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Given point cloud input, the problem of 6-DoF grasp pose detection is to identify a set of hand poses in SE(3) from which an object can be successfully grasped.
A. Bicchi, “On the closure properties of robotic grasping,” The International Journal of Robotics Research , vol. 14, no. 4, pp. 319–334, 1995
1995
Earlier work this paper cites.
V. Nair and G. E. Hinton, “Rectified linear units improve restricted boltzmann machines,” in Icml , 2010
2010
Earlier work this paper cites.
A. Kasper, Z. Xue, and R. Dillmann, “The kit object models database: An object model database for object recognition, localization and manipulation in service robotics,” The International Journal of Robotics Research , vol. 31, no. 8, pp. 927–934, 2012
2012
Earlier work this paper cites.
A. Singh, J. Sha, K. S. Narayan, T. Achim, and P. Abbeel, “Bigbird: A large-scale 3d database of object instances,” in 2014 IEEE international conference on robotics and automation (ICRA) . IEEE, 2014, pp. 509–516
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
I. Lenz, H. Lee, and A. Saxena, “Deep learning for detecting robotic grasps,” The International Journal of Robotics Research , vol. 34, no. 4-5, pp. 705–724, 2015
2015
Earlier work this paper cites.
B. Calli, A. Singh, A. Walsman, S. Srinivasa, P. Abbeel, and A. M. Dollar, “The ycb object and model set: Towards common benchmarks for manipulation research,” in 2015 international conference on advanced robotics (ICAR) . IEEE, 2015, pp. 510–517
2015
Earlier work this paper cites.
D. Kappler, J. Bohg, and S. Schaal, “Leveraging big data for grasp planning,” in 2015 IEEE international conference on robotics and automation (ICRA) . IEEE, 2015, pp. 4304–4311
2015
Earlier work this paper cites.
M. Gualtieri, A. Ten Pas, K. Saenko, and R. Platt, “High precision grasp pose detection in dense clutter,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2016, pp. 598–605
2016
Earlier work this paper cites.
E. Coumans and Y. Bai, “Pybullet, a python module for physics simulation for games, robotics and machine learning,” 2016
2016
Earlier work this paper cites.
A. ten Pas, M. Gualtieri, K. Saenko, and R. Platt, “Grasp pose detection in point clouds,” The International Journal of Robotics Research , vol. 36, no. 13-14, pp. 1455–1473, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” Advances in neural information processing systems , vol. 30, 2017
2017
Cited alongside, same era.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “Pointnet: Deep learning on point sets for 3d classification and segmentation,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 652–660
2017
Cited alongside, same era.
2018
Cited alongside, same era.
Y. Zhang and M. Rabbat, “A graph-cnn for 3d point cloud classification,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2018, pp. 6279–6283
2018
Cited alongside, same era.
2021
Later among the works it cites.
B. Zhao, H. Zhang, X. Lan, H. Wang, Z. Tian, and N. Zheng, “Regnet: Region-based grasp network for end-to-end grasp detection in point clouds,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2021, pp. 13 474–13 480
2021
Later among the works it cites.
H. Huang, Z. Yang, and R. Platt, “Gascn: Graph attention shape completion network,” in 2021 International Conference on 3D Vision (3DV) . IEEE, 2021, pp. 1269–1278
2021
Later among the works it cites.
S. Huang, Z. Gojcic, M. Usvyatsov, A. Wieser, and K. Schindler, “Predator: Registration of 3d point clouds with low overlap,” in Proceedings of the IEEE/CVF Conference on computer vision and pattern recognition , 2021, pp. 4267–4276
2021
Later among the works it cites.
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2018
Cited alongside, same era.
A. Mousavian, C. Eppner, and D. Fox, “6-dof graspnet: Variational grasp generation for object manipulation,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2019, pp. 2901–2910
2019
Cited alongside, same era.
H. Liang, X. Ma, S. Li, M. Görner, S. Tang, B. Fang, F. Sun, and J. Zhang, “Pointnetgpd: Detecting grasp configurations from point sets,” in 2019 International Conference on Robotics and Automation (ICRA) . IEEE, 2019, pp. 3629–3635
2019
Cited alongside, same era.
J. Mahler, M. Matl, V. Satish, M. Danielczuk, B. DeRose, S. McKinley, and K. Goldberg, “Learning ambidextrous robot grasping policies,” Science Robotics , vol. 4, no. 26, p. eaau4984, 2019
2019
Cited alongside, same era.
S. Kumra, S. Joshi, and F. Sahin, “Antipodal robotic grasping using generative residual convolutional neural network,” in 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2020, pp. 9626–9633
2020
Cited alongside, same era.
Y. Qin, R. Chen, H. Zhu, M. Song, J. Xu, and H. Su, “S4g: Amodal single-view single-shot se (3) grasp detection in cluttered scenes,” in Conference on robot learning . PMLR, 2020, pp. 53–65
2020
Cited alongside, same era.
C. Wu, J. Chen, Q. Cao, J. Zhang, Y. Tai, L. Sun, and K. Jia, “Grasp proposal networks: An end-to-end solution for visual learning of robotic grasps,” Advances in Neural Information Processing Systems , vol. 33, pp. 13 174–13 184, 2020
2020
Cited alongside, same era.
2021
Cited alongside, same era.
C. Deng, O. Litany, Y. Duan, A. Poulenard, A. Tagliasacchi, and L. J. Guibas, “Vector neurons: A general framework for so (3)-equivariant networks,” in Proceedings of the IEEE/CVF International Conference on Computer Vision , 2021, pp. 12 200–12 209
2021
Later among the works it cites.
X. Zhu, D. Wang, O. Biza, G. Su, R. Walters, and R. Platt, “Sample efficient grasp learning using equivariant models,” Proceedings of Robotics: Science and Systems (RSS) , 2022
2022
Closest in time.
J. Cai, J. Cen, H. Wang, and M. Y. Wang, “Real-time collision-free grasp pose detection with geometry-aware refinement using high-resolution volume,” IEEE Robotics and Automation Letters , vol. 7, no. 2, pp. 1888–1895, 2022
2022
Closest in time.
2022
Closest in time.
D. Wang, R. Walters, X. Zhu, and R. Platt, “Equivariant q q learning in spatial action spaces,” in Conference on Robot Learning . PMLR, 2022, pp. 1713–1723
2022
Closest in time.
2022
Closest in time.
A. Simeonov, Y. Du, A. Tagliasacchi, J. B. Tenenbaum, A. Rodriguez, P. Agrawal, and V. Sitzmann, “Neural descriptor fields: Se (3)-equivariant object representations for manipulation,” in 2022 International Conference on Robotics and Automation (ICRA) . IEEE, 2022, pp. 6394–6400
2022
Closest in time.