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 RolinatConnect in order to contact the contributor Submitted on : Friday, September 17, 2021 - 2:26:51 PM Last modification on : Thursday, February 17, 2022 - 10:08:04 AM Long-term archiving on: : Saturday, December 18, 2021 - 6:51:18 PM
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⟩