  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.
