Fetching the paper…
Reading the bibliography…
The ability to grasp objects is an essential skill that enables many robotic manipulation tasks.
V.-D. Nguyen, “Constructing force- closure grasps,” The International Journal of Robotics Research , vol. 7, no. 3, pp. 3–16, Jun 1988
1988
Earlier work this paper cites.
C. Borst, M. Fischer, and G. Hirzinger, “Grasp planning: how to choose a suitable task wrench space,” in ICRA , 2004
2004
Earlier work this paper cites.
A. Saxena, L. L. S. Wong, and A. Y. Ng, “Learning grasp strategies with partial shape information,” in AAAI , 2008
2008
Earlier work this paper cites.
P. Brook, M. Ciocarlie, and K. Hsiao, “Collaborative grasp planning with multiple object representations,” in ICRA , 2011
2011
Earlier work this paper cites.
A. Herzog, P. Pastor, M. Kalakrishnan, L. Righetti, T. Asfour, and S. Schaal, “Template-based learning of grasp selection,” in ICRA , 2012
2012
Earlier work this paper cites.
I. A. Sucan and S. Chitta, “Moveit!” https://moveit.ros.org/ , 2013
2013
Earlier work this paper cites.
M. Savva, A. X. Chang, and P. Hanrahan, “Semantically-enriched 3d models for common-sense knowledge,” in CVPR-W , 2015
2015
Earlier work this paper cites.
B. Calli, A. Walsman, A. Singh, S. Srinivasa, P. Abbeel, and A. M. Dollar, “Benchmarking in manipulation research: Using the yale-CMU-berkeley object and model set,” IEEE Robotics & Automation Magazine , vol. 22, no. 3, pp. 36–52, Sep 2015
2015
Earlier work this paper cites.
J. Mahler, F. T. Pokorny, B. Hou, M. Roderick, M. Laskey, M. Aubry, K. Kohlhoff, T. Kröger, J. Kuffner, and K. Goldberg, “Dex-net 1.0: A cloud-based network of 3d objects for robust grasp planning using a multi-armed bandit model with correlated rewards,” in ICRA , 2016
2016
Earlier work this paper cites.
L. Yi, V. G. Kim, D. Ceylan, I.-C. Shen, M. Yan, H. Su, C. Lu, Q. Huang, A. Sheffer, and L. Guibas, “A scalable active framework for region annotation in 3d shape collections,” ACM Transactions on Graphics (TOG) , vol. 35, no. 6, Nov. 2016
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,” in RSS , 2017
2017
Earlier work this paper cites.
C. R. Qi, H. Su, K. Mo, and L. J. Guibas, “PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation,” in CVPR , 2017
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,” in NeurIPS , 2017
2017
Earlier work this paper cites.
D. Morrison, P. Corke, and J. Leitner, “Closing the Loop for Robotic Grasping: A Real-time, Generative Grasp Synthesis Approach,” in RSS , 2018
2018
Earlier work this paper cites.
X. Zhou, X. Lan, H. Zhang, Z. Tian, Y. Zhang, and N. Zheng, “Fully convolutional grasp detection network with oriented anchor box,” in IROS , 2018
2018
Cited alongside, same era.
A. Zeng, S. Song, S. Welker, J. Lee, A. Rodriguez, and T. Funkhouser, “Learning synergies between pushing and grasping with self-supervised deep reinforcement learning,” in IROS , 2018
2018
Cited alongside, same era.
X. Yan, J. Hsu, M. Khansari, Y. Bai, A. Pathak, A. Gupta, J. Davidson, and H. Lee, “Learning 6-dof grasping interaction via deep geometry-aware 3d representations,” in ICRA , 2018
2018
Cited alongside, same era.
A. Depierre, E. Dellandréa, and L. Chen, “Jacquard: A large scale dataset for robotic grasp detection,” in IROS , 2018
2018
Cited alongside, same era.
P. Schmidt, N. Vahrenkamp, M. Wächter, and T. Asfour, “Grasping of unknown objects using deep convolutional neural networks based on depth images,” in ICRA , 2018
P. Ni, W. Zhang, X. Zhu, and Q. Cao, “Pointnet++ grasping: Learning an end-to-end spatial grasp generation algorithm from sparse point clouds,” in ICRA , 2020
2020
Later among the works it cites.
A. Murali, A. Mousavian, C. Eppner, C. Paxton, and D. Fox, “6-dof grasping for target-driven object manipulation in clutter,” in ICRA , 2020
2020
Later among the works it cites.
K. Kleeberger, R. Bormann, W. Kraus, and M. F. Huber, “A survey on learning-based robotic grasping,” Curr Robot Rep , vol. 1, p. 239–249, 2020
2020
Later among the works it cites.
I. Lang, A. Manor, and S. Avidan, “SampleNet: Differentiable Point Cloud Sampling,” in CVPR , 2020
2020
Later among the works it cites.
M. Sundermeyer, A. Mousavian, R. Triebel, and D. Fox, “Contact-graspnet: Efficient 6-dof grasp generation in cluttered scenes,” in ICRA , 2021
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
C. Choi, W. Schwarting, J. DelPreto, and D. Rus, “Learning object grasping for soft robot hands,” IEEE RAL , vol. 3, no. 3, pp. 2370–2377, 2018
2018
Cited alongside, same era.
H. Zhang, X. Lan, S. Bai, X. Zhou, Z. Tian, and N. Zheng, “Roi-based robotic grasp detection for object overlapping scenes,” in IROS , 2019
2019
Cited alongside, same era.
A. Mousavian, C. Eppner, and D. Fox, “6-dof graspnet: Variational grasp generation for object manipulation,” in ICCV , 2019
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 ICRA , 2019
2019
Cited alongside, same era.
O. Dovrat, I. Lang, and S. Avidan, “Learning to sample,” in CVPR , 2019
2019
Cited alongside, same era.
C. Eppner, A. Mousavian, and D. Fox, “A billion ways to grasps - an evaluation of grasp sampling schemes on a dense, physics-based grasp data set,” in ISRR , 2019
2019
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,” in NeurIPS , 2020
2020
Cited alongside, same era.
C. Wang, H.-S. Fang, M. Gou, H. Fang, J. Gao, and C. Lu, “Graspness discovery in clutters for fast and accurate grasp detection,” in ICCV , 2021
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 ICRA , 2021
2021
Later among the works it cites.
W. Wei, Y. Luo, F. Li, G. Xu, J. Zhong, W. Li, and P. Wang, “Gpr: Grasp pose refinement network for cluttered scenes,” in ICRA , 2021
2021
Later among the works it cites.
B. Huang, S. D. Han, A. Boularias, and J. Yu, “Dipn: Deep interaction prediction network with application to clutter removal,” in ICRA , 2021
2021
Later among the works it cites.
A. Alliegro, D. Valsesia, G. Fracastoro, E. Magli, and T. Tommasi, “Denoise and contrast for category agnostic shape completion,” in CVPR , 2021
2021
Later among the works it cites.
H. Wang, Q. Liu, X. Yue, J. Lasenby, and M. J. Kusner, “Unsupervised point cloud pre-training via occlusion completion,” in ICCV , 2021
2021
Later among the works it cites.
E. Coumans and Y. Bai, “Pybullet, a python module for physics simulation for games, robotics and machine learning,” http://pybullet.org , 2016–2021
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,” RSS , 2022
2022
Closest in time.