文章基本信息

标题：Learning to Model the Grasp Space of an Underactuated Robot Gripper Using Variational Autoencoder
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作者：Clément Rolinat ; Mathieu Grossard ; Saifeddine Aloui 等
期刊名称：IFAC PapersOnLine
印刷版ISSN：2405-8963
出版年度：2021
卷号：54
期号：7
页码：523-528
DOI：10.1016/j.ifacol.2021.08.413
语种：English
出版社：Elsevier
摘要：AbstractGrasp planning and most specifically the grasp space exploration is still an open issue in robotics. This article presents a data-driven oriented methodology to model the grasp space of a multi-fingered adaptive gripper for known objects. This method relies on a limited dataset of manually specified expert grasps, and uses variational autoencoder to learn grasp intrinsic features in a compact way from a computational point of view. The learnt model can then be used to generate new non-learnt gripper configurations to explore the grasp space.
关键词：Keywordsmulti-fingered grippergrasp space explorationvariational autoencoder