Learning Object Relations with Graph Neural Networks for Target-Driven Grasping in Dense Clutter
Xibai Lou,Yang Yang,Changhyun Choi,Xibai Lou,Yang Yang,Changhyun Choi
Robots in the real world frequently come across identical objects in dense clutter. When evaluating grasp poses in these scenarios, a target-driven grasping system requires knowledge of spatial relations between scene objects (e.g., proximity, adjacency, and occlusions). To efficiently complete this task, we propose a target-driven grasping system that simultaneously considers object relations and...
