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Planar grasp detection is one of the most fundamental tasks to robotic manipulation, and the recent progress of consumer-grade RGB-D sensors enables delivering more comprehensive features from both the texture and shape modalities.
V. Nguyen, “Constructing force-closure grasps,” Int. J. Robotics Res. , vol. 7, no. 3, pp. 3–16, 1988
1988
Earlier work this paper cites.
A. Bicchi and V. Kumar, “Robotic grasping and contact: A review,” in IEEE International Conference on Robotics and Automation , 2000, pp. 348–353
2000
Earlier work this paper cites.
A. Saxena, J. Driemeyer, and A. Y. Ng, “Robotic grasping of novel objects using vision,” Int. J. Robotics Res. , vol. 27, no. 2, pp. 157–173, 2008
2008
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 , 2011, pp. 3304–3311
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems , 2012, pp. 1106–1114
2012
Earlier work this paper cites.
D. Buchholz, M. Futterlieb, S. Winkelbach, and F. M. Wahl, “Efficient bin-picking and grasp planning based on depth data,” in IEEE International Conference on Robotics and Automation , 2013, pp. 3245–3250
2013
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 , 2015, pp. 1316–1322
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 , 2015, pp. 91–99
2015
Earlier work this paper cites.
B. Çalli, A. Walsman, A. Singh, S. S. Srinivasa, P. Abbeel, and A. M. Dollar, “Benchmarking in manipulation research: Using the yale-cmu-berkeley object and model set,” IEEE Robotics Autom. Mag. , vol. 22, no. 3, pp. 36–52, 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 , 2016, pp. 3406–3413
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 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,” in Robotics: Science and Systems , 2017
2017
Earlier work this paper cites.
D. Guo, F. Sun, H. Liu, T. Kong, B. Fang, and N. Xi, “A hybrid deep architecture for robotic grasp detection,” in IEEE International Conference on Robotics and Automation , 2017, pp. 1609–1614
2017
Earlier work this paper cites.
S. Kumra and C. Kanan, “Robotic grasp detection using deep convolutional neural networks,” in IEEE International Conference on Intelligent Robots and Systems , 2017, pp. 769–776
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems , 2017, pp. 5998–6008
2017
Cited alongside, same era.
F. Chu, R. Xu, and P. A. Vela, “Real-world multiobject, multigrasp detection,” IEEE Robotics Autom. Lett. , vol. 3, no. 4, pp. 3355–3362, 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 , 2018
2018
Cited alongside, same era.
X. Zhou, X. Lan, H. Zhang, Z. Tian, Y. Zhang, and N. Zheng, “Fully convolutional grasp detection network with oriented anchor box,” in IEEE International Conference on Intelligent Robots and Systems , 2018, pp. 7223–7230
2018
Cited alongside, same era.
S. Kumra, S. Joshi, and F. Sahin, “Antipodal robotic grasping using generative residual convolutional neural network,” in IEEE International Conference on Intelligent Robots and Systems , 2020, pp. 9626–9633
2020
Later among the works it cites.
D. Morrison, P. Corke, and J. Leitner, “Learning robust, real-time, reactive robotic grasping,” Int. J. Robotics Res. , vol. 39, no. 2-3, 2020
2020
Later among the works it cites.
W. Wang, W. Liu, J. Hu, Y. Fang, Q. Shao, and J. Qi, “Graspfusionnet: a two-stage multi-parameter grasp detection network based on RGB-XYZ fusion in dense clutter,” Mach. Vis. Appl. , vol. 31, no. 7, p. 58, 2020
2020
Later among the works it cites.
S. Chen and Y. Fu, “Progressively guided alternate refinement network for RGB-D salient object detection,” in European Conference on Computer Vision , 2020, pp. 520–538
2020
Later among the works it cites.
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A. Zeng, S. Song, K. Yu, E. Donlon, F. R. Hogan, M. Bauzá, D. Ma, O. Taylor, M. Liu, E. Romo, N. Fazeli, F. Alet, N. C. Dafle, R. Holladay, I. Morona, P. Q. Nair, D. Green, I. J. Taylor, W. Liu, T. A. Funkhouser, and A. Rodriguez, “Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching,” in IEEE International Conference on Robotics and Automation , 2018, pp. 1–8
2018
Cited alongside, same era.
J. Ma, W. Shao, H. Ye, L. Wang, H. Wang, Y. Zheng, and X. Xue, “Arbitrary-oriented scene text detection via rotation proposals,” IEEE Trans. Multim. , vol. 20, no. 11, pp. 3111–3122, 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,” Int. J. Robotics Res. , vol. 37, no. 4-5, pp. 421–436, 2018
2018
Cited alongside, same era.
A. Depierre, E. Dellandréa, and L. Chen, “Jacquard: A large scale dataset for robotic grasp detection,” in IEEE International Conference on Intelligent Robots and Systems , 2018, pp. 3511–3516
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 IEEE International Conference on Intelligent Robots and Systems , 2019, pp. 4768–4775
2019
Cited alongside, same era.
V. Satish, J. Mahler, and K. Goldberg, “On-policy dataset synthesis for learning robot grasping policies using fully convolutional deep networks,” IEEE Robotics Autom. Lett. , vol. 4, no. 2, pp. 1357–1364, 2019
2019
Cited alongside, same era.
A. Gariépy, J. Ruel, B. Chaib-draa, and P. Giguère, “GQ-STN: optimizing one-shot grasp detection based on robustness classifier,” in IEEE International Conference on Intelligent Robots and Systems , 2019, pp. 3996–4003
2019
Cited alongside, same era.
P. Ramachandran, N. Parmar, A. Vaswani, I. Bello, A. Levskaya, and J. Shlens, “Stand-alone self-attention in vision models,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Cited alongside, same era.
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 , 2020, pp. 11 441–11 450
2020
Later among the works it cites.
H. Zhu, Y. Li, F. Bai, W. Chen, X. Li, J. Ma, C. S. Teo, P. Y. Tao, and W. Lin, “Grasping detection network with uncertainty estimation for confidence-driven semi-supervised domain adaptation,” in IEEE International Conference on Intelligent Robots and Systems , 2020, pp. 9608–9613
2020
Later among the works it cites.
Y. Wang, Y. Zheng, B. Gao, and D. Huang, “Double-dot network for antipodal grasp detection,” in IEEE International Conference on Intelligent Robots and Systems , 2021, pp. 4654–4661
2021
Later among the works it cites.
W. Ji, J. Li, S. Yu, M. Zhang, Y. Piao, S. Yao, Q. Bi, K. Ma, Y. Zheng, H. Lu, and L. Cheng, “Calibrated RGB-D salient object detection,” in IEEE Conference on Computer Vision and Pattern Recognition , 2021, pp. 9471–9481
2021
Later among the works it cites.
A. Depierre, E. Dellandréa, and L. Chen, “Scoring graspability based on grasp regression for better grasp prediction,” in IEEE International Conference on Robotics and Automation , 2021, pp. 4370–4376
2021
Later among the works it cites.
S. Ainetter and F. Fraundorfer, “End-to-end trainable deep neural network for robotic grasp detection and semantic segmentation from RGB,” in IEEE International Conference on Robotics and Automation , 2021, pp. 13 452–13 458
2021
Later among the works it cites.
H. Ma and D. Huang, “Towards scale balanced 6-dof grasp detection in cluttered scenes,” in Conference on Robot Learning , 2022
2022
Later among the works it cites.
Y. Song, J. Wen, D. Liu, and C. Yu, “Deep robotic grasping prediction with hierarchical rgb-d fusion,” International Journal of Control, Automation and Systems , vol. 20, no. 1, pp. 243–254, 2022
2022
Later among the works it cites.
H. Zhang, D. Yang, H. Wang, B. Zhao, X. Lan, J. Ding, and N. Zheng, “REGRAD: A large-scale relational grasp dataset for safe and object-specific robotic grasping in clutter,” IEEE Robotics Autom. Lett. , vol. 7, no. 2, pp. 2929–2936, 2022
2022
Later among the works it cites.