The Collection of a Human Robot Collaboration Dataset for Cooperative Assembly in Glovebox Environments
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arXiv
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866929674574102528 |
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| author | Sharma, Shivansh Huang, Mathew Nair, Sanat Wen, Alan Petlowany, Christina Moore, Juston Wanna, Selma Pryor, Mitch |
| author_facet | Sharma, Shivansh Huang, Mathew Nair, Sanat Wen, Alan Petlowany, Christina Moore, Juston Wanna, Selma Pryor, Mitch |
| contents | Industry 4.0 introduced AI as a transformative solution for modernizing manufacturing processes. Its successor, Industry 5.0, envisions humans as collaborators and experts guiding these AI-driven manufacturing solutions. Developing these techniques necessitates algorithms capable of safe, real-time identification of human positions in a scene, particularly their hands, during collaborative assembly. Although substantial efforts have curated datasets for hand segmentation, most focus on residential or commercial domains. Existing datasets targeting industrial settings predominantly rely on synthetic data, which we demonstrate does not effectively transfer to real-world operations. Moreover, these datasets lack uncertainty estimations critical for safe collaboration. Addressing these gaps, we present HAGS: Hand and Glove Segmentation Dataset. This dataset provides challenging examples to build applications toward hand and glove segmentation in industrial human-robot collaboration scenarios as well as assess out-of-distribution images, constructed via green screen augmentations, to determine ML-classifier robustness. We study state-of-the-art, real-time segmentation models to evaluate existing methods. Our dataset and baselines are publicly available. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2407_14649 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | The Collection of a Human Robot Collaboration Dataset for Cooperative Assembly in Glovebox Environments Sharma, Shivansh Huang, Mathew Nair, Sanat Wen, Alan Petlowany, Christina Moore, Juston Wanna, Selma Pryor, Mitch Computer Vision and Pattern Recognition Industry 4.0 introduced AI as a transformative solution for modernizing manufacturing processes. Its successor, Industry 5.0, envisions humans as collaborators and experts guiding these AI-driven manufacturing solutions. Developing these techniques necessitates algorithms capable of safe, real-time identification of human positions in a scene, particularly their hands, during collaborative assembly. Although substantial efforts have curated datasets for hand segmentation, most focus on residential or commercial domains. Existing datasets targeting industrial settings predominantly rely on synthetic data, which we demonstrate does not effectively transfer to real-world operations. Moreover, these datasets lack uncertainty estimations critical for safe collaboration. Addressing these gaps, we present HAGS: Hand and Glove Segmentation Dataset. This dataset provides challenging examples to build applications toward hand and glove segmentation in industrial human-robot collaboration scenarios as well as assess out-of-distribution images, constructed via green screen augmentations, to determine ML-classifier robustness. We study state-of-the-art, real-time segmentation models to evaluate existing methods. Our dataset and baselines are publicly available. |
| title | The Collection of a Human Robot Collaboration Dataset for Cooperative Assembly in Glovebox Environments |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2407.14649 |