Trial-and-Error Learning of Repulsors for Humanoid QP-based Whole-Body Control

Jonathan Spitz 1 Karim Bouyarmane 2, 1 Serena Ivaldi 1 Jean-Baptiste Mouret 1
1 LARSEN - Lifelong Autonomy and interaction skills for Robots in a Sensing ENvironment
Inria Nancy - Grand Est, LORIA - AIS - Department of Complex Systems, Artificial Intelligence & Robotics
2 UL - Université de Lorraine
Abstract : Whole body controllers based on quadratic programming allow humanoid robots to achieve complex motions. However, they rely on the assumption that the model perfectly captures the dynamics of the robot and its environment, whereas even the most accurate models are never perfect. In this paper, we introduce a trial-and-error learning algorithm that allows whole-body controllers to operate in spite of inaccurate models, without needing to update these models. The main idea is to encourage the controller to perform the task differently after each trial by introducing repulsors in the quadratic program cost function. We demonstrate our algorithm on (1) a simple 2D case and (2) a simulated iCub robot for which the model used by the controller and the one used in simulation do not match.
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Communication dans un congrès
IEEE RAS International Conference on Humanoid Robots, 2017, Birmingham, France. 2017
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Soumis le : vendredi 28 juillet 2017 - 02:48:45
Dernière modification le : lundi 23 avril 2018 - 13:38:01

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Jonathan Spitz, Karim Bouyarmane, Serena Ivaldi, Jean-Baptiste Mouret. Trial-and-Error Learning of Repulsors for Humanoid QP-based Whole-Body Control. IEEE RAS International Conference on Humanoid Robots, 2017, Birmingham, France. 2017. 〈hal-01569948〉

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