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README.md

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# TSID - Task Space Inverse Dynamics
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[![Building Status](https://travis-ci.org/stack-of-tasks/tsid.svg?branch=master)](https://travis-ci.org/stack-of-tasks/tsid)
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[![Pipeline status](https://gepgitlab.laas.fr/stack-of-tasks/tsid/badges/master/pipeline.svg)](https://gepgitlab.laas.fr/stack-of-tasks/tsid/commits/master)
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[![Coverage report](https://gepgitlab.laas.fr/stack-of-tasks/tsid/badges/master/coverage.svg?job=doc-coverage)](http://projects.laas.fr/gepetto/doc/stack-of-tasks/tsid/master/coverage/)
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[![Pipeline status](https://gitlab.laas.fr/stack-of-tasks/tsid/badges/master/pipeline.svg)](https://gitlab.laas.fr/stack-of-tasks/tsid/commits/master)
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[![Coverage report](https://gitlab.laas.fr/stack-of-tasks/tsid/badges/master/coverage.svg?job=doc-coverage)](http://projects.laas.fr/gepetto/doc/stack-of-tasks/tsid/master/coverage/)
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TSID is C++ library for optimization-based inverse-dynamics control based on the rigid multi-body dynamics library [Pinocchio](https://github.com/stack-of-tasks/pinocchio).
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Take a look at the project [wiki](https://github.com/stack-of-tasks/tsid/wiki) for more details.
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To install EigenPy you can compile the source code:
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git clone https://github.com/stack-of-tasks/eigenpy
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or, on Ubuntu, you can use apt-get:
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sudo apt-get install robotpkg-py27-eigenpy
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For testing the python bindings, you can run the unit test scripts in the `script` folder, for instance:
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ipython script/test_formulation.py
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To run the demo using gepetto-viewer:
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ipython demo/demo_romeo.py

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