Publication: IEEE Transactions on Robotics
Abstract: This article surveys the literature on 6 degrees of freedom (6-DoF) Grasping using deep learning. We focus our review on robotic grasping in table-top scenarios, where the robot requires all 6 degrees of freedom of the end-effector pose to pick objects from the table successfully. Our review found the following ive approaches most prevalent in literature:sampling based approaches, direct regression, using shape-completion, reinforcement learning or considering semantics. We structure over review around these common methodologies, exploring the current research behind each approach. We report a list of the quantitative metrics commonly used to assess the success of the grasping tasks, while we also review the current object sets, datasets and sensor modalities used in the deep-learning methods. We then discuss the findings of our reviews and make recommendations on the future directions for the field hoping to mitigate some of the current issues which exist in this field.
Bibtex:
@article{10149823,
author = {Newbury, Rhys and Gu, Morris and Chumbley, Lachlan and Mousavian, Arsalan and Eppner, Clemens and Leitner, Jürgen and Bohg, Jeannette and Morales, Antonio and Asfour, Tamim and Kragic, Danica and Fox, Dieter and Cosgun, Akansel},
journal = {IEEE Transactions on Robotics},
title = {Deep Learning Approaches to Grasp Synthesis: A Review},
year = {2023},
volume = {39},
number = {5},
pages = {3994-4015},
project_ keywords = {Grasping;Deep learning;Task analysis;Grippers;Systematics;Shape;Force;Dexterous manipulation;deep learning in robotics and automation;grasping;perception for grasping and manipulation},
doi = {10.1109/TRO.2023.3280597}
}