Deep Learning Approaches to Grasp Synthesis: A Review
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Otros documentos de la autoría: Newbury, Rhys; Gu, Morris; Chumbley, Lachlan; Mousavian, Arsalan; Eppner, Clemens; Leitner, Jürgen; Bohg, Jeannette; Morales, Antonio; Asfour, Tamim; Kragic, Danica; Fox, Dieter; Cosgun, Akansel
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Deep Learning Approaches to Grasp Synthesis: A ReviewAutoría
Fecha de publicación
2023-06-13Editor
IEEECita bibliográfica
R. Newbury et al., "Deep Learning Approaches to Grasp Synthesis: A Review," in IEEE Transactions on Robotics, vol. 39, no. 5, pp. 3994-4015, Oct. 2023, doi: 10.1109/TRO.2023.3280597.Tipo de documento
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Resumen
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 ... [+]
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. [-]
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IEEE Transactions on Robotics ( Volume: 39, Issue: 5, October 2023)Derechos de acceso
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