Learning to Model the Grasp Space of an Underactuated Robot Gripper Using Variational Autoencoder. Issue 7 (2021)

• Record Type: Journal Article
• Authors: Rolinat, Clément; Grossard, Mathieu; Aloui, Saifeddine; Godin, Christelle
• Is Part Of: IFAC-PapersOnLine. Volume 54:Issue 7(2021)
• Page Start: 523
• Page End: 528
• Publication Date: 2021
• Subjects: multi-fingered gripper -- grasp space exploration -- variational autoencoder; Automatic control -- Periodicals; 629.805
• DOI: 10.1016/j.ifacol.2021.08.413
• Languages: English
• ISSNs: 2405-8963

Abstract

Abstract: Grasp 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.