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As the basis for prehensile manipulation, it is vital to enable robots to grasp as robustly as humans.
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
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1996
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A. Bicchi and V. Kumar, “Robotic grasping and contact: A review,” in
2000
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A. Bicchi and V. Kumar, “Robotic grasping and contact: A review,” in
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A. T. Miller, S. Knoop, H. I. Christensen, and P. K. Allen, “Automatic grasp planning using shape primitives,” in
2003
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D. L. Bowers and R. Lumia, “Manipulation of unmodeled objects using intelligent grasping schemes,”
2003
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H. Deubel and W. X. Schneider, “Attentional selection in sequential manual movements, movements around an obstacle and in grasping,” in
2004
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2006
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A. Saxena, J. Driemeyer, and A. Y. Ng, “Robotic grasping of novel objects using vision,”
2008
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A. Saxena, L. L. Wong, and A. Y. Ng, “Learning grasp strategies with partial shape information.” in
2008
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D. Baldauf and H. Deubel, “Attentional landscapes in reaching and grasping,”
2010
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Q. V. Le, D. Kamm, A. F. Kara, and A. Y. Ng, “Learning to grasp objects with multiple contact points,” in
2010
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Y. Jiang, S. Moseson, and A. Saxena, “Efficient grasping from rgbd images: Learning using a new rectangle representation,” in
2011
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2011
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J. Bohg, A. Morales, T. Asfour, and D. Kragic, “Data-driven grasp synthesis—a survey,”
2014
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J. Redmon and A. Angelova, “Real-time grasp detection using convolutional neural networks,”
2014
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S. Kim, A. Shukla, and A. Billard, “Catching objects in flight,”
2014
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A. Menon, B. Cohen, and M. Likhachev, “Motion planning for smooth pickup of moving objects,” in
2014
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D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,”
2014
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I. Lenz, H. Lee, and A. Saxena, “Deep learning for detecting robotic grasps,”
2015
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J. Redmon and A. Angelova, “Real-time grasp detection using convolutional neural networks,” in
2015
Cited alongside, same era.
L. Pinto and A. Gupta, “Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours,” in
2016
Cited alongside, same era.
J. Mahler, J. Liang, S. Niyaz, M. Laskey, R. Doan, X. Liu, J. Aparicio, and K. Goldberg, “Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics,” in
2017
Cited alongside, same era.
A. ten Pas, M. Gualtieri, K. Saenko, and R. Platt, “Grasp pose detection in point clouds,”
2017
Cited alongside, same era.
U. Viereck, A. Pas, K. Saenko, and R. Platt, “Learning a visuomotor controller for real world robotic grasping using simulated depth images,” in
2017
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
2020
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S. Song, A. Zeng, J. Lee, and T. Funkhouser, “Grasping in the wild: Learning 6dof closed-loop grasping from low-cost demonstrations,”
2020
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D. Morrison, P. Corke, and J. Leitner, “Egad! an evolved grasping analysis dataset for diversity and reproducibility in robotic manipulation,”
2020
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P. Khosla, P. Teterwak, C. Wang, A. Sarna, Y. Tian, P. Isola, A. Maschinot, C. Liu, and D. Krishnan, “Supervised contrastive learning,”
2020
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S. Sajjan, M. Moore, M. Pan, G. Nagaraja, J. Lee, A. Zeng, and S. Song, “Clear grasp: 3d shape estimation of transparent objects for manipulation,” in
2020
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J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel, “Domain randomization for transferring deep neural networks from simulation to the real world,” in
2017
Cited alongside, same era.
L.-C. Chen, G. Papandreou, I. Kokkinos, K. Murphy, and A. L. Yuille, “Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs,”
2017
Cited alongside, same era.
M. Guo, D. V. Gealy, J. Liang, J. Mahler, A. Goncalves, S. McKinley, J. A. Ojea, and K. Goldberg, “Design of parallel-jaw gripper tip surfaces for robust grasping,” in
2017
Cited alongside, same era.
F.-J. Chu, R. Xu, and P. A. Vela, “Real-world multiobject, multigrasp detection,”
2018
Cited alongside, same era.
2018
Cited alongside, same era.
S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen, “Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection,”
2018
Cited alongside, same era.
K. Bousmalis, A. Irpan, P. Wohlhart, Y. Bai, M. Kelcey, M. Kalakrishnan, L. Downs, J. Ibarz, P. Pastor, K. Konolige
2018
Cited alongside, same era.
Later among the works it cites.
M. Gou, H.-S. Fang, Z. Zhu, S. Xu, C. Wang, and C. Lu, “Rgb matters: Learning 7-dof grasp poses on monocular rgbd images,” in
2021
Later among the works it cites.
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
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
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
2021
Later among the works it cites.
2021
Later among the works it cites.
I. Akinola, J. Xu, S. Song, and P. K. Allen, “Dynamic grasping with reachability and motion awareness,” in
2021
Later among the works it cites.
W. Yang, C. Paxton, A. Mousavian, Y.-W. Chao, M. Cakmak, and D. Fox, “Reactive human-to-robot handovers of arbitrary objects,” in
2021
Later among the works it cites.
C. Eppner, A. Mousavian, and D. Fox, “Acronym: A large-scale grasp dataset based on simulation,” in
2021
Later among the works it cites.
B. Wen and K. Bekris, “Bundletrack: 6d pose tracking for novel objects without instance or category-level 3d models,” in
2021
Later among the works it cites.
Z. Dong, H. Tian, X. Bao, Y. Yan, and F. Chen, “Graspvdn: scene-oriented grasp estimation by learning vector representations of grasps,”
2022
Closest in time.
R. Newbury, M. Gu, L. Chumbley, A. Mousavian, C. Eppner, J. Leitner, J. Bohg, A. Morales, T. Asfour, D. Kragic, D. Fox, and A. Cosgun, “Deep learning approaches to grasp synthesis: A review,” 2022
2022
Closest in time.
L. Wang, Y. Xiang, W. Yang, A. Mousavian, and D. Fox, “Goal-auxiliary actor-critic for 6d robotic grasping with point clouds,” in
2022
Closest in time.
M. Moosmann, F. Spenrath, J. Rosport, P. Melzer, W. Kraus, R. Bormann, and M. F. Huber, “Transfer learning for machine learning-based detection and separation of entanglements in bin-picking applications,” in
2022
Closest in time.