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Deep Reinforcement Multi-Directional Kick-Learning of a Simulated Robot with Toes

  • This paper describes a thorough analysis of using PPO to learn kick behaviors with simulated NAO robots in the simspark environment. The analysis includes an investigation of the influence of PPO hyperparameters, network size, training setups and performance in real games. We believe to improve the state of the art mainly in four points: first, the kicks are learned with a toed version of the NAOThis paper describes a thorough analysis of using PPO to learn kick behaviors with simulated NAO robots in the simspark environment. The analysis includes an investigation of the influence of PPO hyperparameters, network size, training setups and performance in real games. We believe to improve the state of the art mainly in four points: first, the kicks are learned with a toed version of the NAO robot, second, we improve the reliability with respect to kickable area and avoidance of falls, third, the kick can be parameterized with desired distance and direction as input to the deep network and fourth, the approach allows to integrate the learned behavior seamlessly into soccer games. The result is a significant improvement of the general level of play.show moreshow less

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Metadaten
Document Type:Conference Proceeding
Conference Type:Konferenzartikel
Zitierlink: https://opus.hs-offenburg.de/5339
Bibliografische Angaben
Title (English):Deep Reinforcement Multi-Directional Kick-Learning of a Simulated Robot with Toes
Conference:2021 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), 28-29 April 2021, Santa Maria da Feira, Portugal : Online Videoconference
Author:Martin Spitznagel, David Weiler, Klaus DorerStaff MemberORCiDGND
Year of Publication:2021
Publisher:IEEE
First Page:104
Last Page:110
Parent Title (English):2021 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC)
Editor:Vitor Santos, Nuno Lau, Pedro Neto, Ana Cristina Lopes
ISBN:978-1-6654-3198-9 (elektronisch)
ISBN:978-1-6654-3199-6 (Print)
DOI:https://doi.org/10.1109/ICARSC52212.2021.9429811
URL:https://magma.hs-offenburg.de/fileadmin/default_upload/2021ICARSCWithCopyright.pdf
Language:English
Inhaltliche Informationen
Institutes:Fakultät Elektrotechnik, Medizintechnik und Informatik (EMI) (ab 04/2019)
Institutes:Bibliografie
Projekte / Magma Offenburg
Formale Angaben
Open Access: Open Access 
 Grün 
Licence (German):License LogoUrheberrechtlich geschützt