Policy gradient learning for quadruped soccer robots

Abstract : In real-world robotic applications, many factors, both at low level (e.g., vision, motion control and behaviors) and at high level (e.g., plans and strategies) determine the quality of the robot performance. Consequently, fine tuning of the parameters, in the implementation of the basic functionalities, as well as in the strategic decisions, is a key issue in robot software development. In recent years, machine learning techniques have been successfully used to find optimal parameters for typical robotic functionalities. However, one major drawback of learning techniques is time consumption: in practical applications, methods designed for physical robots must be effective with small amounts of data. In this paper, we present a method for concurrent learning of best strategy and optimal parameters using policy gradient reinforcement learning algorithm. The results of our experimental work in a simulated environment and on a real robot show a very high convergence rate.
Type de document :
Article dans une revue
Robotics and Autonomous Systems, Elsevier, 2010, 58 (7), pp.872-878. 〈10.1016/j.robot.2010.03.008〉
Liste complète des métadonnées

Littérature citée [8 références]  Voir  Masquer  Télécharger

Contributeur : Isabelle Gouat <>
Soumis le : lundi 9 novembre 2015 - 13:37:20
Dernière modification le : lundi 30 octobre 2017 - 02:58:03
Document(s) archivé(s) le : mercredi 10 février 2016 - 10:25:23


Fichiers produits par l'(les) auteur(s)




Andrea Cherubini, Francesca Giannone, Luca Iocchi, Daniele Nardi, Pier Francesco Palamara. Policy gradient learning for quadruped soccer robots. Robotics and Autonomous Systems, Elsevier, 2010, 58 (7), pp.872-878. 〈10.1016/j.robot.2010.03.008〉. 〈lirmm-01222978〉



Consultations de la notice


Téléchargements de fichiers