1. [Publications](/publications)
2. 6-DOF Grasping for Target-driven Object Manipulation in Clutter
 
 # 6-DOF Grasping for Target-driven Object Manipulation in Clutter

  ![](/sites/default/files/styles/wide/public/publications/Screenshot%20from%202020-06-30%2017-06-32_1.png?itok=vpU_huj1)

 Grasping in cluttered environments is a fundamental but challenging robotic skill. It requires both reasoning about unseen object parts and potential collisions with the manipulator. Most existing data-driven approaches avoid this problem by limiting themselves to top-down planar grasps which is insufficient for many real-world scenarios and greatly limits possible grasps. We present a method that plans 6-DOF grasps for any desired object in a cluttered scene from partial point cloud observations. Our method achieves a grasp success of 80.3%, outperforming baseline approaches by 17.6% and clearing 9 cluttered table scenes (which contain 23 unknown objects and 51 picks in total) on a real robotic platform. By using our learned collision checking module, we can even reason about effective grasp sequences to retrieve objects that are not immediately accessible.

**Finalist for ICRA 2020 Best Student &amp; Best Manipulation Paper Award** (<http://www.icra2020.org/program/conference-awards>)



 ## Authors



Adithyavairavan Murali (NVIDIA, CMU)

Arsalan Mousavian (NVIDIA)

Clemens Eppner (NVIDIA)

Chris Paxton (NVIDIA)

Dieter Fox (NVIDIA)

 

 

 ## Publication Date



Monday, June 1, 2020

 

 ## Published in



[ICRA 2020](https://www.icra2020.org)

 

 ## Research Area



[Computer Vision](/research-area/computer-vision)

[Robotics](/research-area/robotics)

 

 

 ## External Links



[arXiv Paper](https://arxiv.org/pdf/1912.03628.pdf)

[Video](https://youtu.be/w0B5S-gCsJk)

 

 

 ## Awards



Best Paper Finalist in Robot Manipulation, ICRA 2020

Best Student Paper Finalist, ICRA 2020