MPGNet: Learning Move-Push-Grasping Synergy for Target-Oriented Grasping in Occluded Scenes
Fuente:
arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| Soggetti: | |
| Accesso online: | |
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| _version_ | 1866909291614568448 |
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| author | Li, Dayou Zhao, Chenkun Yang, Shuo Song, Ran Li, Xiaolei Zhang, Wei |
| author_facet | Li, Dayou Zhao, Chenkun Yang, Shuo Song, Ran Li, Xiaolei Zhang, Wei |
| contents | This paper focuses on target-oriented grasping in occluded scenes, where the target object is specified by a binary mask and the goal is to grasp the target object with as few robotic manipulations as possible. Most existing methods rely on a push-grasping synergy to complete this task. To deliver a more powerful target-oriented grasping pipeline, we present MPGNet, a three-branch network for learning a synergy between moving, pushing, and grasping actions. We also propose a multi-stage training strategy to train the MPGNet which contains three policy networks corresponding to the three actions. The effectiveness of our method is demonstrated via both simulated and real-world experiments. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2408_10525 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MPGNet: Learning Move-Push-Grasping Synergy for Target-Oriented Grasping in Occluded Scenes Li, Dayou Zhao, Chenkun Yang, Shuo Song, Ran Li, Xiaolei Zhang, Wei Robotics This paper focuses on target-oriented grasping in occluded scenes, where the target object is specified by a binary mask and the goal is to grasp the target object with as few robotic manipulations as possible. Most existing methods rely on a push-grasping synergy to complete this task. To deliver a more powerful target-oriented grasping pipeline, we present MPGNet, a three-branch network for learning a synergy between moving, pushing, and grasping actions. We also propose a multi-stage training strategy to train the MPGNet which contains three policy networks corresponding to the three actions. The effectiveness of our method is demonstrated via both simulated and real-world experiments. |
| title | MPGNet: Learning Move-Push-Grasping Synergy for Target-Oriented Grasping in Occluded Scenes |
| topic | Robotics |
| url | https://arxiv.org/abs/2408.10525 |