Language-driven Grasp Detection with Mask-guided Attention

TL;DR

This paper presents a novel transformer-based framework that integrates visual data, segmentation masks, and natural language instructions to improve robotic grasp detection, especially under occlusions, demonstrated by significant experimental success.

Contribution

The paper introduces a new language-driven grasp detection framework using mask-guided attention and transformer mechanisms, advancing the integration of natural language in robotic grasping.

Findings

Outperforms recent baselines with a 10% success rate increase

Effective in real-world robotic experiments

Significantly improves grasp detection accuracy under occlusions

Abstract

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.