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The accurate detection and grasping of transparent objects are challengingbut of significance to robots. Here, a visual-tactile fusion framework fortransparent object grasping under complex backgrounds and variant lightconditions is proposed, including the grasping position detection, tactilecalibration, and visual-tactile fusion based classification. First, amulti-scene synthetic grasping dataset generation method with a Gaussiandistribution based data annotation is proposed. Besides, a novel graspingnetwork named TGCNN is proposed for grasping position detection, showing goodresults in both synthetic and real scenes. In tactile calibration, inspired byhuman grasping, a fully convolutional network based tactile feature extractionmethod and a central location based adaptive grasping strategy are designed,improving the success rate by 36.7% compared to direct grasping. Furthermore, avisual-tactile fusion method is proposed for transparent objectsclassification, which improves the classification accuracy by 34%. The proposedframework synergizes the advantages of vision and touch, and greatly improvesthe grasping efficiency of transparent objects.

Visual-tactile Fusion for Transparent Object Grasping in Complex Backgrounds