Deep Learning Approaches to Grasp Synthesis: A Review

Grasping is the process of picking up an object by applying forces and torques at a set of contacts. Recent advances in deep learning methods have allowed rapid progress in robotic object grasping. In this systematic review, we surveyed the publications over the last decade, with a particular intere...

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Bibliographic Details
Published in:IEEE transactions on robotics Vol. 39; no. 5; pp. 1 - 22
Main Authors: Newbury, Rhys, Gu, Morris, Chumbley, Lachlan, Mousavian, Arsalan, Eppner, Clemens, Leitner, Jurgen, Bohg, Jeannette, Morales, Antonio, Asfour, Tamim, Kragic, Danica, Fox, Dieter, Cosgun, Akansel
Format: Journal Article
Language:English
Published: New York IEEE 01.10.2023
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Deep learning
deep learning in robotics and automation
Dexterous manipulation
End effectors
Force
Grasping
Grasping (robotics)
Grippers
perception for grasping and manipulation
Shape
Systematics
Task analysis
ISSN:1552-3098, 1941-0468, 1941-0468
Online Access:Get full text
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Summary:Grasping is the process of picking up an object by applying forces and torques at a set of contacts. Recent advances in deep learning methods have allowed rapid progress in robotic object grasping. In this systematic review, we surveyed the publications over the last decade, with a particular interest in grasping an object using all six degrees of freedom of the end-effector pose. Our review found four common methodologies for robotic grasping: sampling-based approaches, direct regression, reinforcement learning, and exemplar approaches In addition, we found two "supporting methods" around grasping that use deep learning to support the grasping process, shape approximation, and affordances. We have distilled the publications found in this systematic review (85 papers) into ten key takeaways we consider crucial for future robotic grasping and manipulation research.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
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ISSN:1552-3098
1941-0468
1941-0468
DOI:10.1109/TRO.2023.3280597