Difference between revisions of "EECI 2020: Computer Session: TuLiP"

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(Lecture Materials)
(Lecture Materials)
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==  Lecture Materials ==
 
==  Lecture Materials ==
* Lecture slides: [http://www.cds.caltech.edu/~murray/courses/eeci-sp13/C2_tulip-21Mar13.pdf TuLiP]
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* Lecture slides: [http://www.cds.caltech.edu/~murray/courses/eeci-sp2020/C2_tulip-11Mar2020.pdf TuLiP]
 
* amination script: [home/tichakorn/Shared/eeci/animate.py animate.py]
 
* amination script: [home/tichakorn/Shared/eeci/animate.py animate.py]
* [http://www.cds.caltech.edu/~murray/courses/eeci-sp12/tulip_examples.zip Example TuLiP files] (zip file):  
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* [http://www.cds.caltech.edu/~murray/courses/eeci-sp2020/tulip_examples.zip Example TuLiP files] (zip file):  
** 6 cell robot, discrete state space: [http://www.cds.caltech.edu/~murray/courses/eeci-sp12/robot_discrete_simple.py robot_discrete_simple.py]
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** 6 cell robot, discrete state space: [http://www.cds.caltech.edu/~murray/courses/eeci-sp2020/robot_simple_discrete.py robot_simple_discrete.py]
** 6 cell robot, with dynamics: [http://www.cds.caltech.edu/~murray/courses/eeci-sp12/robot_simple.py robot_simple.py], [http://www.cds.caltech.edu/~murray/courses/eeci-sp12/robot_simple2.py robot_simple2.py] (alternative formulation)
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** 6 cell robot, with dynamics: [http://www.cds.caltech.edu/~murray/courses/eeci-sp2020/robot_simple_continuous.py robot_simple_continuous.py]
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** 3x3 exercise: [http://www.cds.caltech.edu/~murray/courses/eeci-sp2020/exercise_3x3.py exercise_3x3.py]
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** Left turn exercise: [http://www.cds.caltech.edu/~murray/courses/eeci-sp2020/exercise_leftturn.py exercise_leftturn.py]
  
 
== Further Reading ==
 
== Further Reading ==

Revision as of 09:28, 12 March 2020

Prev: Reactive Synthesis Course home Next: Minimum Violation Planning

This lecture provides an overview of TuLiP, a Python-based software toolbox for the synthesis of embedded control software that is provably correct with respect to a GR[1] specifications. TuLiP combines routines for (1) finite state abstraction of control systems, (2) digital design synthesis from GR[1] specifications, and (3) receding horizon planning. The underlying digital design synthesis routine treats the environment as adversary; hence, the resulting controller is guaranteed to be correct for any admissible environment profile. TuLiP applies the receding horizon framework, allowing the synthesis problem to be broken into a set of smaller problems, and consequently alleviating the computational complexity of the synthesis procedure, while preserving the correctness guarantee.

A brief overview of TuLiP will be followed by hands-on exercises using the toolbox.

Lecture Materials

Further Reading

Additional Information