Crocoddyl Optimal Control Library

Crocoddyl follows a specific development pattern that leverages both Python and C++:

1. Prototyping in Python: Use the Python API to quickly prototype ideas. You can derive models, costs, and constraints directly in Python. The Python API mirrors the C++ interface but uses NumPy matrices instead of Eigen objects.
2. Production in C++: Once a prototype is successful, translate it to C++ for maximum performance. The C++ interface is highly efficient and supports automatic code generation and automatic differentiation (currently only available in C++).
3. Extending: If you need to extend the library, it is preferred to do so in C++ and then bind the API so the new functionality is available in Python.

After installation, you can run examples using the Python module or via the build directory if you have compiled from source.

Running via Python: Use the -m flag to run specific examples. You can pass arguments like display or plot to enable visual outputs.

Running from Build Directory: If you have a build directory, you can run tests, examples, or benchmarks using make.

Enabling Visuals: You can enable display and/or plots by setting the following environment variables:

• CROCODDYL_DISPLAY=1
• CROCODDYL_PLOT=1

# Run via Python
python -m crocoddyl.examples.quadrupedal_gaits "display" "plot"

# Run from build directory
cd build
make test
make -s examples-quadrupedal_gaits INPUT="display plot"
make -s benchmarks-cpp-quadrupedal_gaits INPUT="100 walk"

# Using environment variables for visuals
export CROCODDYL_DISPLAY=1
export CROCODDYL_PLOT=1

On ArchLinux, you can install Crocoddyl using an AUR helper like paru.

paru -Syu crocoddyl

You can access Crocoddyl's technical documentation via Doxygen. For practical learning, it is highly recommended to explore the provided Jupyter notebooks which cover topics from basic introduction to scaling for robotics.

Recommended Notebooks:

• 01_introduction_to_crocoddyl.ipynb
• 02_optimizing_a_cartpole_swingup.ipynb
• 03_optimizing_an_unicycle.ipynb
• 04_actuating_an_acrobot.ipynb
• 05_codegenerating_a_cartpole.ipynb
• 06_scaling_to_robotics.ipynb

Crocoddyl is available in nixpkgs as crocoddyl and python3Packages.crocoddyl.

As a flake, you can:

• Run a python shell with crocoddyl: nix run github:loco-3d/crocoddyl
• Use it in your own flake by adding: crocoddyl.url = "github:loco-3d/crocoddyl";

You can install Crocoddyl using the Conda package manager from the conda-forge channel. This method supports multiple platforms.

conda install crocoddyl -c conda-forge

For Python environments, you can install Crocoddyl directly using pip.

pip install --user crocoddyl

Crocoddyl can be installed via several methods depending on your environment:

• APT packaging: Available for Ubuntu 18.04 and 20.04.
• Conda: Available via conda packages.
• Pip: Available via the Python package index.
• Source: Can be built from source on Linux and Mac OS X if binaries are not provided.

To install via robotpkg on Debian or Ubuntu, follow these steps:

1. Add the robotpkg software repository:

sudo tee /etc/apt/sources.list.d/robotpkg.list <<EOF
deb [arch=amd64] http://robotpkg.openrobots.org/packages/debian/pub $(lsb_release -sc) robotpkg
EOF

curl http://robotpkg.openrobots.org/packages/debian/robotpkg.key | sudo apt-key add -
sudo apt update

2. Install Crocoddyl and its Python bindings:

sudo apt install robotpkg-py3\*-crocoddyl

3. Configure your environment variables:

export PATH=/opt/openrobots/bin:$PATH
export PKG_CONFIG_PATH=/opt/openrobots/lib/pkgconfig:$PKG_CONFIG_PATH
export LD_LIBRARY_PATH=/opt/openrobots/lib:$LD_LIBRARY_PATH
export PYTHONPATH=/opt/openrobots/lib/python3.10/site-packages:$PYTHONPATH