S4G: Amodal Single-view Single-Shot SE(3) Grasp Detection in Cluttered Scenes

Yuzhe Qin, Rui Chen, Hao Zhu, Meng Song, Jing Xu, Hao Su
Proceedings of the Conference on Robot Learning, PMLR 100:53-65, 2020.

Abstract

Grasping is among the most fundamental and long-lasting problems in robotics study. This paper studies the problem of 6-DoF(degree of freedom) grasping by a parallel gripper in a cluttered scene captured using a commodity depth sensor from a single viewpoint. We address the problem in a learning-based framework. At the high level, we rely on a single-shot grasp proposal network, trained with synthetic data and tested in real-world scenarios. Our single-shot neural network architecture can predict amodal grasp proposal efficiently and effectively. Our training data synthesis pipeline can generate scenes of complex object configuration and leverage an innovative gripper contact model to create dense and high-quality grasp annotations. Experiments in synthetic and real environments have demonstrated that the proposed approach can outperform state-of-the-arts by a large margin.

Cite this Paper

BibTeX
@InProceedings{pmlr-v100-qin20a, title = {S4G: Amodal Single-view Single-Shot SE(3) Grasp Detection in Cluttered Scenes}, author = {Qin, Yuzhe and Chen, Rui and Zhu, Hao and Song, Meng and Xu, Jing and Su, Hao}, booktitle = {Proceedings of the Conference on Robot Learning}, pages = {53--65}, year = {2020}, editor = {Kaelbling, Leslie Pack and Kragic, Danica and Sugiura, Komei}, volume = {100}, series = {Proceedings of Machine Learning Research}, month = {30 Oct--01 Nov}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v100/qin20a/qin20a.pdf}, url = {https://proceedings.mlr.press/v100/qin20a.html}, abstract = {Grasping is among the most fundamental and long-lasting problems in robotics study. This paper studies the problem of 6-DoF(degree of freedom) grasping by a parallel gripper in a cluttered scene captured using a commodity depth sensor from a single viewpoint. We address the problem in a learning-based framework. At the high level, we rely on a single-shot grasp proposal network, trained with synthetic data and tested in real-world scenarios. Our single-shot neural network architecture can predict amodal grasp proposal efficiently and effectively. Our training data synthesis pipeline can generate scenes of complex object configuration and leverage an innovative gripper contact model to create dense and high-quality grasp annotations. Experiments in synthetic and real environments have demonstrated that the proposed approach can outperform state-of-the-arts by a large margin.} }
Endnote
%0 Conference Paper %T S4G: Amodal Single-view Single-Shot SE(3) Grasp Detection in Cluttered Scenes %A Yuzhe Qin %A Rui Chen %A Hao Zhu %A Meng Song %A Jing Xu %A Hao Su %B Proceedings of the Conference on Robot Learning %C Proceedings of Machine Learning Research %D 2020 %E Leslie Pack Kaelbling %E Danica Kragic %E Komei Sugiura %F pmlr-v100-qin20a %I PMLR %P 53--65 %U https://proceedings.mlr.press/v100/qin20a.html %V 100 %X Grasping is among the most fundamental and long-lasting problems in robotics study. This paper studies the problem of 6-DoF(degree of freedom) grasping by a parallel gripper in a cluttered scene captured using a commodity depth sensor from a single viewpoint. We address the problem in a learning-based framework. At the high level, we rely on a single-shot grasp proposal network, trained with synthetic data and tested in real-world scenarios. Our single-shot neural network architecture can predict amodal grasp proposal efficiently and effectively. Our training data synthesis pipeline can generate scenes of complex object configuration and leverage an innovative gripper contact model to create dense and high-quality grasp annotations. Experiments in synthetic and real environments have demonstrated that the proposed approach can outperform state-of-the-arts by a large margin.
APA
Qin, Y., Chen, R., Zhu, H., Song, M., Xu, J. & Su, H.. (2020). S4G: Amodal Single-view Single-Shot SE(3) Grasp Detection in Cluttered Scenes. Proceedings of the Conference on Robot Learning, in Proceedings of Machine Learning Research 100:53-65 Available from https://proceedings.mlr.press/v100/qin20a.html.

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