6-DoF Grasp Detection in Clutter with Enhanced Receptive Field and Graspable Balance Sampling
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arXiv
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| Autores principales: | , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866929611119525888 |
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| author | Wang, Hanwen Zhang, Ying Wang, Yunlong Li, Jian |
| author_facet | Wang, Hanwen Zhang, Ying Wang, Yunlong Li, Jian |
| contents | 6-DoF grasp detection of small-scale grasps is crucial for robots to perform specific tasks. This paper focuses on enhancing the recognition capability of small-scale grasping, aiming to improve the overall accuracy of grasping prediction results and the generalization ability of the network. We propose an enhanced receptive field method that includes a multi-radii cylinder grouping module and a passive attention module. This method enhances the receptive field area within the graspable space and strengthens the learning of graspable features. Additionally, we design a graspable balance sampling module based on a segmentation network, which enables the network to focus on features of small objects, thereby improving the recognition capability of small-scale grasping. Our network achieves state-of-the-art performance on the GraspNet-1Billion dataset, with an overall improvement of approximately 10% in average precision@k (AP). Furthermore, we deployed our grasp detection model in pybullet grasping platform, which validates the effectiveness of our method. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2407_01209 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | 6-DoF Grasp Detection in Clutter with Enhanced Receptive Field and Graspable Balance Sampling Wang, Hanwen Zhang, Ying Wang, Yunlong Li, Jian Robotics 6-DoF grasp detection of small-scale grasps is crucial for robots to perform specific tasks. This paper focuses on enhancing the recognition capability of small-scale grasping, aiming to improve the overall accuracy of grasping prediction results and the generalization ability of the network. We propose an enhanced receptive field method that includes a multi-radii cylinder grouping module and a passive attention module. This method enhances the receptive field area within the graspable space and strengthens the learning of graspable features. Additionally, we design a graspable balance sampling module based on a segmentation network, which enables the network to focus on features of small objects, thereby improving the recognition capability of small-scale grasping. Our network achieves state-of-the-art performance on the GraspNet-1Billion dataset, with an overall improvement of approximately 10% in average precision@k (AP). Furthermore, we deployed our grasp detection model in pybullet grasping platform, which validates the effectiveness of our method. |
| title | 6-DoF Grasp Detection in Clutter with Enhanced Receptive Field and Graspable Balance Sampling |
| topic | Robotics |
| url | https://arxiv.org/abs/2407.01209 |