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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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https://hal-cea.archives-ouvertes.fr/cea-03347668
Contributor : Clément Rolinat Connect in order to contact the contributor
Submitted on : Friday, September 17, 2021 - 2:26:51 PM
Last modification on : Friday, October 1, 2021 - 3:40:42 AM

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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, Elsevier, 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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