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.
@article{arxiv.2409.14403,
title = {GraspMamba: A Mamba-based Language-driven Grasp Detection Framework with Hierarchical Feature Learning},
author = {Huy Hoang Nguyen and An Vuong and Anh Nguyen and Ian Reid and Minh Nhat Vu},
journal= {arXiv preprint arXiv:2409.14403},
year = {2024}
}