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Automated Scenario Generation for Autonomous Robots using Large Language Models
https://ipsj.ixsq.nii.ac.jp/records/241867
https://ipsj.ixsq.nii.ac.jp/records/2418677a5d7d59-6172-48b9-8ea1-9c2d351727fd
| 名前 / ファイル | ライセンス | アクション |
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IPSJ-APRIS2024007.pdf (919.0 kB)
2026年12月27日からダウンロード可能です。
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Copyright (c) 2024 by the Information Processing Society of Japan
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| 非会員：¥0, IPSJ:学会員：¥0, EMB:会員：¥0, DLIB:会員：¥0 | ||
| Item type | Symposium(1) | |||||||||
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| 公開日 | 2024-12-27 | |||||||||
| タイトル | ||||||||||
| タイトル | Automated Scenario Generation for Autonomous Robots using Large Language Models | |||||||||
| タイトル | ||||||||||
| 言語 | en | |||||||||
| タイトル | Automated Scenario Generation for Autonomous Robots using Large Language Models | |||||||||
| 言語 | ||||||||||
| 言語 | eng | |||||||||
| 資源タイプ | ||||||||||
| 資源タイプ識別子 | http://purl.org/coar/resource_type/c_5794 | |||||||||
| 資源タイプ | conference paper | |||||||||
| 著者所属 | ||||||||||
| Shibaura Institute of Technology | ||||||||||
| 著者所属 | ||||||||||
| Shibaura Institute of Technology | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Shibaura Institute of Technology | ||||||||||
| 著者所属(英) | ||||||||||
| en | ||||||||||
| Shibaura Institute of Technology | ||||||||||
| 著者名 |
Kyoji, Tanaka
× Kyoji, Tanaka
× Kenji, Hisazumi
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| 著者名(英) |
Kyoji, Tanaka
× Kyoji, Tanaka
× Kenji, Hisazumi
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| 論文抄録 | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | In recent autonomous robot development, there is a growing need for early problem detection through cost-effective validation and countermeasures using simulation testing. However, there are still many challenges in the automatic generation of test scenarios and the diversity and complexity of the tests. This paper proposes a more accurate drone simulation that meets user requirements by conducting scenario-based testing using the Hakoniwa platform, which allows for IoT and robot software development in a virtual environment, and Large Language Models (LLM). | |||||||||
| 論文抄録(英) | ||||||||||
| 内容記述タイプ | Other | |||||||||
| 内容記述 | In recent autonomous robot development, there is a growing need for early problem detection through cost-effective validation and countermeasures using simulation testing. However, there are still many challenges in the automatic generation of test scenarios and the diversity and complexity of the tests. This paper proposes a more accurate drone simulation that meets user requirements by conducting scenario-based testing using the Hakoniwa platform, which allows for IoT and robot software development in a virtual environment, and Large Language Models (LLM). | |||||||||
| 書誌情報 |
Proceedings of Asia Pacific Conference on Robot IoT System Development and Platform 巻 2024, p. 45-46, 発行日 2024-12-27 |
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| 出版者 | ||||||||||
| 言語 | ja | |||||||||
| 出版者 | 情報処理学会 | |||||||||
