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arXiv · 2409.14403

GraspMamba: A Mamba-based Language-driven Grasp Detection Framework with Hierarchical Feature Learning

Huy Hoang Nguyen·An Vuong·Anh Nguyen·Ian Reid·Minh Nhat Vu
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Abstract

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.

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Huy Hoang Nguyen, An Vuong, Anh Nguyen, Ian Reid, Minh Nhat Vu. 2024-09-22. GraspMamba: A Mamba-based Language-driven Grasp Detection Framework with Hierarchical Feature Learning. https://arxiv.org/abs/2409.14403

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