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. The video of our experiments can be found at: https://research.nvidia.com/publication/2019-10_6-DOF-GraspNet\%3A-Variational
@article{arxiv.1905.10520,
title = {6-DOF GraspNet: Variational Grasp Generation for Object Manipulation},
author = {Arsalan Mousavian and Clemens Eppner and Dieter Fox},
journal= {arXiv preprint arXiv:1905.10520},
year = {2019}
}
Comments
Accepted to ICCV 2019. Extended camera ready version with additional experiments