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Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes
[article]
2021
arXiv
pre-print
Grasping unseen objects in unconstrained, cluttered environments is an essential skill for autonomous robotic manipulation. Despite recent progress in full 6-DoF grasp learning, existing approaches often consist of complex sequential pipelines that possess several potential failure points and run-times unsuitable for closed-loop grasping. Therefore, we propose an end-to-end network that efficiently generates a distribution of 6-DoF parallel-jaw grasps directly from a depth recording of a scene.
arXiv:2103.14127v1
fatcat:ofjfk54pove3bglsajuan2v24a