ML Anthology

6-DOF GraspNet: Variational Grasp Generation for Object Manipulation

Arsalan Mousavian, Clemens Eppner, Dieter Fox
ICCV 2019
doi:10.1109/ICCV.2019.00299 /iccv/2019/mousavian2019iccv-6dof/

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.

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Text

Mousavian et al. "6-DOF GraspNet: Variational Grasp Generation for Object Manipulation." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019. doi:10.1109/ICCV.2019.00299

Markdown

[Mousavian et al. "6-DOF GraspNet: Variational Grasp Generation for Object Manipulation." Proceedings of the IEEE/CVF International Conference on Computer Vision, 2019.](https://mlanthology.org/iccv/2019/mousavian2019iccv-6dof/) doi:10.1109/ICCV.2019.00299

BibTeX

@inproceedings{mousavian2019iccv-6dof,
  title     = {{6-DOF GraspNet: Variational Grasp Generation for Object Manipulation}},
  author    = {Mousavian, Arsalan and Eppner, Clemens and Fox, Dieter},
  booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision},
  year      = {2019},
  doi       = {10.1109/ICCV.2019.00299},
  url       = {https://mlanthology.org/iccv/2019/mousavian2019iccv-6dof/}
}