6-DOF GraspNet: Variational Grasp Generation for Object Manipulation

Arsalan Mousavian, Clemens Eppner, Dieter Fox; The IEEE International Conference on Computer Vision (ICCV), 2019, pp. 2901-2910

Abstract


Generating grasp poses is a crucial component for any robot object manipulation task. In this work, we formulate the problem of grasp generation as sampling a set of grasps using a variational autoencoder and assess and refine the sampled grasps using a grasp evaluator model. Both Grasp Sampler and Grasp Refinement networks take 3D point clouds observed by a depth camera as input. We evaluate our approach in simulation and real-world robot experiments. Our approach achieves 88% success rate on various commonly used objects with diverse appearances, scales, and weights. Our model is trained purely in simulation and works in the real-world without any extra steps.

Related Material


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[bibtex]
@InProceedings{Mousavian_2019_ICCV,
author = {Mousavian, Arsalan and Eppner, Clemens and Fox, Dieter},
title = {6-DOF GraspNet: Variational Grasp Generation for Object Manipulation},
booktitle = {The IEEE International Conference on Computer Vision (ICCV)},
month = {October},
year = {2019}
}