WEKO3
アイテム
Local Online Motor Babbling: Learning Motor Abundance of a Musculoskeletal Robot Arm
http://hdl.handle.net/10228/00008256
http://hdl.handle.net/10228/00008256249b28a5-3657-43a6-a6f8-dc7f402d32c9
| 名前 / ファイル | ライセンス | アクション |
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10352620.pdf (4.4 MB)
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| Item type | 学術雑誌論文 = Journal Article(1) | |||||||||||||
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| 公開日 | 2021-05-17 | |||||||||||||
| 資源タイプ | ||||||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_6501 | |||||||||||||
| 資源タイプ | journal article | |||||||||||||
| タイトル | ||||||||||||||
| タイトル | Local Online Motor Babbling: Learning Motor Abundance of a Musculoskeletal Robot Arm | |||||||||||||
| 言語 | en | |||||||||||||
| 言語 | ||||||||||||||
| 言語 | eng | |||||||||||||
| 著者 |
Liu, Zinan
× Liu, Zinan× Hitzmann, Arne× 池本, 周平
WEKO
30354
× Stark, Svenja× Peters, Jan× Hosoda, Koh |
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| 抄録 | ||||||||||||||
| 内容記述タイプ | Abstract | |||||||||||||
| 内容記述 | Motor babbling and goal babbling has been used for sensorimotor learning of highly redundant systems in soft robotics. Recent works in goal babbling have demonstrated successful learning of inverse kinematics (IK) on such systems, and suggest that babbling in the goal space better resolves motor redundancy by learning as few yet efficient sensorimotor mappings as possible. However, for musculoskeletal robot systems, motor redundancy can provide useful information to explain muscle activation patterns, thus the term motor abundance. In this work, we introduce some simple heuristics to empirically define the unknown goal space, and learn the IK of a 10 DoF musculoskeletal robot arm using directed goal babbling. We then further propose local online motor babbling guided by Covariance Matrix Adaptation Evolution Strategy (CMA-ES), which bootstraps on the goal babbling samples for initialization, such that motor abundance can be queried online for any static goal. Our approach leverages the resolving of redundancies and the efficient guided exploration of motor abundance in two stages of learning, allowing both kinematic accuracy and motor variability at the queried goal. The result shows that local online motor babbling guided by CMA-ES can efficiently explore motor abundance at queried goal positions on a musculoskeletal robot system and gives useful insights in terms of muscle stiffness and synergy. | |||||||||||||
| 言語 | en | |||||||||||||
| 備考 | ||||||||||||||
| 内容記述タイプ | Other | |||||||||||||
| 内容記述 | IEEE/RSJ International Conference on Intelligent Robots and Systems (iROS2019), November 4 - 8, 2019, Macau, China | |||||||||||||
| 書誌情報 |
en : 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) p. 6594-6601, 発行日 2020-01-27 |
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| 出版社 | ||||||||||||||
| 出版者 | IEEE | |||||||||||||
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| 関連タイプ | isVersionOf | |||||||||||||
| 識別子タイプ | DOI | |||||||||||||
| 関連識別子 | https://doi.org/10.1109/IROS40897.2019.8967791 | |||||||||||||
| ISBN | ||||||||||||||
| 識別子タイプ | ISBN | |||||||||||||
| 関連識別子 | 978-1-7281-4004-9 | |||||||||||||
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| 識別子タイプ | ISBN | |||||||||||||
| 関連識別子 | 978-1-7281-4003-2 | |||||||||||||
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| 識別子タイプ | ISBN | |||||||||||||
| 関連識別子 | 978-1-7281-4005-6 | |||||||||||||
| 日本十進分類法 | ||||||||||||||
| 主題Scheme | NDC | |||||||||||||
| 主題 | 501 | |||||||||||||
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| 収録物識別子タイプ | PISSN | |||||||||||||
| 収録物識別子 | 2153-0858 | |||||||||||||
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| 収録物識別子タイプ | EISSN | |||||||||||||
| 収録物識別子 | 2153-0866 | |||||||||||||
| 著作権関連情報 | ||||||||||||||
| 権利情報 | Copyright (c) 2020 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. | |||||||||||||
| 出版タイプ | ||||||||||||||
| 出版タイプ | AM | |||||||||||||
| 出版タイプResource | http://purl.org/coar/version/c_ab4af688f83e57aa | |||||||||||||
| 査読の有無 | ||||||||||||||
| 値 | yes | |||||||||||||
| 研究者情報 | ||||||||||||||
| URL | https://hyokadb02.jimu.kyutech.ac.jp/html/100001226_ja.html | |||||||||||||
| 論文ID（連携） | ||||||||||||||
| 値 | 10352620 | |||||||||||||
| 連携ID | ||||||||||||||
| 値 | 8838 | |||||||||||||
