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Multi-step motion learning by combining learning-from-demonstration and policy-search
http://hdl.handle.net/10061/0002000204
http://hdl.handle.net/10061/0002000204b2d98516-58da-4647-b307-a0c4811c0605
| アイテムタイプ | 学術雑誌論文 / Journal Article(1) | |||||||||
|---|---|---|---|---|---|---|---|---|---|---|
| 公開日 | 2024-04-15 | |||||||||
| タイトル | ||||||||||
| タイトル | Multi-step motion learning by combining learning-from-demonstration and policy-search | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | Multi-step task | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | motion learning | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | learning from demonstration | |||||||||
| キーワード | ||||||||||
| 主題Scheme | Other | |||||||||
| 主題 | reinforcement learning | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ | journal article | |||||||||
| アクセス権 | ||||||||||
| アクセス権 | open access | |||||||||
| 著者 |
Mo, Yaqiang
× Mo, Yaqiang
× 佐々木, 光× 松原, 崇充× Yamazaki, Kimitoshi
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| 抄録 | ||||||||||
| 内容記述タイプ | Abstract | |||||||||
| 内容記述 | In this paper, we focus on tasks that require multi-step motions to achieve the goal (defined as a ‘multi-step task’), and we describe a method for a robot to automatically achieve the final goal of a multi-step task. We proposed a method based on reinforcement learning and ‘Teaching by Showing’ for multi-step tasks. A robot can learn how to complete a task automatically by referring to the motions of a human operator, even if the task consists of multi-step motions. Because a human operator is not required to operate the robot during the learning process, we believe that our proposed method can reduce the burden on the human operator. Finally, we conducted experiments to validate the effectiveness of the proposed method and compared it to a conventional reinforcement learning method. | |||||||||
| 書誌情報 |
en : Advanced Robotics 巻 37, 号 9, p. 560-575, 発行日 2023-01-19 |
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| 出版者 | ||||||||||
| 出版者 | Taylor and Francis | |||||||||
| ISSN | ||||||||||
| 収録物識別子タイプ | EISSN | |||||||||
| 収録物識別子 | 1568-5535 | |||||||||
| 出版者版DOI | ||||||||||
| 関連タイプ | isReplacedBy | |||||||||
| 識別子タイプ | DOI | |||||||||
| 関連識別子 | https://doi.org/10.1080/01691864.2022.2163187 | |||||||||
| 出版者版URI | ||||||||||
| 関連タイプ | isReplacedBy | |||||||||
| 識別子タイプ | URI | |||||||||
| 関連識別子 | https://www.tandfonline.com/doi/full/10.1080/01691864.2022.2163187 | |||||||||
| 権利 | ||||||||||
| 権利情報Resource | http://creativecommons.org/licenses/by-nc-nd/4.0/ | |||||||||
| 権利情報 | $00A9 2023 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group and The Robotics Society of Japan.This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives License (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited, and is not altered, transformed, or built upon in any way. | |||||||||
| 著者版フラグ | ||||||||||
| 出版タイプ | NA | |||||||||
