GraspMamba: A Mamba-based Language-driven Grasp Detection Framework with Hierarchical Feature Learning
<|reference_start|>GraspMamba: A Mamba-based Language-driven Grasp Detection Framework with Hierarchical Feature Learning: 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.<|reference_end|>
@article{nguyen2024graspmamba:,
    title={GraspMamba: A Mamba-based Language-driven Grasp Detection Framework with
  Hierarchical Feature Learning},
    author={Huy Hoang Nguyen, An Vuong, Anh Nguyen, Ian Reid, Minh Nhat Vu},
    journal={arXiv preprint arXiv:2409.14403},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.14403},
    primaryClass={cs.RO cs.CV}
}