Aligator

Aligator is an efficient and versatile trajectory optimization library for robotics and beyond.

It can be used for motion generation and planning, optimal estimation, deployment of model-predictive control on complex systems, and much more.

Features

Aligator is a C++ template library, which provides

• a modeling interface for optimal control problems, node-per-node
• a set of efficient solvers for constrained trajectory optimization
• support for the pinocchio rigid-body dynamics library and its analytical derivatives
• an interface to the Crocoddyl trajectory optimization library which can be used as an alternative frontend
• Python bindings leveraging eigenpy

Aligator provides efficient implementations of the following algorithms for (constrained) trajectory optimization:

• ProxDDP: Proximal Differentiable Dynamic Programming, detailed in this paper
• FeasibleDDP: Feasible Differentiable Dynamic Programming, detailed in this paper

Installation

From Conda

From our channel.

conda install -c simple-robotics aligator

Build from source

git clone https://github.com/Simple-Robotics/aligator --recursive
cmake -DCMAKE_INSTALL_PREFIX=your_install_folder -S . -B build/ && cd build/
cmake --build . -jNCPUS

Dependencies

• proxsuite-nlp
• Eigen3 >= 3.3.7
• Boost >= 1.71.0
• (optional) eigenpy>=3.1.0 | conda (Python bindings)
• (optional) Pinocchio | conda
• (optional) Crocoddyl | conda
• (optional) example-robot-data | conda (required for some examples and benchmarks)
• a C++14 compliant compiler

Python dependencies

• typed-argument-parser
• matplotlib

Notes

• For developers, add the -DCMAKE_EXPORT_COMPILE_COMMANDS=1 when working with language servers e.g. clangd.
• To use the Crocoddyl interface, add -DBUILD_CROCODDYL_COMPAT=ON
• By default, building the library will instantiate the templates for the double scalar type.
• To build against a Conda environment, activate the environment and run export CMAKE_PREFIX_PATH=$CONDA_PREFIX before running CMake and use $CONDA_PREFIX as your install folder.

Benchmarking

We recommend using Flame Graphs to evaluate performance. If you have the Rust toolchain and cargo installed, we suggest you install cargo-flamegraph. Then, you can create a flame graph with the following command:

flamegraph -o my_flamegraph.svg -- ./build/examples/example-croc-talos-arm

Contributors

• Antoine Bambade (Inria): mathematics and algorithms developer
• Justin Carpentier (Inria): project instructor
• Wilson Jallet (LAAS-CNRS/Inria): main developer and manager of the project
• Sarah Kazdadi: linear algebra czar
• Quentin Le Lidec (Inria): feature developer
• Joris Vaillant (Inria): core developer
• Nicolas Mansard (LAAS-CNRS): project coordinator
• Guilhem Saurel (LAAS-CNRS): core maintener
• Fabian Schramm (Inria): core developer
• Ludovic De Matteïs (LAAS-CNRS/Inria): feature developer

Acknowledgments

The development of Aligator is actively supported by the Willow team @INRIA and the Gepetto team @LAAS-CNRS.