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Object grasping in cluttered scenes is a widely investigated field of robot manipulation.
V. Nguyen, “Constructing force-closure grasps,” The International Journal of Robotics Research (IJRR) , 1988
1988
Earlier work this paper cites.
C. Ferrari and J. F. Canny, “Planning optimal grasps,” in IEEE International Conference on Robotics and Automation (ICRA) , 1992
1992
Earlier work this paper cites.
A. Bicchi and V. Kumar, “Robotic grasping and contact: A review,” in IEEE International Conference on Robotics and Automation (ICRA) , 2000
2000
Earlier work this paper cites.
A. T. Miller and P. K. Allen, “Graspit! A versatile simulator for robotic grasping,” IEEE Robotics & Automation Magazine , 2004
2004
Earlier work this paper cites.
A. Collet, M. Martinez, and S. S. Srinivasa, “The moped framework: Object recognition and pose estimation for manipulation,” The international journal of robotics research (IJRR) , 2011
2011
Earlier work this paper cites.
Y. Jiang, S. Moseson, and A. Saxena, “Efficient grasping from rgbd images: Learning using a new rectangle representation,” in IEEE International conference on robotics and automation (ICRA) , 2011
2011
Earlier work this paper cites.
M.-Y. Liu, O. Tuzel, A. Veeraraghavan, Y. Taguchi, T. K. Marks, and R. Chellappa, “Fast object localization and pose estimation in heavy clutter for robotic bin picking,” The International Journal of Robotics Research (IJRR) , 2012
2012
Earlier work this paper cites.
E. Todorov, T. Erez, and Y. Tassa, “Mujoco: A physics engine for model-based control,” in 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems , 2012
2012
Earlier work this paper cites.
J. Bohg, A. Morales, T. Asfour, and D. Kragic, “Data-driven grasp synthesis - A survey,” IEEE Transactions on Robotics (TRO) , 2014
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 (IJRR) , 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 IEEE International conference on advanced robotics (ICAR) , 2015
2015
Earlier work this paper cites.
J. Redmon and A. Angelova, “Real-time grasp detection using convolutional neural networks,” in IEEE International Conference on Robotics and Automation (ICRA) , 2015
2015
Earlier work this paper cites.
S. Ren, K. He, R. B. Girshick, and J. Sun, “Faster R-CNN: towards real-time object detection with region proposal networks,” in Advances in neural information processing systems (NIPS) , 2015
2015
Earlier work this paper cites.
L. Pinto and A. Gupta, “Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours,” in IEEE international conference on robotics and automation (ICRA) , 2016
2016
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 IEEE international conference on robotics and automation (ICRA) , 2016
2016
Earlier work this paper cites.
A. Zeng, K.-T. Yu, S. Song, D. Suo, E. Walker, A. Rodriguez, and J. Xiao, “Multi-view self-supervised deep learning for 6d pose estimation in the amazon picking challenge,” in IEEE international conference on robotics and automation (ICRA) , 2017
2017
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 Robotics: Science and Systems (RSS) , 2017
2017
Cited alongside, same era.
A. ten Pas, M. Gualtieri, K. Saenko, and R. Platt, “Grasp pose detection in point clouds,” The International Journal of Robotics Research (IJRR) , 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 IEEE conference on computer vision and pattern recognition (CVPR) , 2017
2017
Cited alongside, same era.
S. Kumra and C. Kanan, “Robotic grasp detection using deep convolutional neural networks,” in IEEE International Conference on Intelligent Robots and Systems (IROS) , 2017
2017
Cited alongside, same era.
C. Wang, D. Xu, Y. Zhu, R. Martín-Martín, C. Lu, L. Fei-Fei, and S. Savarese, “Densefusion: 6d object pose estimation by iterative dense fusion,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019
2019
Later among the works it cites.
H. Zhang, X. Lan, S. Bai, X. Zhou, Z. Tian, and N. Zheng, “Roi-based robotic grasp detection for object overlapping scenes,” in IEEE International Conference on Intelligent Robots and Systems (IROS) , 2019
2019
Later among the works it cites.
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 IEEE International Conference on Robotics and Automation (ICRA) , 2019
2019
Later among the works it cites.
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 (CoRL) , 2019
2019
Later among the works it cites.
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C. R. Qi, L. Yi, H. Su, and L. J. Guibas, “Pointnet++: Deep hierarchical feature learning on point sets in a metric space,” in Advances in neural information processing systems (NIPS) , 2017
2017
Cited alongside, same era.
T.-Y. Lin, P. Goyal, R. Girshick, K. He, and P. Dollár, “Focal loss for dense object detection,” in IEEE international conference on computer vision (ICCV) , 2017
2017
Cited alongside, same era.
W. Kehl, F. Manhardt, F. Tombari, S. Ilic, and N. Navab, “SSD-6D: making rgb-based 3d detection and 6d pose estimation great again,” in IEEE International Conference on Computer Vision (ICCV) , 2017
2017
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,” in Robotics: Science and Systems (RSS) , 2018
2018
Cited alongside, same era.
F. Chu, R. Xu, and P. A. Vela, “Real-world multiobject, multigrasp detection,” IEEE Robotics and Automation Letters (RAL) , 2018
2018
Cited alongside, same era.
D. Morrison, J. Leitner, and P. Corke, “Closing the loop for robotic grasping: A real-time, generative grasp synthesis approach,” in Robotics: Science and Systems (RSS) , 2018
2018
Cited alongside, same era.
S. Caldera, A. Rassau, and D. Chai, “Review of deep learning methods in robotic grasp detection,” Multimodal Technologies and Interaction , 2018
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,” The International Journal of Robotics Research (IJRR) , 2018
2018
Cited alongside, same era.
A. Mousavian, C. Eppner, and D. Fox, “6-dof graspnet: Variational grasp generation for object manipulation,” in IEEE International Conference on Computer Vision (ICCV) , 2019
2019
Later among the works it cites.
S. Brahmbhatt, A. Handa, J. Hays, and D. Fox, “Contactgrasp: Functional multi-finger grasp synthesis from contact,” in IEEE International Conference on Intelligent Robots and Systems (IROS) , 2019
2019
Later among the works it cites.
2019
Later among the works it cites.
W. Wu, Z. Qi, and F. Li, “Pointconv: Deep convolutional networks on 3d point clouds,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019
2019
Later among the works it cites.
H. Thomas, C. R. Qi, J. Deschaud, B. Marcotegui, F. Goulette, and L. J. Guibas, “Kpconv: Flexible and deformable convolution for point clouds,” in IEEE International Conference on Computer Vision (ICCV) , 2019
2019
Later among the works it cites.
Y. Liu, B. Fan, G. Meng, J. Lu, S. Xiang, and C. Pan, “Densepoint: Learning densely contextual representation for efficient point cloud processing,” in IEEE International Conference on Computer Vision (ICCV) , 2019
2019
Later among the works it cites.
S. Shi, X. Wang, and H. Li, “Pointrcnn: 3d object proposal generation and detection from point cloud,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2019
2019
Later among the works it cites.
C. R. Qi, O. Litany, K. He, and L. J. Guibas, “Deep hough voting for 3d object detection in point clouds,” in IEEE International Conference on Computer Vision (ICCV) , 2019
2019
Later among the works it cites.
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 IEEE International Conference on Robotics and Automation (ICRA) , 2020
2020
Later among the works it cites.
H. Fang, C. Wang, M. Gou, and C. Lu, “Graspnet-1billion: A large-scale benchmark for general object grasping,” in IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , 2020
2020
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 , 2016–2020
2020
Later among the works it cites.