6-DOF Grasp Detection for Unknown Objects Using Surface Reconstruction

Schaub, Henry; Schöttl, Alfred; Hoh, Maximilian

doi:10.1109/hora52670.2021.9461271

Cited by 5 publications

(2 citation statements)

References 20 publications

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“…They then use a 4-DoF grasping algorithm [79] for each real and virtual camera. This is extended by Schaub et al [80] who fused depth images around the scene to provide more detailed depth maps. Choi et al [53] extended a previous 4-DoF approach [81] to 6-DoF by using an iterative improvement algorithm approach to choose an approach direction.…”

Section: Deep Learning In Graspingmentioning

confidence: 99%

Deep Learning Approaches to Grasp Synthesis: A Review

Newbury

Lachlan

et al. 2023

IEEE Trans. Robot.

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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 6 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. Additionally, 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. An online version of the survey is available at https://rhys-newbury.github.io/projects/6dof/

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Section: Deep Learning In Graspingmentioning

confidence: 99%

Deep Learning Approaches to Grasp Synthesis: A Review

Newbury

Lachlan

et al. 2023

IEEE Trans. Robot.

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show abstract

“…They then use a 4-DoF grasping algorithm [8] for each real and virtual camera. This is extended by Schaub et al [76] who fused depth images around the scene to provide more detailed depth maps. Choi et al [51] extended a previous 4-DoF approach [77] to 6-DoF by using an iterative improvement algorithm approach to choose an approach direction.…”

Section: A Samplingmentioning

confidence: 99%

Deep Learning Approaches to Grasp Synthesis: A Review

Newbury¹,

Gu²,

Lachlan³

et al. 2022

Preprint

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Grasping is the process of picking 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. We systematically surveyed the publications over the last decade, with a particular interest in grasping an object using all 6 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. Furthermore, we found two 'supporting methods' around grasping that use deeplearning 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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Real-Time Generative Grasping with Spatio-temporal Sparse Convolution

Player

Chang

et al. 2023

2023 IEEE International Conference on Robotics and Automation (ICRA)

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6-DOF Grasp Detection for Unknown Objects Using Surface Reconstruction

Cited by 5 publications

References 20 publications

Deep Learning Approaches to Grasp Synthesis: A Review

Deep Learning Approaches to Grasp Synthesis: A Review

Deep Learning Approaches to Grasp Synthesis: A Review

Real-Time Generative Grasping with Spatio-temporal Sparse Convolution

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