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

Project description

TSID - Task Space Inverse Dynamics

License Pipeline status Coverage report PyPI version Code style: black

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


  • 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] $(lsb_release -sc) robotpkg

curl | 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:

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
cd tsid
mkdir _build-RELEASE
cd _build-RELEASE
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

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/

To run the demo using gepetto-viewer:

ipython demo/


This package is authored by:

It includes key contributions from:

And is maintained by:


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 = {},
    author = {Andrea Del Prete, Nicolas Mansard, Oscar E Ramos, Olivier Stasse, Francesco Nori}

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