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Optimal control and machine learning for humanoid and aerial robots

Mathieu Geisert 1
1 LAAS-GEPETTO - Équipe Mouvement des Systèmes Anthropomorphes
LAAS - Laboratoire d'analyse et d'architecture des systèmes
Abstract : What are the common characteristics of humanoid robots and quadrotors? Well, not many… Therefore, this thesis focuses on the development of algorithms allowing to dynamically control a robot while staying generic with respect to the model of the robot and the task that needs to be solved. Numerical optimal control is good candidate to achieve such objective. However, it suffers from several difficulties such as a high number of parameters to tune and a relatively important computation time. This document presents several ameliorations allowing to reduce these problems. On one hand, the tasks can be ordered according to a hierarchy and solved with an appropriate algorithm to lower the number of parameters to tune. On the other hand, machine learning can be used to initialize the optimization solver or to generate a simplified model of the robot, and therefore can be used to decrease the computation time.
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Submitted on : Thursday, October 18, 2018 - 5:09:08 PM
Last modification on : Thursday, June 10, 2021 - 3:04:22 AM
Long-term archiving on: : Saturday, January 19, 2019 - 3:08:41 PM


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  • HAL Id : tel-01886622, version 2


Mathieu Geisert. Optimal control and machine learning for humanoid and aerial robots. Automatic. INSA de Toulouse, 2018. English. ⟨NNT : 2018ISAT0011⟩. ⟨tel-01886622v2⟩



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