4 citations · 6 across the 4 of their papers we have counts for
Showing cs.ROShow all
3 papers · 1 filter
cs.RO2024★ 1 cited
REGNet V2: End-to-End REgion-based Grasp Detection Network for Grippers of Different Sizes in Point Clouds
Binglei Zhao, Han Wang, Jian Tang +5
Grasping has been a crucial but challenging problem in robotics for many years. One of the most important challenges is how to make grasping generalizable and robust to novel objec…
cs.RO2021★ 1 cited
REGRAD: A Large-Scale Relational Grasp Dataset for Safe and Object-Specific Robotic Grasping in Clutter
Hanbo Zhang, Deyu Yang, Han Wang +4
Despite the impressive progress achieved in robotic grasping, robots are not skilled in sophisticated tasks (e.g. search and grasp a specified target in clutter). Such tasks involv…
cs.RO2020★ 4 cited
REGNet: REgion-based Grasp Network for End-to-end Grasp Detection in Point Clouds
Binglei Zhao, Hanbo Zhang, Xuguang Lan +3
Reliable robotic grasping in unstructured environments is a crucial but challenging task. The main problem is to generate the optimal grasp of novel objects from partial noisy obse…