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Authors: Andres El-Fakdi 1 ; Marc Carreras 1 ; Javier Antich 2 and Alberto Ortiz 2

Affiliations: 1 Computer Vision and Robotics Group (VICOROB), Institute of Informatics and Applications, University of Girona, Spain ; 2 University of Balearic Islands, Spain

Keyword(s): Machine learning in control applications, space and underwater robots.

Related Ontology Subjects/Areas/Topics: Informatics in Control, Automation and Robotics ; Intelligent Control Systems and Optimization ; Machine Learning in Control Applications ; Robotics and Automation ; Space and Underwater Robotics

Abstract: This paper proposes a field application of a high-level Reinforcement Learning (RL) control system for solving the action selection problem of an autonomous robot in cable tracking task. The learning system is characterized by using a Direct Policy Search method for learning the internal state/action mapping. Policy only algorithms may suffer from long convergence times when dealing with real robotics. In order to speed up the process, the learning phase has been carried out in a simulated environment and, in a second step, the policy has been transferred and tested successfully on a real robot. Future steps plan to continue the learning process on-line while on the real robot while performing the mentioned task. We demonstrate its feasibility with real experiments on the underwater robot ICT INEU AUV .

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Paper citation in several formats:
El-Fakdi, A.; Carreras, M.; Antich, J. and Ortiz, A. (2008). LEARNING BY EXAMPLE - Reinforcement Learning Techniques for Real Autonomous Underwater Cable Tracking. In Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO; ISBN 978-989-8111-31-9; ISSN 2184-2809, SciTePress, pages 61-68. DOI: 10.5220/0001490500610068

@conference{icinco08,
author={Andres El{-}Fakdi. and Marc Carreras. and Javier Antich. and Alberto Ortiz.},
title={LEARNING BY EXAMPLE - Reinforcement Learning Techniques for Real Autonomous Underwater Cable Tracking},
booktitle={Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO},
year={2008},
pages={61-68},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0001490500610068},
isbn={978-989-8111-31-9},
issn={2184-2809},
}

TY - CONF

JO - Proceedings of the Fifth International Conference on Informatics in Control, Automation and Robotics - Volume 2: ICINCO
TI - LEARNING BY EXAMPLE - Reinforcement Learning Techniques for Real Autonomous Underwater Cable Tracking
SN - 978-989-8111-31-9
IS - 2184-2809
AU - El-Fakdi, A.
AU - Carreras, M.
AU - Antich, J.
AU - Ortiz, A.
PY - 2008
SP - 61
EP - 68
DO - 10.5220/0001490500610068
PB - SciTePress

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