libtsid 1.10.0

A fast and flexible implementation of Rigid Body Dynamics algorithms and their analytical derivatives

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 $DEVEL/openrobots/src/
git clone --recursive [e-mail]:stack-of-tasks/tsid.git
cd tsid
mkdir _build-RELEASE
cd _build-RELEASE
cmake .. -DCMAKE_BUILD_TYPE=RELEASE -DCMAKE_INSTALL_PREFIX=$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}
}