The accurate detection and grasping of transparent objects are challenging
but of significance to robots. Here, a visual-tactile fusion framework for
transparent object grasping under complex backgrounds and variant light
conditions is proposed, including the grasping position detection, tactile
calibration, and visual-tactile fusion based classification. First, a
multi-scene synthetic grasping dataset generation method with a Gaussian
distribution based data annotation is proposed. Besides, a novel grasping
network named TGCNN is proposed for grasping position detection, showing good
results in both synthetic and real scenes. In tactile calibration, inspired by
human grasping, a fully convolutional network based tactile feature extraction
method and a central location based adaptive grasping strategy are designed,
improving the success rate by 36.7% compared to direct grasping. Furthermore, a
visual-tactile fusion method is proposed for transparent objects
classification, which improves the classification accuracy by 34%. The proposed
framework synergizes the advantages of vision and touch, and greatly improves
the grasping efficiency of transparent objects.

本论文提出了一个适用于复杂背景和光线条件下透明对象抓取的视触融合框架，包括抓取位置检测、触觉校准和基于视触融合的分类。该框架综合了视觉和触觉的优势，极大地提高了透明对象的抓取效率。