Multimodal grasp data set: A novel visual–tactile data set for robotic manipulation. (7th January 2019)

• Record Type: Journal Article
• Authors: Wang, Tao; Yang, Chao; Kirchner, Frank; Du, Peng; Sun, Fuchun; Fang, Bin
• Is Part Of: International journal of advanced robotic systems. Volume 16:Number 1(2019:Jan./Feb.)
• Publication Date: 2019-01-07
• Subjects: Grasping data set -- visual and tactile -- robotic manipulation -- slip detection -- long short-term memory; Robotics -- Periodicals; Robotics; Periodicals; 629.892
• DOI: 10.1177/1729881418821571
• Languages: English
• ISSNs: 1729-8806

Abstract

This article introduces a visual–tactile multimodal grasp data set, aiming to further the research on robotic manipulation. The data set was built by the novel designed dexterous robot hand, the Intel's Eagle Shoal robot hand (Intel Labs China, Beijing, China). The data set contains 2550 sets data, including tactile, joint, time label, image, and RGB and depth video. With the integration of visual and tactile data, researchers could be able to better understand the grasping process and analyze the deeper grasping issues. In this article, the building process of the data set was introduced, as well as the data set composition. In order to evaluate the quality of data set, the tactile data were analyzed by short-time Fourier transform. The tactile data–based slip detection was realized by long short-term memory and contrasted with visual data. The experiments compared the long short-term memory with the traditional classifiers, and generalization ability on different grasp directions and different objects is implemented. The results have proved that the data set's value in promoting research on robotic manipulation area showed the effective slip detection and generalization ability of long short-term memory. Further work on visual and tactile data will be devoted to in the future.