1. [Publications](/publications)
2. Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes
 
 # Contact-GraspNet: Efficient 6-DoF Grasp Generation in Cluttered Scenes

  ![](/sites/default/files/styles/wide/public/publications/contact_graspnet.png?itok=4Bi8ctyV)

 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. Our novel grasp representation treats 3D points of the recorded point cloud as potential grasp contacts. By rooting the full 6-DoF grasp pose and width in the observed point cloud, we can reduce the dimensionality of our grasp representation to 4-DoF which greatly facilitates the learning process. Our class-agnostic approach is trained on 17 million simulated grasps and generalizes well to real world sensor data. In a robotic grasping study of unseen objects in structured clutter we achieve over 90% success rate, cutting the failure rate in half compared to a recent state-of-the-art method.



 ## Authors



Martin Sundermeyer (DLR, TUM)

Arsalan Mousavian (NVIDIA)

Rudolph Triebel (DLR, TUM)

Dieter Fox (NVIDIA)

 

 

 ## Publication Date



Wednesday, March 24, 2021

 

 ## Published in



[ICRA 2021](http://www.icra2021.org/)

 

 ## Research Area



[Robotics](/research-area/robotics)

 

 

 ## External Links



[Paper](https://arxiv.org/pdf/2103.14127.pdf)

[Video](https://www.youtube.com/watch?v=qRLKYSLXElM)