A Survey on Learning-Based Robotic Grasping

doi:10.1007/s43154-020-00021-6

A Survey on Learning-Based Robotic Grasping

2020-09-20

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DOI：10.1007/s43154-020-00021-6

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AbstractPurpose of ReviewThis 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 FindingsModel-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.SummaryBoth 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.

A Survey on Learning-Based Robotic Grasping

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