| アイテムタイプ |
学術雑誌論文 = Journal Article(1) |
| 公開日 |
2024-01-04 |
| 資源タイプ |
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資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
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資源タイプ |
journal article |
| タイトル |
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|
タイトル |
Learning Soft Robot Dynamics Using Differentiable Kalman Filters and Spatio-Temporal Embeddings |
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言語 |
en |
| その他のタイトル |
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その他のタイトル |
Learning Soft Robot Dynamics using Differentiable Kalman Filters and Spatio-Temporal Embeddings |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 著者 |
Liu, Xiao
池本, 周平
Yoshimitsu, Yuhei
Amor, Heni Ben
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| 抄録 |
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内容記述タイプ |
Abstract |
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内容記述 |
This paper introduces a novel approach for modeling the dynamics of soft robots, utilizing a differentiable filter architecture. The proposed approach enables end-to-end training to learn system dynamics, noise characteristics, and temporal behavior of the robot. A novel spatio-temporal embedding process is discussed to handle observations with varying sensor placements and sampling frequencies. The efficacy of this approach is demonstrated on a tensegrity robot arm by learning end-effector dynamics from demonstrations with complex bending motions. The model is proven to be robust against missing modalities, diverse sensor placement, and varying sampling rates. Additionally, the proposed framework is shown to identify physical interactions with humans during motion. The utilization of a differentiable filter presents a novel solution to the difficulties of modeling soft robot dynamics. Our approach shows substantial improvement in accuracy compared to state-of-the-art filtering methods, with at least a 24% reduction in mean absolute error (MAE) observed. Furthermore, the predicted end-effector positions show an average MAE of 25.77mm from the ground truth, highlighting the advantage of our approach. The code is available at https://github.com/ir-lab/soft_robot_DEnKF. |
|
言語 |
en |
| 備考 |
|
|
内容記述タイプ |
Other |
|
内容記述 |
2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 01-05 October, 2023, Detroit, MI, USA |
|
言語 |
en |
| 書誌情報 |
en : 2023 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
p. 2550-2557,
発行日 2023-12-13
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| 出版社 |
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出版者 |
IEEE |
| DOI |
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関連タイプ |
isVersionOf |
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識別子タイプ |
DOI |
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|
関連識別子 |
https://doi.org/10.1109/IROS55552.2023.10341856 |
| ISBN |
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識別子タイプ |
ISBN |
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|
関連識別子 |
978-1-6654-9190-7 |
| ISBN |
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|
識別子タイプ |
ISBN |
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|
関連識別子 |
978-1-6654-9191-4 |
| ISSN |
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収録物識別子タイプ |
EISSN |
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収録物識別子 |
2153-0866 |
| ISSN |
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収録物識別子タイプ |
PISSN |
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収録物識別子 |
2153-0858 |
| 著作権関連情報 |
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|
権利情報 |
Copyright (c) 2023 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. |
| キーワード |
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主題Scheme |
Other |
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主題 |
Measurement |
| キーワード |
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主題Scheme |
Other |
|
主題 |
Sensor placement |
| キーワード |
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主題Scheme |
Other |
|
主題 |
Analytical models |
| キーワード |
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|
主題Scheme |
Other |
|
主題 |
Deformation |
| キーワード |
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主題Scheme |
Other |
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主題 |
Force |
| キーワード |
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|
主題Scheme |
Other |
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主題 |
Dynamics |
| キーワード |
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主題Scheme |
Other |
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主題 |
Soft robotics |
| 出版タイプ |
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出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| 査読の有無 |
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値 |
yes |
| 研究者情報 |
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URL |
https://hyokadb02.jimu.kyutech.ac.jp/html/100001226_ja.html |
| 論文ID（連携） |
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|
値 |
10422458 |
| 連携ID |
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値 |
11449 |
2308.09868v1.pdf (9 MB)
