Region-aware Grasp Framework with Normalized Grasp Space for Efficient 6-DoF Grasping
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| _version_ | 1866912119160569856 |
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| author | Chen, Siang Xie, Pengwei Tang, Wei Hu, Dingchang Dai, Yixiang Wang, Guijin |
| author_facet | Chen, Siang Xie, Pengwei Tang, Wei Hu, Dingchang Dai, Yixiang Wang, Guijin |
| contents | A series of region-based methods succeed in extracting regional features and enhancing grasp detection quality. However, faced with a cluttered scene with potential collision, the definition of the grasp-relevant region stays inconsistent, and the relationship between grasps and regional spaces remains incompletely investigated. In this paper, we propose Normalized Grasp Space (NGS) from a novel region-aware viewpoint, unifying the grasp representation within a normalized regional space and benefiting the generalizability of methods. Leveraging the NGS, we find that CNNs are underestimated for 3D feature extraction and 6-DoF grasp detection in clutter scenes and build a highly efficient Region-aware Normalized Grasp Network (RNGNet). Experiments on the public benchmark show that our method achieves significant >20% performance gains while attaining a real-time inference speed of approximately 50 FPS. Real-world cluttered scene clearance experiments underscore the effectiveness of our method. Further, human-to-robot handover and dynamic object grasping experiments demonstrate the potential of our proposed method for closed-loop grasping in dynamic scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2406_01767 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Region-aware Grasp Framework with Normalized Grasp Space for Efficient 6-DoF Grasping Chen, Siang Xie, Pengwei Tang, Wei Hu, Dingchang Dai, Yixiang Wang, Guijin Robotics A series of region-based methods succeed in extracting regional features and enhancing grasp detection quality. However, faced with a cluttered scene with potential collision, the definition of the grasp-relevant region stays inconsistent, and the relationship between grasps and regional spaces remains incompletely investigated. In this paper, we propose Normalized Grasp Space (NGS) from a novel region-aware viewpoint, unifying the grasp representation within a normalized regional space and benefiting the generalizability of methods. Leveraging the NGS, we find that CNNs are underestimated for 3D feature extraction and 6-DoF grasp detection in clutter scenes and build a highly efficient Region-aware Normalized Grasp Network (RNGNet). Experiments on the public benchmark show that our method achieves significant >20% performance gains while attaining a real-time inference speed of approximately 50 FPS. Real-world cluttered scene clearance experiments underscore the effectiveness of our method. Further, human-to-robot handover and dynamic object grasping experiments demonstrate the potential of our proposed method for closed-loop grasping in dynamic scenarios. |
| title | Region-aware Grasp Framework with Normalized Grasp Space for Efficient 6-DoF Grasping |
| topic | Robotics |
| url | https://arxiv.org/abs/2406.01767 |