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Journal Articles IFAC-PapersOnLine Year : 2021

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

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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

Cite

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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