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Other literature type . Preprint . 2021
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Learning to Model the Grasp Space of an Underactuated Robot Gripper Using Variational Autoencoder

Authors: Rolinat, Clément; Grossard, Mathieu; Aloui, Saifeddine; Godin, Christelle;

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.

Comment: accepted at SYSID 2021 conference

Country
France
Subjects by Vocabulary

Microsoft Academic Graph classification: Grasp planning business.industry Computer science Underactuated robots GRASP Robotics Space (commercial competition) Autoencoder Space exploration Point (geometry) Artificial intelligence business

Keywords

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)

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    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
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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).
BIP!Citations provided by BIP!
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.
BIP!Popularity provided by BIP!
influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
BIP!Influence provided by BIP!
impulse
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.
BIP!Impulse provided by BIP!
1
Average
Average
Average
Green
gold
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