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| Main Authors: | , , , , , , , , , , , |
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| Format: | Preprint |
| Published: |
2023
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2310.16951 |
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| _version_ | 1866917820696100864 |
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| author | Adler, Aviv Ahmad, Ayah Qiu, Yulei Wang, Shengyin Agboh, Wisdom C. Llontop, Edith Qiu, Tianshuang Ichnowski, Jeffrey Kollar, Thomas Cheng, Richard Dogar, Mehmet Goldberg, Ken |
| author_facet | Adler, Aviv Ahmad, Ayah Qiu, Yulei Wang, Shengyin Agboh, Wisdom C. Llontop, Edith Qiu, Tianshuang Ichnowski, Jeffrey Kollar, Thomas Cheng, Richard Dogar, Mehmet Goldberg, Ken |
| contents | This paper addresses the "Teenager's Problem": efficiently removing scattered garments from a planar surface into a basket. As grasping and transporting individual garments is highly inefficient, we propose policies to select grasp locations for multiple garments using an overhead camera. Our core approach is segment-based, which uses segmentation on the overhead RGB image of the scene. We propose a Probabilistic Set Cover formulation of the problem, aiming to minimize the number of grasps that clear all garments off the surface. Grasp efficiency is measured by Objects per Transport (OpT), which denotes the average number of objects removed per trip to the laundry basket. Additionally, we explore several depth-based methods, which use overhead depth data to find efficient grasps. Experiments suggest that our segment-based method increases OpT by $50\%$ over a random baseline, whereas combined hybrid methods yield improvements of $33\%$. Finally, a method employing consolidation (with segmentation) is considered, which locally moves the garments on the work surface to increase OpT, when the distance to the basket is much greater than the local motion distances. This yields an improvement of $81\%$ over the baseline. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2310_16951 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | The Teenager's Problem: Efficient Garment Decluttering as Probabilistic Set Cover Adler, Aviv Ahmad, Ayah Qiu, Yulei Wang, Shengyin Agboh, Wisdom C. Llontop, Edith Qiu, Tianshuang Ichnowski, Jeffrey Kollar, Thomas Cheng, Richard Dogar, Mehmet Goldberg, Ken Robotics This paper addresses the "Teenager's Problem": efficiently removing scattered garments from a planar surface into a basket. As grasping and transporting individual garments is highly inefficient, we propose policies to select grasp locations for multiple garments using an overhead camera. Our core approach is segment-based, which uses segmentation on the overhead RGB image of the scene. We propose a Probabilistic Set Cover formulation of the problem, aiming to minimize the number of grasps that clear all garments off the surface. Grasp efficiency is measured by Objects per Transport (OpT), which denotes the average number of objects removed per trip to the laundry basket. Additionally, we explore several depth-based methods, which use overhead depth data to find efficient grasps. Experiments suggest that our segment-based method increases OpT by $50\%$ over a random baseline, whereas combined hybrid methods yield improvements of $33\%$. Finally, a method employing consolidation (with segmentation) is considered, which locally moves the garments on the work surface to increase OpT, when the distance to the basket is much greater than the local motion distances. This yields an improvement of $81\%$ over the baseline. |
| title | The Teenager's Problem: Efficient Garment Decluttering as Probabilistic Set Cover |
| topic | Robotics |
| url | https://arxiv.org/abs/2310.16951 |