Language-driven Grasp Detection with Mask-guided Attention

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 addre...

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Bibliographic Details
Main Authors: Van Vo, Tuan, Vu, Minh Nhat, Huang, Baoru, Vuong, An, Le, Ngan, Vo, Thieu, Nguyen, Anh
Format: Conference Proceeding
Language:English
Subjects:
Accuracy
Attention mechanisms
Feature extraction
Grasping
Natural languages
Semantic segmentation
Service robots
Source coding
Transformers
Visualization
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Summary: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.
ISSN:2153-0866
DOI:10.1109/IROS58592.2024.10802256