/images/visual_abstract.png" />
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MagBotSim is a library for physics-based simulation environments for motion planning
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and object manipulation in the field of magnetic robotics. The main component of every environment is a
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*magnetic levitation (MagLev)* system, which consists of two basic components, as shown in the Figure above. Firstly,
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dynamically actuated shuttles as passive motor modules, so-called *movers*, consist of a housing and a complex permanent
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magnet structure based on Halbach arrays on the lower side of the mover. Secondly, static motor modules, so-called *tiles*,
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are the active component of the drive system. As shown in the Figure above, the tiles enable the coil-induced emission
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of electromagnetic fields (yellow) that interact with the mover's field (blue). During operation, the movers hover above the
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tiles and can be controlled in six dimensions by adjusting the currents in the coils contained in the tiles.
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MagBotSim is designed to match real-world applications, so that control policies can be transferred to real MagLev systems without
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further training or calibration. Since the library is based on the [MuJoCo](https://mujoco.readthedocs.io/en/stable/overview.html)
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physics engine, MagBotSim enables users to perform object manipulation tasks with MagLev systems. In addition, as reinforcement
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learning (RL) is frequently used in multi-agent path finding and object manipulation, MagBotSim includes basic environments with
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single-agent ([Gymnasium](https://gymnasium.farama.org/)) and multi-agent ([PettingZoo Parallel API](https://pettingzoo.farama.org/api/parallel/)) RL
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APIs that can serve as starting points for developing new research-specific environments. However, MagBotSim can also be
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used without RL and provides several utilities, such as impedance control for the movers.
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## Installation
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The MagBotSim package can be installed via PIP:
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```
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pip install magbotsim
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```
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To install optional dependencies, to build the documentation, or to run the tests, use:
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```
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pip install magbotsim[docs, tests]
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```
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**Note:** Depending on your shell (e.g. when using Zsh), you may need to use additional quotation marks:
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```
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pip install "magbotsim[docs, tests]"
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```
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## Documentation
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The documentation is available at: https://ubi-coro.github.io/MagBotSim/
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## License
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MagBotSim is published under the GNU General Public License v3.0.
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## Reinforcement Learning Example
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The following example shows how to use a trained policy with an environment that follows the Gymnasium API:
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```python
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import gymnasium as gym
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import magbotsim
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gym.register_envs(magbotsim)
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mover_params = {
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'shape': 'mesh',
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'mesh': {'mover_stl_path': 'beckhoff_apm4220_mover', 'bumper_stl_path': 'beckhoff_apm4220_bumper'},
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'mass': 0.639 - 0.034,
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'bumper_mass': 0.034,
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}
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env_kwargs = {
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'mover_params': mover_params,
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'initial_mover_zpos': 0.002,
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'render_mode': 'human',
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'render_every_cycle': True,
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}
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env = gym.make("StateBasedPushTEnv-v0", **env_kwargs)
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observation, info = env.reset(seed=42)
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for _ in range(0,100):
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terminated, truncated = False, False
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while not terminated and not truncated:
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action = env.action_space.sample() # use custom policy instead
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observation, reward, terminated, truncated, info = env.step(action)
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observation, info = env.reset()
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env.close()
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```
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## Maintainer
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MagBotSim is currently maintained by Lara Bergmann (@lbergmann1).

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