Learning to model the grasp space of an underactuated robot gripper using variational autoencoder
| Autor: | Christelle Godin, Saifeddine Aloui, Clément Rolinat, Mathieu Grossard |
|---|---|
| Přispěvatelé: | Laboratoire d'Intégration des Systèmes et des Technologies (LIST (CEA)), Direction de Recherche Technologique (CEA) (DRT (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Université Paris-Saclay, Commissariat à l'énergie atomique et aux énergies alternatives - Laboratoire d'Electronique et de Technologie de l'Information (CEA-LETI), Université Grenoble Alpes (UGA), Laboratoire d'Intégration des Systèmes et des Technologies (LIST) |
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
FOS: Computer and information sciences
TheoryofComputation_MISCELLANEOUS 0209 industrial biotechnology Grasp planning Computer science business.industry GRASP Underactuated robots Robotics 02 engineering and technology Space (commercial competition) [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE] Autoencoder Space exploration [SPI.AUTO]Engineering Sciences [physics]/Automatic Computer Science - Robotics 020901 industrial engineering & automation Control and Systems Engineering 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Point (geometry) Artificial intelligence business Robotics (cs.RO) |
| Zdroj: | IFAC-Papers IFAC-PapersOnLine, 2021, 19th IFAC Symposium on System Identification SYSID 2021, 54 (7), pp.523-528. ⟨10.1016/j.ifacol.2021.08.413⟩ IFAC-PapersOnLine, Elsevier, 2021, 19th IFAC Symposium on System Identification SYSID 2021, 54 (7), pp.523-528. ⟨10.1016/j.ifacol.2021.08.413⟩ |
| ISSN: | 2405-8963 |
| DOI: | 10.1016/j.ifacol.2021.08.413⟩ |
| Popis: | 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. Comment: accepted at SYSID 2021 conference |
| Databáze: | OpenAIRE |
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