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Grasping objects is one of the most important abilities that a robot needs to master in order to interact with its environment.
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J. Bohg and D. Kragic, “Learning grasping points with shape context,” Robotics and Autonomous Systems , vol. 58, no. 4, pp. 362–377, 2010
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A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in Neural Information Processing Systems 25 , F. Pereira, C. J. C. Burges, L. Bottou, and K. Q. Weinberger, Eds. Curran Associates, Inc., 2012, pp. 1097–1105
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J. Redmon and A. Angelova, “Real-time grasp detection using convolutional neural networks,” in Robotics and Automation (ICRA), 2015 IEEE International Conference on . IEEE, 2015, pp. 1316–1322
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I. Lenz, H. Lee, and A. Saxena, “Deep learning for detecting robotic grasps,” The International Journal of Robotics Research , vol. 34, no. 4-5, pp. 705–724, 2015
2015
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2015
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S. Ren, K. He, R. 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
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L. Pinto and A. Gupta, “Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours,” in 2016 IEEE international conference on robotics and automation (ICRA) . IEEE, 2016, pp. 3406–3413
2016
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W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in European conference on computer vision . Springer, 2016, pp. 21–37
2016
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S. Kumra and C. Kanan, “Robotic grasp detection using deep convolutional neural networks,” in 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2017, pp. 769–776
2017
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J. Redmon and A. Farhadi, “Yolo9000: better, faster, stronger,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 7263–7271
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X. Zhou, X. Lan, H. Zhang, Z. Tian, Y. Zhang, and N. Zheng, “Fully convolutional grasp detection network with oriented anchor box,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 7223–7230
2018
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F.-J. Chu, R. Xu, and P. A. Vela, “Real-world multiobject, multigrasp detection,” IEEE Robotics and Automation Letters , vol. 3, no. 4, pp. 3355–3362, 2018
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J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 779–788
2016
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D. Guo, F. Sun, H. Liu, T. Kong, B. Fang, and N. Xi, “A hybrid deep architecture for robotic grasp detection,” in 2017 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2017, pp. 1609–1614
2017
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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) , 2017
2017
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2017
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A. Depierre, E. Dellandréa, and L. Chen, “Jacquard: A large scale dataset for robotic grasp detection,” in 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2018, pp. 3511–3516
2018
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D. Morrison, P. Corke, and J. Leitner, “Learning robust, real-time, reactive robotic grasping,” The International Journal of Robotics Research , p. 0278364919859066, 2019
2019
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S. James, P. Wohlhart, M. Kalakrishnan, D. Kalashnikov, A. Irpan, J. Ibarz, S. Levine, R. Hadsell, and K. Bousmalis, “Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to-canonical adaptation networks,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 12 627–12 637
2019
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V. Satish, J. Mahler, and K. Goldberg, “On-policy dataset synthesis for learning robot grasping policies using fully convolutional deep networks,” IEEE Robotics and Automation Letters , vol. 4, no. 2, pp. 1357–1364, 2019
2019
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