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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
Author:Martin Spitznagel, David Weiler, Klaus DorerGND
Editor:Vitor Santos, Nuno Lau, Pedro Neto, Ana Cristina Lopes
Publisher:IEEE Xplore
Year of Publication:2021
ISBN:978-1-6654-3198-9 (elektronisch)
ISBN:978-1-6654-3199-6 (Print)
Language:German
Parent Title (German):Proceedings of IEEE International Conference on Autonomous Robot Systems and Competitions
First Page:104
Last Page:110
Document Type:Conference Proceeding
Institutes:Bibliografie
Open Access:Zugriffsbeschränkt
Release Date:2022/01/13
Licence (German):License LogoUrheberrechtlich geschützt
Note:
Konferenz: 2021 IEEE International Conference on Autonomous Robot Systems and Competitions (ICARSC), 28-29 April 2021, Santa Maria da Feira, Portugal
DOI:https://doi.org/10.1109/ICARSC52212.2021.9429811