UniGarmentManip: A Unified Framework for Category-Level Garment Manipulation via Dense Visual Correspondence
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866929340498837504 |
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| author | Wu, Ruihai Lu, Haoran Wang, Yiyan Wang, Yubo Dong, Hao |
| author_facet | Wu, Ruihai Lu, Haoran Wang, Yiyan Wang, Yubo Dong, Hao |
| contents | Garment manipulation (e.g., unfolding, folding and hanging clothes) is essential for future robots to accomplish home-assistant tasks, while highly challenging due to the diversity of garment configurations, geometries and deformations. Although able to manipulate similar shaped garments in a certain task, previous works mostly have to design different policies for different tasks, could not generalize to garments with diverse geometries, and often rely heavily on human-annotated data. In this paper, we leverage the property that, garments in a certain category have similar structures, and then learn the topological dense (point-level) visual correspondence among garments in the category level with different deformations in the self-supervised manner. The topological correspondence can be easily adapted to the functional correspondence to guide the manipulation policies for various downstream tasks, within only one or few-shot demonstrations. Experiments over garments in 3 different categories on 3 representative tasks in diverse scenarios, using one or two arms, taking one or more steps, inputting flat or messy garments, demonstrate the effectiveness of our proposed method. Project page: https://warshallrho.github.io/unigarmentmanip. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_06903 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | UniGarmentManip: A Unified Framework for Category-Level Garment Manipulation via Dense Visual Correspondence Wu, Ruihai Lu, Haoran Wang, Yiyan Wang, Yubo Dong, Hao Computer Vision and Pattern Recognition Garment manipulation (e.g., unfolding, folding and hanging clothes) is essential for future robots to accomplish home-assistant tasks, while highly challenging due to the diversity of garment configurations, geometries and deformations. Although able to manipulate similar shaped garments in a certain task, previous works mostly have to design different policies for different tasks, could not generalize to garments with diverse geometries, and often rely heavily on human-annotated data. In this paper, we leverage the property that, garments in a certain category have similar structures, and then learn the topological dense (point-level) visual correspondence among garments in the category level with different deformations in the self-supervised manner. The topological correspondence can be easily adapted to the functional correspondence to guide the manipulation policies for various downstream tasks, within only one or few-shot demonstrations. Experiments over garments in 3 different categories on 3 representative tasks in diverse scenarios, using one or two arms, taking one or more steps, inputting flat or messy garments, demonstrate the effectiveness of our proposed method. Project page: https://warshallrho.github.io/unigarmentmanip. |
| title | UniGarmentManip: A Unified Framework for Category-Level Garment Manipulation via Dense Visual Correspondence |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2405.06903 |