TSID - Task Space Inverse Dynamics

TSID is a C++ library for optimization-based inverse-dynamics control based on the rigid multi-body dynamics library Pinocchio.

Documentation

Take a look at the project wiki for an overview of the design of the library.
In the exercises folder you can find several examples of how to use TSID in Python with robot manipulators, humanoids, or quadrupeds.
On the website of Andrea Del Prete you can find slides and video lessons on TSID.
Memmo 2020 summer school

Installation with Conda

If you want to directly dive into TSID in Python, only one single line is sufficient (assuming you have Conda installed):

conda install tsid -c conda-forge

Installation from Debian/Ubuntu packages, with robotpkg

If you have never added robotpkg's software repository you can do it with the following commands:

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

You can install TSID and its python bindings (replace * with you Python version) with:

sudo apt install robotpkg-py3*-tsid

Installation from sources

First you need to install the following dependencies:

boost (unit_test_framework)
eigen3
pinocchio
eiquadprog
example-robot-data (only for running the examples)

To install eigen3 on Ubuntu you can use apt-get: sudo apt-get install libeigen3-dev

To install pinocchio follow the instruction on its website.

To compile TSID:

cd $DEVEL/openrobots/src/
git clone --recursive git@github.com:stack-of-tasks/tsid.git
cd tsid
mkdir _build-RELEASE
cd _build-RELEASE
cmake .. -DCMAKE_BUILD_TYPE=RELEASE -DCMAKE_INSTALL_PREFIX=$DEVEL/openrobots
make install

Python Bindings

To use this library in python, we offer python bindings based on Boost.Python and EigenPy.

To install EigenPy you can compile the source code:

git clone https://github.com/stack-of-tasks/eigenpy

or, on Ubuntu, you can use apt-get:

sudo apt-get install robotpkg-py3*-eigenpy

For testing the python bindings, you can run the unit test scripts in the script folder, for instance:

ipython script/test_formulation.py

To run the demo using gepetto-viewer:

ipython demo/demo_romeo.py

Credits

This package is authored by:

Andrea Del Prete (University of Trento)
Justin Carpentier (INRIA)

It includes key contributions from:

Julian Viereck (Max Planck Institute, New York University)
Sanghyun Kim (Seoul National University)
Eloise Dalin (LORIA, INRIA Lorraine)
Noelie Ramuzat (LAAS, CNRS)
Pierre Fernbach (LAAS, CNRS)
Aurelie Bonnefoy (LAAS, CNRS)
Etienne Arlaud (INRIA)
Fabian Schramm (INRIA)

And is maintained by:

Guilhem Saurel (LAAS-CNRS)

Citing

If you are (or not) happy with TSID and want to cite it, please use the following citation:

@inproceedings {adelprete:jnrh:2016,
    title = {Implementing Torque Control with High-Ratio Gear Boxes and without Joint-Torque Sensors},
    booktitle = {Int. Journal of Humanoid Robotics},
    year = {2016},
    pages = {1550044},
    url = {https://hal.archives-ouvertes.fr/hal-01136936/document},
    author = {Andrea Del Prete, Nicolas Mansard, Oscar E Ramos, Olivier Stasse, Francesco Nori}
}