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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2024
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| Accès en ligne: | https://arxiv.org/abs/2408.11347 |
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| _version_ | 1866912030954356736 |
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| author | Ugai, Takanori Hara, Kensho Egami, Shusaku Fukuda, Ken |
| author_facet | Ugai, Takanori Hara, Kensho Egami, Shusaku Fukuda, Ken |
| contents | We used a 3D simulator to create artificial video data with standardized annotations, aiming to aid in the development of Embodied AI. Our question answering (QA) dataset measures the extent to which a robot can understand human behavior and the environment in a home setting. Preliminary experiments suggest our dataset is useful in measuring AI's comprehension of daily life. \end{abstract} |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_11347 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multimodal Datasets and Benchmarks for Reasoning about Dynamic Spatio-Temporality in Everyday Environments Ugai, Takanori Hara, Kensho Egami, Shusaku Fukuda, Ken Artificial Intelligence We used a 3D simulator to create artificial video data with standardized annotations, aiming to aid in the development of Embodied AI. Our question answering (QA) dataset measures the extent to which a robot can understand human behavior and the environment in a home setting. Preliminary experiments suggest our dataset is useful in measuring AI's comprehension of daily life. \end{abstract} |
| title | Multimodal Datasets and Benchmarks for Reasoning about Dynamic Spatio-Temporality in Everyday Environments |
| topic | Artificial Intelligence |
| url | https://arxiv.org/abs/2408.11347 |