22 citations · 23 across the 2 of their papers we have counts for
2 papers
cs.RO2023★ 1 cited
Multiple-object Grasping Using a Multiple-suction-cup Vacuum Gripper in Cluttered Scenes
Ping Jiang, Junji Oaki, Yoshiyuki Ishihara +1
Multiple-suction-cup grasping can improve the efficiency of bin picking in cluttered scenes. In this paper, we propose a grasp planner for a vacuum gripper to use multiple suction…
cs.RO2021★ 22 cited
Learning suction graspability considering grasp quality and robot reachability for bin-picking
Ping Jiang, Junji Oaki, Yoshiyuki Ishihara +7
Deep learning has been widely used for inferring robust grasps. Although human-labeled RGB-D datasets were initially used to learn grasp configurations, preparation of this kind of…