GraspMamba: A Mamba-based Language-driven Grasp Detection Framework with Hierarchical Feature Learning
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arXiv
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| Auteurs principaux: | , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910616642387968 |
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| author | Nguyen, Huy Hoang Vuong, An Nguyen, Anh Reid, Ian Vu, Minh Nhat |
| author_facet | Nguyen, Huy Hoang Vuong, An Nguyen, Anh Reid, Ian Vu, Minh Nhat |
| contents | Grasp detection is a fundamental robotic task critical to the success of many industrial applications. However, current language-driven models for this task often struggle with cluttered images, lengthy textual descriptions, or slow inference speed. We introduce GraspMamba, a new language-driven grasp detection method that employs hierarchical feature fusion with Mamba vision to tackle these challenges. By leveraging rich visual features of the Mamba-based backbone alongside textual information, our approach effectively enhances the fusion of multimodal features. GraspMamba represents the first Mamba-based grasp detection model to extract vision and language features at multiple scales, delivering robust performance and rapid inference time. Intensive experiments show that GraspMamba outperforms recent methods by a clear margin. We validate our approach through real-world robotic experiments, highlighting its fast inference speed. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_14403 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | GraspMamba: A Mamba-based Language-driven Grasp Detection Framework with Hierarchical Feature Learning Nguyen, Huy Hoang Vuong, An Nguyen, Anh Reid, Ian Vu, Minh Nhat Robotics Computer Vision and Pattern Recognition Grasp detection is a fundamental robotic task critical to the success of many industrial applications. However, current language-driven models for this task often struggle with cluttered images, lengthy textual descriptions, or slow inference speed. We introduce GraspMamba, a new language-driven grasp detection method that employs hierarchical feature fusion with Mamba vision to tackle these challenges. By leveraging rich visual features of the Mamba-based backbone alongside textual information, our approach effectively enhances the fusion of multimodal features. GraspMamba represents the first Mamba-based grasp detection model to extract vision and language features at multiple scales, delivering robust performance and rapid inference time. Intensive experiments show that GraspMamba outperforms recent methods by a clear margin. We validate our approach through real-world robotic experiments, highlighting its fast inference speed. |
| title | GraspMamba: A Mamba-based Language-driven Grasp Detection Framework with Hierarchical Feature Learning |
| topic | Robotics Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2409.14403 |