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This paper considers the problem of retrieving an object from many tightly packed objects using a combination of robotic pushing and grasping actions.
1903
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1904
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1911
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1912
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A. Bicchi and V. Kumar, “Robotic grasping and contact: A review.” IEEE International Conference on Robotics and Automation, 2000
2000
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A. Boularias, O. Kroemer, and J. Peters, “Learning robot grasping from 3-d images with markov random fields,” in 2011 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2011, San Francisco, CA, USA, September 25-30, 2011 , 2011, pp. 1548–1553. http://dx.doi.org/10.1109/IROS.2011.6094888
2011
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A. Rodriguez, M. T. Mason, and S. Ferry, “From caging to grasping,” The International Journal of Robotics Research , vol. 31, no. 7, pp. 886–900, 2012
2012
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L. Chang, J. R. Smith, and D. Fox, “Interactive singulation of objects from a pile,” in 2012 IEEE International Conference on Robotics and Automation , 2012, pp. 3875–3882
2012
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C. B. Browne, E. Powley, D. Whitehouse, S. M. Lucas, P. I. Cowling, P. Rohlfshagen, S. Tavener, D. Perez, S. Samothrakis, and S. Colton, “A survey of monte carlo tree search methods,” IEEE Transactions on Computational Intelligence and AI in Games , vol. 4, no. 1, pp. 1–43, 2012
2012
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E. Rohmer, S. P. N. Singh, and M. Freese, “V-rep: A versatile and scalable robot simulation framework,” in 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems , 2013, pp. 1321–1326
2013
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J. Bohg, A. Morales, T. Asfour, and D. Kragic, “Data-driven grasp synthesis—a survey,” Trans. Rob. , vol. 30, no. 2, p. 289–309, Apr. 2014
2014
Earlier work this paper cites.
A. Boularias, J. A. Bagnell, and A. Stentz, “Efficient optimization for autonomous robotic manipulation of natural objects,” in Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence, July 27 -31, 2014, Québec City, Québec, Canada. , 2014, pp. 2520–2526. http://www.aaai.org/ocs/index.php/AAAI/AAAI14/paper/view/8414
2014
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V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al. , “Human-level control through deep reinforcement learning,” nature , vol. 518, no. 7540, pp. 529–533, 2015
2015
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M. Laskey, J. Lee, C. Chuck, D. Gealy, W. Hsieh, F. T. Pokorny, A. D. Dragan, and K. Goldberg, “Robot grasping in clutter: Using a hierarchy of supervisors for learning from demonstrations,” in 2016 IEEE International Conference on Automation Science and Engineering (CASE) , 2016, pp. 827–834
2016
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K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 770–778
2016
Cited alongside, same era.
2016
Cited alongside, same era.
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,” 2017
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Y. Xiao, S. Katt, A. t. Pas, S. Chen, and C. Amato, “Online planning for target object search in clutter under partial observability,” in International Conference on Robotics and Automation , 2019
2019
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 , 2016–2019
2019
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D. Hafner, T. Lillicrap, J. Ba, and M. Norouzi, “Dream to control: Learning behaviors by latent imagination,” in International Conference on Learning Representations , 2020. https://openreview.net/forum?id=S1lOTC4tDS
2020
Later among the works it cites.
C. Gabellieri, F. Angelini, V. Arapi, A. Palleschi, M. G. Catalano, G. Grioli, L. Pallottino, A. Bicchi, M. Bianchi, and M. Garabini, “Grasp it like a pro: Grasp of unknown objects with robotic hands based on skilled human expertise,” IEEE Robotics and Automation Letters , vol. 5, no. 2, pp. 2808–2815, 2020
2020
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M. Andrychowicz, F. Wolski, A. Ray, J. Schneider, R. Fong, P. Welinder, B. McGrew, J. Tobin, O. Pieter Abbeel, and W. Zaremba, “Hindsight experience replay,” in Advances in Neural Information Processing Systems , I. Guyon, U. V. Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, Eds., vol. 30, 2017. https://proceedings.neurips.cc/paper/2017/file/453fadbd8a1a3af50a9df4df899537b5-Paper.pdf
2017
Cited alongside, same era.
2018
Cited alongside, same era.
Muhayyuddin, M. Moll, L. Kavraki, and J. Rosell, “Randomized physics-based motion planning for grasping in cluttered and uncertain environments,” IEEE Robotics and Automation Letters , vol. 3, no. 2, pp. 712–719, April 2018
2018
Cited alongside, same era.
D. Kalashnikov, A. Irpan, P. Pastor, J. Ibarz, A. Herzog, E. Jang, D. Quillen, E. Holly, M. Kalakrishnan, V. Vanhoucke, and S. Levine, “Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation,” 2018
2018
Cited alongside, same era.
2018
Cited alongside, same era.
M. Danielczuk, J. Mahler, C. Correa, and K. Goldberg, “Linear push policies to increase grasp access for robot bin picking,” in 2018 IEEE 14th International Conference on Automation Science and Engineering (CASE) , 2018, pp. 1249–1256
2018
Cited alongside, same era.
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
Cited alongside, same era.
A. Mousavian, C. Eppner, and D. Fox, “6-dof graspnet: Variational grasp generation for object manipulation,” in 2019 IEEE/CVF International Conference on Computer Vision, ICCV 2019, Seoul, Korea (South), October 27 - November 2, 2019 , 2019, pp. 2901–2910. https://doi.org/10.1109/ICCV.2019.00299
2019
Cited alongside, same era.
Later among the works it cites.
Q. Lu, M. Van der Merwe, B. Sundaralingam, and T. Hermans, “Multifingered grasp planning via inference in deep neural networks: Outperforming sampling by learning differentiable models,” IEEE Robotics & Automation Magazine , vol. 27, no. 2, pp. 55–65, 2020
2020
Later among the works it cites.
H.-S. Fang, C. Wang, M. Gou, and C. Lu, “Graspnet-1billion: A large-scale benchmark for general object grasping,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition , 2020, pp. 11 444–11 453
2020
Later among the works it cites.
A. Eitel, N. Hauff, and W. Burgard, “Learning to singulate objects using a push proposal network,” in Robotics Research , N. M. Amato, G. Hager, S. Thomas, and M. Torres-Torriti, Eds. Cham: Springer International Publishing, 2020, pp. 405–419
2020
Later among the works it cites.
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) . IEEE, 2020, pp. 6232–6238
2020
Later among the works it cites.
A. Kurenkov, J. Taglic, R. Kulkarni, M. Dominguez-Kuhne, R. Martín-Martín, A. Garg, and S. Savarese, “Visuomotor mechanical search: Learning to retrieve target objects in clutter,” in IEEE/RSJ Int. Conference. on Intelligent Robots and Systems (IROS) , 2020
2020
Later among the works it cites.
L. Yen-Chen, A. Zeng, S. Song, P. Isola, and T.-Y. Lin, “Learning to see before learning to act: Visual pre-training for manipulation,” in 2020 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 2020, pp. 7286–7293
2020
Later among the works it cites.
J. Schrittwieser, I. Antonoglou, T. Hubert, K. Simonyan, L. Sifre, S. Schmitt, A. Guez, E. Lockhart, D. Hassabis, T. Graepel, et al. , “Mastering atari, go, chess and shogi by planning with a learned model,” Nature , vol. 588, no. 7839, pp. 604–609, 2020
2020
Later among the works it cites.
B. Huang, S. D. Han, A. Boularias, and J. Yu, “Dipn: Deep interaction prediction network with application to clutter removal,” in 2021 IEEE International Conference on Robotics and Automation (ICRA) , 2021
2021
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