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Journal Articles IFAC-PapersOnLine Year : 2021

Learning to model the grasp space of an underactuated robot gripper using variational autoencoder

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.
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Dates and versions

cea-03347668 , version 1 (17-09-2021)

Licence

Attribution - NonCommercial - NoDerivatives - CC BY 4.0

Identifiers

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Clément Rolinat, Mathieu Grossard, Saifeddine Aloui, Christelle Godin. Learning to model the grasp space of an underactuated robot gripper using variational autoencoder. IFAC-PapersOnLine, 2021, 19th IFAC Symposium on System Identification SYSID 2021, 54 (7), pp.523-528. ⟨10.1016/j.ifacol.2021.08.413⟩. ⟨cea-03347668⟩
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