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This paper aims to improve robots' versatility and adaptability by allowing them to use a large variety of end-effector tools and quickly adapt to new tools.
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 2016 IEEE international conference on robotics and automation (ICRA) . IEEE, 2016, pp. 1957–1964
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J. Varley, J. Weisz, J. Weiss, and P. Allen, “Generating multi-fingered robotic grasps via deep learning,” in 2015 IEEE/RSJ international conference on intelligent robots and systems (IROS) . IEEE, 2015, pp. 4415–4420
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M. Gualtieri, A. Ten Pas, K. Saenko, and R. Platt, “High precision grasp pose detection in dense clutter,” in 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2016, pp. 598–605
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Q. Lu, K. Chenna, B. Sundaralingam, and T. Hermans, “Planning multi-fingered grasps as probabilistic inference in a learned deep network,” in Int’l Symp. on Robotics Research , 2017
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A. Zeng, K.-T. Yu, S. Song, D. Suo, E. Walker Jr, A. Rodriguez, and J. Xiao, “Multi-view self-supervised deep learning for 6d pose estimation in the amazon picking challenge,” in Proceedings of the IEEE International Conference on Robotics and Automation , 2017
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J. Varley, C. DeChant, A. Richardson, J. Ruales, and P. Allen, “Shape completion enabled robotic grasping,” in 2017 IEEE/RSJ international conference on intelligent robots and systems (IROS) . IEEE, 2017, pp. 2442–2447
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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,” RSS , 2017
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J. Mahler, M. Matl, X. Liu, A. Li, D. Gealy, and K. Goldberg, “Dex-net 3.0: Computing robust vacuum suction grasp targets in point clouds using a new analytic model and deep learning,” in 2018 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2018, pp. 1–8
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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 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2018
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J. Mahler, M. Matl, V. Satish, M. Danielczuk, B. DeRose, S. McKinley, and K. Goldberg, “Learning ambidextrous robot grasping policies,” Science Robotics , vol. 4, no. 26, p. eaau4984, 2019
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A. Mousavian, C. Eppner, and D. Fox, “6-dof graspnet: Variational grasp generation for object manipulation,” in Proceedings of the IEEE International Conference on Computer Vision , 2019, pp. 2901–2910
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Cited alongside, same era.
B. Calli, A. Singh, J. Bruce, A. Walsman, K. Konolige, S. Srinivasa, P. Abbeel, and A. M. Dollar, “Yale-cmu-berkeley dataset for robotic manipulation research,” The International Journal of Robotics Research , vol. 36, no. 3, pp. 261–268, 2017
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A. Zeng, S. Song, K.-T. Yu, E. Donlon, F. Hogan, M. Bauza, D. Ma, O. Taylor, M. Liu, E. Romo, N. Fazeli, F. Alet, N. Chavan-Dafle, R. Holladay, I. Morona, P. Q. Nair, D. Green, I. Taylor, W. Liu, T. 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 (ICRA) , 2018
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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 2018 IEEE International Conference on Robotics and Automation (ICRA) , 2018, pp. 3766–3773
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2019
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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
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2020
Closest in time.
S. Song, A. Zeng, J. Lee, and T. Funkhouser, “Grasping in the wild: Learning 6dof closed-loop grasping from low-cost demonstrations,” Robotics and Automation Letters , 2020
2020
Closest in time.
A. Murali, A. Mousavian, C. Eppner, C. Paxton, and D. Fox, “6-dof grasping for target-driven object manipulation in clutter,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) , 2020, pp. 6232–6238
2020
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L. Shao, F. Ferreira, M. Jorda, V. Nambiar, J. Luo, E. Solowjow, J. A. Ojea, O. Khatib, and J. Bohg, “Unigrasp: Learning a unified model to grasp with multifingered robotic hands,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 2286–2293, 2020
2020
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