| アイテムタイプ |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2025-12-26 |
| タイトル |
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タイトル |
ISPIL: Interactive Sub-Goal-Planning Imitation Learning for Long-Horizon Tasks With Diverse Goals |
| 言語 |
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言語 |
eng |
| キーワード |
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|
主題Scheme |
Other |
|
主題 |
Interactive imitation learning |
| キーワード |
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|
主題Scheme |
Other |
|
主題 |
learning-to-plan |
| キーワード |
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|
主題Scheme |
Other |
|
主題 |
hierarchical policy |
| 資源タイプ |
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資源タイプ |
journal article |
| アクセス権 |
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アクセス権 |
open access |
| 著者 |
Ochoa, Cynthia
Oh, Hanbit
Kwon, Yuhwan
Domae, Yukiyasu
松原, 崇充
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| 抄録 |
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内容記述タイプ |
Abstract |
|
内容記述 |
Imitation Learning (IL) is a promising approach for teaching tasks to robots by human demonstrations, although it faces challenges from long-horizon tasks and diverse goals in real-world settings. These issues stem from (i) a distribution mismatch between demonstrations and real-world execution and (ii) existing policy models that typically focus on prelearned final goals, limiting efficiency with diverse goals. To address this situation, we propose Interactive Sub-Goal-Planning Imitation Learning (ISPIL), an IL framework that learns hierarchical, goal-conditioned policies. Specifically, a high-level policy sets reachable sub-goals for the final goals, and a low-level policy executes the required actions. ISPIL interactively collects two types of demonstration data based on the novelty criteria: meta-sub-goal data, which represent with symbols the causal relationships between sub-goals, and action data, which consist of the physical robotic actions required to achieve these sub-goals. Meta-sub-goal data enable effective planning using a Regression Planning Network (RPN), and a sub-goal switching function helps reduce unnecessary data queries at the high level. We validate ISPIL through simulations and real-robot experiments in a kitchen-like environment and demonstrate improved task execution and generalizability across diverse goals. |
| 書誌情報 |
en : IEEE Access
巻 12,
p. 197616-197631,
ページ数 16,
発行日 2024-12-23
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| 出版者 |
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出版者 |
IEEE |
| 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 |
2169-3536 |
| 出版者版DOI |
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関連タイプ |
isReplacedBy |
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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/ACCESS.2024.3521302 |
| 出版者版URI |
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関連タイプ |
isReplacedBy |
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|
識別子タイプ |
URI |
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関連識別子 |
https://ieeexplore.ieee.org/document/10811934 |
| 権利 |
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権利情報Resource |
https://creativecommons.org/licenses/by-nc-nd/4.0/ |
|
権利情報 |
© 2024 The Authors. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License. For more information, see https://creativecommons.org/licenses/by-nc-nd/4.0/ |
| 著者版フラグ |
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出版タイプ |
NA |
| 助成情報 |
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|
助成機関名 |
New Energy and Industrial Technology Development Organization (NEDO) |
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研究課題番号 |
JPNP20006 |
