A Survey on Learning-Based Robotic Grasping

Author:

Kleeberger KilianORCID,Bormann Richard,Kraus Werner,Huber Marco F.

Abstract

Abstract Purpose of Review This review provides a comprehensive overview of machine learning approaches for vision-based robotic grasping and manipulation. Current trends and developments as well as various criteria for categorization of approaches are provided. Recent Findings Model-free approaches are attractive due to their generalization capabilities to novel objects, but are mostly limited to top-down grasps and do not allow a precise object placement which can limit their applicability. In contrast, model-based methods allow a precise placement and aim for an automatic configuration without any human intervention to enable a fast and easy deployment. Summary Both approaches to robotic grasping and manipulation with and without object-specific knowledge are discussed. Due to the large amount of data required to train AI-based approaches, simulations are an attractive choice for robot learning. This article also gives an overview of techniques and achievements in transfers from simulations to the real world.

Funder

Fraunhofer Institute for Manufacturing Engineering and Automation (IPA Fraunhofer)

Publisher

Springer Science and Business Media LLC

Subject

General Engineering

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