  Grasp detection is an essential task in robotics with various industrial
applications. However, traditional methods often struggle with occlusions and
do not utilize language for grasping. Incorporating natural language into grasp
detection remains a challenging task and largely unexplored. To address this
gap, we propose a new method for language-driven grasp detection with
mask-guided attention by utilizing the transformer attention mechanism with
semantic segmentation features. Our approach integrates visual data,
segmentation mask features, and natural language instructions, significantly
improving grasp detection accuracy. Our work introduces a new framework for
language-driven grasp detection, paving the way for language-driven robotic
applications. Intensive experiments show that our method outperforms other
recent baselines by a clear margin, with a 10.0% success score improvement. We
further validate our method in real-world robotic experiments, confirming the
effectiveness of our approach.
