BIP! Finder - Learning to Model the Grasp Space of an Underactuated Robot Gripper Using Variational Autoencoder

2021 • Learning to Model the Grasp Space of an Underactuated Robot Gripper Using Variational Autoencoder

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

Venue: IFAC-PapersOnLine

Type: Publication

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.

Topics: Artificial intelligence

DOI: 10.1016/j.ifacol.2021.08.413

BIP! social metrics: 0 65
External links: Crossref OpenAIRE

Found 2 versions BibTex PDF

Topic-specific impact indicators

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.
Influence:
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
Citation Count:
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).
Impulse:
This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.