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A Survey on Learning-Based Robotic Grasping

: Kleeberger, Kilian; Bormann, Richard; Kraus, Werner; Huber, Marco

Fulltext ()

Current robotics reports 1 (2020), No.4, pp.239-249
ISSN: 2662-4087
Journal Article, Electronic Publication
Fraunhofer IPA ()
deep learning; Künstliche Intelligenz; Roboterarm; Robotermanipulation; Simulation

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.
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.