6 citations · 11 across the 3 of their papers we have counts for
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cs.RO2024
Graspness Discovery in Clutters for Fast and Accurate Grasp Detection
Chenxi Wang, Hao-Shu Fang, Minghao Gou +3
Efficient and robust grasp pose detection is vital for robotic manipulation. For general 6 DoF grasping, conventional methods treat all points in a scene equally and usually adopt…
cs.RO2021
OCRTOC: A Cloud-Based Competition and Benchmark for Robotic Grasping and Manipulation
Ziyuan Liu, Wei Liu, Yuzhe Qin +8
In this paper, we propose a cloud-based benchmark for robotic grasping and manipulation, called the OCRTOC benchmark. The benchmark focuses on the object rearrangement problem, spe…
cs.RO2021★ 6 cited
RGB Matters: Learning 7-DoF Grasp Poses on Monocular RGBD Images
Minghao Gou, Hao-Shu Fang, Zhanda Zhu +3
General object grasping is an important yet unsolved problem in the field of robotics. Most of the current methods either generate grasp poses with few DoF that fail to cover most…