Language-driven Grasp Detection with Mask-guided Attention
This work addresses the challenge of language-driven grasp detection for robotics applications, representing a novel integration rather than an incremental improvement.
The paper tackles the problem of grasp detection in robotics by incorporating natural language instructions, addressing limitations of traditional methods that struggle with occlusions. The proposed method achieves a 10.0% improvement in success score over baselines and is validated in real-world robotic experiments.
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.