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Learning to Model the Grasp Space of an Underactuated Robot Gripper Using Variational Autoencoder
Copyright policy )Learning to Model the Grasp Space of an Underactuated Robot Gripper Using Variational Autoencoder
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
ACM Computing Classification System: TheoryofComputation_MISCELLANEOUS
Microsoft Academic Graph classification: Grasp planning business.industry Computer science GRASP Underactuated robots Robotics Space (commercial competition) Autoencoder Space exploration Point (geometry) Artificial intelligence business
FOS: Computer and information sciences, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [SPI.AUTO]Engineering Sciences [physics]/Automatic, Computer Science - Robotics, Control and Systems Engineering, Robotics (cs.RO)
FOS: Computer and information sciences, [INFO.INFO-NE]Computer Science [cs]/Neural and Evolutionary Computing [cs.NE], [SPI.AUTO]Engineering Sciences [physics]/Automatic, Computer Science - Robotics, Control and Systems Engineering, Robotics (cs.RO)
ACM Computing Classification System: TheoryofComputation_MISCELLANEOUS
Microsoft Academic Graph classification: Grasp planning business.industry Computer science GRASP Underactuated robots Robotics Space (commercial competition) Autoencoder Space exploration Point (geometry) Artificial intelligence business
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).1 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).1 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Average influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Average
Citations provided by BIP!Popularity provided by BIP!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