ICCV 2019poster714 citations
6-DOF GraspNet: Variational Grasp Generation for Object Manipulation
Arsalan Mousavian, Clemens Eppner, Dieter Fox
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.
BibTeX
@inproceedings{iccv2019_6dofgraspnetvari,
title = {6-DOF GraspNet: Variational Grasp Generation for Object Manipulation},
author = {Arsalan Mousavian and Clemens Eppner and Dieter Fox},
booktitle = {ICCV 2019},
year = {2019}
}