Fetching the paper…
Reading the bibliography…
Suction is an important solution for the longstanding robotic grasping problem.
X. Provot et al. , “Deformation constraints in a mass-spring model to describe rigid cloth behaviour,” in Graphics interface . Canada: Canadian Information Processing Society, 1995, pp. 147–147
1995
Earlier work this paper cites.
B. Bahr, Y. Li, and M. Najafi, “Design and suction cup analysis of a wall climbing robot,” Computers & electrical engineering , vol. 22, no. 3, pp. 193–209, 1996
1996
Earlier work this paper cites.
A. Ali, M. Hosseini, and B. Sahari, “A review of constitutive models for rubber-like materials,” American Journal of Engineering and Applied Sciences , vol. 3, no. 1, pp. 232–239, 2010
2010
Earlier work this paper cites.
C. Hernandez, M. Bharatheesha, W. Ko, H. Gaiser, J. Tan, K. van Deurzen, M. de Vries, B. Van Mil, J. van Egmond, R. Burger et al. , “Team delft’s robot winner of the amazon picking challenge 2016,” in Robot World Cup . Springer, 2016, pp. 613–624
2016
Earlier work this paper cites.
C. Rennie, R. Shome, K. E. Bekris, and A. F. De Souza, “A dataset for improved rgbd-based object detection and pose estimation for warehouse pick-and-place,” IEEE Robotics and Automation Letters , vol. 1, no. 2, pp. 1179–1185, 2016
2016
Earlier work this paper cites.
C. Eppner, S. Höfer, R. Jonschkowski, R. Martín-Martín, A. Sieverling, V. Wall, and O. Brock, “Lessons from the amazon picking challenge: Four aspects of building robotic systems.” in Robotics: science and systems , 2016, p. 4831–4835
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Earlier work this paper cites.
J. Mahler, J. Liang, S. Niyaz, M. Laskey, R. Doan, X. Liu, J. A. Ojea, and K. Goldberg, “Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics,” Robotics: Science and Systems (RSS) , pp. 1–8, 2017
2017
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.
A. Zeng, S. Song, K.-T. Yu, E. Donlon, F. R. Hogan, M. Bauza, D. Ma, O. Taylor, M. Liu, E. Romo et al. , “Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching,” in 2018 IEEE international conference on robotics and automation (ICRA) . IEEE, 2018, pp. 1–8
2018
Cited alongside, same era.
M. Schwarz, C. Lenz, G. M. García, S. Koo, A. S. Periyasamy, M. Schreiber, and S. Behnke, “Fast object learning and dual-arm coordination for cluttered stowing, picking, and packing,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 3347–3354
2018
Cited alongside, same era.
J. Mahler, M. Matl, X. Liu, A. Li, D. Gealy, and K. Goldberg, “Dex-net 3.0: Computing robust vacuum suction grasp targets in point clouds using a new analytic model and deep learning,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 1–8
2018
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
Later among the works it cites.
Q. Shao, J. Hu, W. Wang, Y. Fang, W. Liu, J. Qi, and J. Ma, “Suction grasp region prediction using self-supervised learning for object picking in dense clutter,” in 2019 IEEE 5th International Conference on Mechatronics System and Robots (ICMSR) . IEEE, 2019, pp. 7–12
2019
Later among the works it cites.
K. Kleeberger, C. Landgraf, and M. F. Huber, “Large-scale 6d object pose estimation dataset for industrial bin-picking,” in 2019 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2019, pp. 2573–2578
2019
Later among the works it cites.
C. Correa, J. Mahler, M. Danielczuk, and K. Goldberg, “Robust toppling for vacuum suction grasping,” in 2019 IEEE 15th International Conference on Automation Science and Engineering (CASE) . IEEE, 2019, pp. 1421–1428
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
D. Morrison, A. W. Tow, M. Mctaggart, R. Smith, N. Kelly-Boxall, S. Wade-Mccue, J. Erskine, R. Grinover, A. Gurman, T. Hunn et al. , “Cartman: The low-cost cartesian manipulator that won the amazon robotics challenge,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 7757–7764
2018
Cited alongside, same era.
Y. Xiang, T. Schmidt, V. Narayanan, and D. Fox, “Posecnn: A convolutional neural network for 6d object pose estimation in cluttered scenes,” Robotics: Science and Systems (RSS) , pp. 1–8, 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
L.-C. Chen, Y. Zhu, G. Papandreou, F. Schroff, and H. Adam, “Encoder-decoder with atrous separable convolution for semantic image segmentation,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 801–818
2018
Cited alongside, same era.
2018
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.
2019
Later among the works it cites.
X. Zhou, D. Wang, and P. Krähenbühl, “Objects as points,” arXiv preprint arXiv:1904.07850 , 2019
2019
Later among the works it cites.
H.-S. Fang, C. Wang, M. Gou, and C. Lu, “Graspnet-1billion: A large-scale benchmark for general object grasping,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 444–11 453
2020
Later among the works it cites.
Y. He, W. Sun, H. Huang, J. Liu, H. Fan, and J. Sun, “Pvn3d: A deep point-wise 3d keypoints voting network for 6dof pose estimation,” in Proceedings of the IEEE/CVF conference on computer vision and pattern recognition , 2020, pp. 11 632–11 641
2020
Later among the works it cites.
M. Gou, H. Fang, Z. Zhu, S. Xu, C. Wang, and C. Lu, “Rgb matters: Learning 7-dof grasp poses on monocular rgbd images,” International Conference on Robotics and Automation (ICRA) , pp. 1–8, 2021
2021
Closest in time.