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Journal articles
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.
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 : Monday, December 13, 2021 - 9:16:42 AM
Long-term archiving on: : Saturday, December 18, 2021 - 6:51:18 PM
Contributor : Clément Rolinat Connect in order to contact the contributor
Submitted on : Friday, September 17, 2021 - 2:26:51 PM
Last modification on : Monday, December 13, 2021 - 9:16:42 AM
Long-term archiving on: : Saturday, December 18, 2021 - 6:51:18 PM
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