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Multimodal grasp data set: A novel visuala??tactile data set for robotic manipulation

机译：多模式抓取数据集：一种新颖的视觉，触觉数据集，用于机器人操纵

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摘要

This article introduces a visuala??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 Intela??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 dataa??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 seta??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.
机译：本文介绍了可视化触觉多模式抓取数据集，旨在进一步研究机器人操纵。该数据集由新颖设计的灵巧机器人手，Intela的Eagle Shoal机器人手（英特尔实验室中国，中国北京）建立。该数据集包含2550套数据，包括触觉，关节，时间标签，图像，RGB和深度视频。通过视觉和触觉数据的整合，研究人员可以更好地理解抓握过程并分析更深层次的抓握问题。在本文中，介绍了数据集的构建过程以及数据集的组成。为了评估数据集的质量，通过短时傅立叶变换对触觉数据进行了分析。通过长期的短期记忆实现了基于触觉数据的滑动检测，并与视觉数据进行了对比。实验将长时记忆与传统分类器进行了比较，并实现了对不同方向和对象的归纳能力。结果证明，数据集在促进机器人操纵区域研究方面的价值显示了有效的滑移检测和长短期记忆的泛化能力。视觉和触觉数据的进一步工作将在未来投入。

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