GDN: A Coarse-To-Fine (C2F) Representation for End-To-End 6-DoF Grasp Detection
October 21, 2020 Β· Declared Dead Β· π Conference on Robot Learning
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Authors
Kuang-Yu Jeng, Yueh-Cheng Liu, Zhe Yu Liu, Jen-Wei Wang, Ya-Liang Chang, Hung-Ting Su, Winston H. Hsu
arXiv ID
2010.10695
Category
cs.RO: Robotics
Cross-listed
cs.CV
Citations
21
Venue
Conference on Robot Learning
Last Checked
3 months ago
Abstract
We proposed an end-to-end grasp detection network, Grasp Detection Network (GDN), cooperated with a novel coarse-to-fine (C2F) grasp representation design to detect diverse and accurate 6-DoF grasps based on point clouds. Compared to previous two-stage approaches which sample and evaluate multiple grasp candidates, our architecture is at least 20 times faster. It is also 8% and 40% more accurate in terms of the success rate in single object scenes and the complete rate in clutter scenes, respectively. Our method shows superior results among settings with different number of views and input points. Moreover, we propose a new AP-based metric which considers both rotation and transition errors, making it a more comprehensive evaluation tool for grasp detection models.
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