VMGNet: A Low Computational Complexity Robotic Grasping Network Based on VMamba with Multi-Scale Feature Fusion Article Swipe
Yuhao Jin
,
Qiang Gao
,
Xiaohui Zhu
,
Yong Yue
,
Eng Gee Lim
,
Yuqing Chen
,
Prudence W. H. Wong
,
Yijie Chu
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.12520
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2411.12520
While deep learning-based robotic grasping technology has demonstrated strong adaptability, its computational complexity has also significantly increased, making it unsuitable for scenarios with high real-time requirements. Therefore, we propose a low computational complexity and high accuracy model named VMGNet for robotic grasping. For the first time, we introduce the Visual State Space into the robotic grasping field to achieve linear computational complexity, thereby greatly reducing the model's computational cost. Meanwhile, to improve the accuracy of the model, we propose an efficient and lightweight multi-scale feature fusion module, named Fusion Bridge Module, to extract and fuse information at different scales. We also present a new loss function calculation method to enhance the importance differences between subtasks, improving the model's fitting ability. Experiments show that VMGNet has only 8.7G Floating Point Operations and an inference time of 8.1 ms on our devices. VMGNet also achieved state-of-the-art performance on the Cornell and Jacquard public datasets. To validate VMGNet's effectiveness in practical applications, we conducted real grasping experiments in multi-object scenarios, and VMGNet achieved an excellent performance with a 94.4% success rate in real-world grasping tasks. The video for the real-world robotic grasping experiments is available at https://youtu.be/S-QHBtbmLc4.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2411.12520
- https://arxiv.org/pdf/2411.12520
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404573971
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404573971Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2411.12520Digital Object Identifier
- Title
-
VMGNet: A Low Computational Complexity Robotic Grasping Network Based on VMamba with Multi-Scale Feature FusionWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-11-19Full publication date if available
- Authors
-
Yuhao Jin, Qiang Gao, Xiaohui Zhu, Yong Yue, Eng Gee Lim, Yuqing Chen, Prudence W. H. Wong, Yijie ChuList of authors in order
- Landing page
-
https://arxiv.org/abs/2411.12520Publisher landing page
- PDF URL
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https://arxiv.org/pdf/2411.12520Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://arxiv.org/pdf/2411.12520Direct OA link when available
- Concepts
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Artificial intelligence, Scale (ratio), Computer science, Feature (linguistics), Fusion, Computational complexity theory, Computer vision, Pattern recognition (psychology), Algorithm, Geography, Cartography, Philosophy, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.datasets. | 151 |
| abstract_inverted_index.different | 97 |
| abstract_inverted_index.efficient | 80 |
| abstract_inverted_index.excellent | 171 |
| abstract_inverted_index.grasping. | 41 |
| abstract_inverted_index.improving | 115 |
| abstract_inverted_index.inference | 132 |
| abstract_inverted_index.introduce | 47 |
| abstract_inverted_index.practical | 157 |
| abstract_inverted_index.real-time | 24 |
| abstract_inverted_index.scenarios | 21 |
| abstract_inverted_index.subtasks, | 114 |
| abstract_inverted_index.Meanwhile, | 69 |
| abstract_inverted_index.Operations | 129 |
| abstract_inverted_index.Therefore, | 26 |
| abstract_inverted_index.complexity | 12, 32 |
| abstract_inverted_index.importance | 111 |
| abstract_inverted_index.increased, | 16 |
| abstract_inverted_index.real-world | 179, 186 |
| abstract_inverted_index.scenarios, | 166 |
| abstract_inverted_index.technology | 5 |
| abstract_inverted_index.unsuitable | 19 |
| abstract_inverted_index.Experiments | 120 |
| abstract_inverted_index.calculation | 106 |
| abstract_inverted_index.complexity, | 61 |
| abstract_inverted_index.differences | 112 |
| abstract_inverted_index.experiments | 163, 189 |
| abstract_inverted_index.information | 95 |
| abstract_inverted_index.lightweight | 82 |
| abstract_inverted_index.multi-scale | 83 |
| abstract_inverted_index.performance | 144, 172 |
| abstract_inverted_index.demonstrated | 7 |
| abstract_inverted_index.multi-object | 165 |
| abstract_inverted_index.adaptability, | 9 |
| abstract_inverted_index.applications, | 158 |
| abstract_inverted_index.computational | 11, 31, 60, 67 |
| abstract_inverted_index.effectiveness | 155 |
| abstract_inverted_index.requirements. | 25 |
| abstract_inverted_index.significantly | 15 |
| abstract_inverted_index.learning-based | 2 |
| abstract_inverted_index.state-of-the-art | 143 |
| abstract_inverted_index.https://youtu.be/S-QHBtbmLc4. | 193 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 8 |
| citation_normalized_percentile |