A fast and flexible implementation of Rigid Body Dynamics algorithms and their analytical derivatives
Project description
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)
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}
}
Project details
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SHA256 | d6092b3de800f7daba97b956edb33c172881f02704eba7d4f58c4761b55a005f |
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MD5 | 0f1010a7f0fbb1628f596d7a47b3d1b4 |
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BLAKE2b-256 | 51f1b9ab126e6c01c4c28782aad7093b5ecafc4882a94969087fc270c41cff62 |
Hashes for tsid-1.6.2.1-0-cp311-cp311-manylinux_2_17_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | d68c6178a29dbfdbba01a60609077103ad51d82d840831f48be0e7aa4015fcd9 |
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MD5 | a5415685b3cd19f41eeb93068d89d00e |
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BLAKE2b-256 | ec7e9b4cf61aa04badba0fbc0484861f81821de4cdd55ed266273d1e1f7fe56d |
Hashes for tsid-1.6.2.1-0-cp311-cp311-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5d7504e6ab47de9c39d375e7b45a834dc11f5c5bb3fe2aafb0d60aeb26dd5f71 |
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MD5 | 24c77ca33cb41fedcd42f094702b494c |
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BLAKE2b-256 | 497f30d14ea059e709b2b5bd352c02b7ad5310dc0afffbba30afa0d608498c61 |
Hashes for tsid-1.6.2.1-0-cp310-cp310-musllinux_1_1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 763a53370a11399d34a9ce3f8041f8c75629b3c743607cdded4a9534b6e96aac |
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MD5 | 3c7f20b309c7fbc78d45c757d760f3be |
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BLAKE2b-256 | d24a10a05b19bd566b10bed2a2042b4a3067ddf66921d6eb98e3032f7c5e75a2 |
Hashes for tsid-1.6.2.1-0-cp310-cp310-manylinux_2_28_aarch64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | de5e8d31d799efaa02226d66a6ac5bc55269e1455ea5c84fee9b4ddbbc96055b |
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MD5 | 94cf778ada1f08c80b98489845f7584f |
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BLAKE2b-256 | 5b6ea9bc948c17ab42e79a81c68ee7f1293161e5a7405a51f1f3f2d96db991a9 |
Hashes for tsid-1.6.2.1-0-cp310-cp310-manylinux_2_17_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b7b4331371889f4933e387da8ed82794b617401bd4cea2ed213789d2ace5a9dd |
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MD5 | 8a3f04dd51dc17b829e50abcb83b9f2e |
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BLAKE2b-256 | 0bf5ac0be71d30f3eb47125a6f881f17655081724cb262f9babc79002d814502 |
Hashes for tsid-1.6.2.1-0-cp310-cp310-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 0a160acc7b755f48b57f93c946920ff97bbb37d6c2c859cee4f61730f4400ea4 |
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MD5 | 5ce21debe0d5d520fd2339cebaef1141 |
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BLAKE2b-256 | 9f94ed22d95170ab124397bd0448b11429efd04aebffcf07dd1efe147209ce13 |
Hashes for tsid-1.6.2.1-0-cp39-cp39-musllinux_1_1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | f26156d9926d39ecf4e83972c7169dd94fb73d6c8c014b5cbd4c1f77838ebb34 |
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MD5 | 1cc4777c176d4ccfa719d61dac7b0145 |
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BLAKE2b-256 | 52efcdbb70cf484abbae0d0eed23e19382826e5ba5f7cbf32bd59619dfd4e2c9 |
Hashes for tsid-1.6.2.1-0-cp39-cp39-manylinux_2_28_aarch64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 5cbb047170cf5961868848519fa1da65c98f90ed21853bc8c39bfb0a8b7740a7 |
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MD5 | d0974bfad41dbdc5a20010d8b27e54ae |
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BLAKE2b-256 | ab8ce319965edece427e65c22de463d67e6516d873f92d49bc43613f865c7a78 |
Hashes for tsid-1.6.2.1-0-cp39-cp39-manylinux_2_17_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 11b8fe492aa5bf4c6be7e9647abe1ea246bc2ba4d23cf431f60b5955ef2d63ab |
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MD5 | 80959ba7ea6907f71203287e0e9e9952 |
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BLAKE2b-256 | 62a9436acad85105513779572f8b395cf5f5ccc6842bed52ba7d1fd93732ae12 |
Hashes for tsid-1.6.2.1-0-cp39-cp39-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 205923d8366e18f1ce06975ecb19ea5c5180fe964e63379e7a0c976472e22027 |
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MD5 | 54bde0b4b90ecf14b3a358c8cecbd17f |
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BLAKE2b-256 | 63df9c810f2db7adf3bbfa2435f284322748aa439693c298bdf31d91ac96b525 |
Hashes for tsid-1.6.2.1-0-cp38-cp38-musllinux_1_1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | b1490271211be9fb5fb50e344158ee77bbd26ce832ab45efc0d825f6109bfa1e |
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MD5 | a557c4c7fd24a15e9dcb237548819ab6 |
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BLAKE2b-256 | 4538d4b62ffb29d83c8fa4601cec64f7a68973d6ae277aeb56c5e9f8d7c71a5e |
Hashes for tsid-1.6.2.1-0-cp38-cp38-manylinux_2_28_aarch64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 666634c277319fd4902fe57fa4870095ff8e30cd982b1f610e4db84ceca1258f |
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MD5 | 80f0152b3a8408982314cae56a30637f |
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BLAKE2b-256 | 1fe361d191492b0db6f0cc49e5c317c131604ac1d53941950dc766c8f3ae1642 |
Hashes for tsid-1.6.2.1-0-cp38-cp38-manylinux_2_17_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | e04da848d0463960ac3bed9a908976f8a5428a10b35fc281746f8a415679877b |
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MD5 | fe4b1e6ab3854a13e5f37c3a293dc880 |
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BLAKE2b-256 | f8cbaee7836fb2d0dd0fde7b7f08ca1f46e8ea27f202083df9d54f50c910e08e |
Hashes for tsid-1.6.2.1-0-cp38-cp38-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 1a526f9cf3308ab4804c552d039b436ab815268bceb25372b2cc0ca9c01fa2f4 |
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MD5 | d3b14c2dff409c4b3414004ef8031eef |
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BLAKE2b-256 | 8f13c05b4220e7aead1afd5b80d5ae73469ce4af761762f5b558688186077670 |
Hashes for tsid-1.6.2.1-0-cp37-cp37m-musllinux_1_1_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | ba13382ac522434cc0e632ab5ab8abf339a907862be94ecee11ef8e81acb9bed |
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MD5 | d1a0f31a29f833a69ea256cffba8de38 |
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BLAKE2b-256 | 4937c9e9501ae40288b12d3c775bccfe105aa49ecc1eb293dc12e9d0dd6f456c |
Hashes for tsid-1.6.2.1-0-cp37-cp37m-manylinux_2_28_aarch64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | f19ebdfcbe6f455e7d10cc40a2f5f670c6a68f73b3c21251512beed3a21672e4 |
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MD5 | 1fbee65b092a139e689f70ab64fcaa9f |
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BLAKE2b-256 | 4b9bc9333b06da8b561d14e7a928cfc754c9796e3c93ada00cb91db77204b02e |
Hashes for tsid-1.6.2.1-0-cp37-cp37m-manylinux_2_17_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | 24937363f79816a27fb96d7a62244e193538f88a66bb081ad6df90917bef8c62 |
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MD5 | 09bc8847c7bb8f38dd5b03f01780cad1 |
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BLAKE2b-256 | 891605a0ce9cbf155f2e61a92c220fb3c0e1e1e098898c11b7badd7542ee7a1c |
Hashes for tsid-1.6.2.1-0-cp37-cp37m-macosx_10_16_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | a435ad46474929d50e1f11333762984a0e6a058dee6bd3aaec470826f7b76023 |
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MD5 | 2efcf7b2b5713ae1a6c4bf358775e7ab |
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BLAKE2b-256 | af5b7961eb97002ddedac6249c73256032e04e1e6101a1a42495655f6edf079a |