Kleeberger, KilianBormann, RichardKraus, WernerHuber, Marco F.2023-08-042023-08-0420202662-40871858462568http://nbn-resolving.de/urn:nbn:de:bsz:93-opus-ds-133881http://elib.uni-stuttgart.de/handle/11682/13388http://dx.doi.org/10.18419/opus-13369This 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. 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.eninfo:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/4.0/004620article2023-05-16