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Data-driven approaches to solving robotic tasks have gained a lot of traction in recent years.
Maximum likelihood from incomplete data via the em algorithm
Arthur P Dempster, Nan M Laird, and Donald B Rubin · 1977
Earlier work this paper cites.
Constructing force-closure grasps
Van-Duc Nguyen · 1988
Earlier work this paper cites.
Robotic grasping and contact: a review
Antonio Bicchi and Vijay Kumar · 2000
Earlier work this paper cites.
Context dependent pre-trained deep neural networks for large vocabulary speech recognition
George Dahl, Dong Yu, Li Deng, and Alex Acero · 2010
Earlier work this paper cites.
A study of the effect of different types of noise on the precision of supervised learning techniques
David F Nettleton, Albert Orriols-Puig, and Albert Fornells · 2010
Earlier work this paper cites.
Learning to control a low-cost manipulator using data-efficient reinforcement learning
Marc Peter Deisenroth, Carl Edward Rasmussen, and Dieter Fox · 2011
Earlier work this paper cites.
Adam: A method for stochastic optimization
Diederik P Kingma and Jimmy Ba · 2014
Earlier work this paper cites.
Data-driven grasp synthesis—a survey
Jeannette Bohg, Antonio Morales, Tamim Asfour, and Danica Kragic · 2014
Earlier work this paper cites.
Classification in the presence of label noise: a survey
Benoît Frénay and Michel Verleysen · 2014
Earlier work this paper cites.
Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
Lerrel Pinto and Abhinav Gupta · 2015
Earlier work this paper cites.
Deep learning for detecting robotic grasps
Ian Lenz, Honglak Lee, and Ashutosh Saxena · 2015
Cited alongside, same era.
Learning from massive noisy labeled data for image classification
Tong Xiao, Tian Xia, Yi Yang, Chang Huang, and Xiaogang Wang · 2015
Cited alongside, same era.
Learning to poke by poking: Experiential learning of intuitive physics
Pulkit Agarwal, Ashwin Nair, Pieter Abbeel, Jitendra Malik, and Sergey Levine · 2016
Cited alongside, same era.
Unsupervised learning for physical interaction through video prediction
Chelsea Finn, Ian Goodfellow, and Sergey Levine · 2016
Cited alongside, same era.
Seeing through the human reporting bias:visual classifiers from noisy human-centric labels
Ishan Misra, Lawrence Zitnick, Margaret Mitchell, and Ross Girshick · 2016
Cited alongside, same era.
Combining self-supervised learning and imitation for vision-based rope manipulation
Ashvin Nair, Dian Chen, Pulkit Agrawal, Phillip Isola, Pieter Abbeel, Jitendra Malik, and Sergey Levine · 2017
Later among the works it cites.
Revisiting unreasonable effectiveness of data in deep learning era
Chen Sun, Abhinav Shrivastava, Saurabh Singh, and Abhinav Gupta · 2017
Later among the works it cites.
Automatic differentiation in pytorch
Adam Paszke, Sam Gross, Soumith Chintala, Gregory Chanan, Edward Yang, Zachary DeVito, Zeming Lin, Alban Desmaison, Luca Antiga, and Adam Lerer · 2017
Later among the works it cites.
Dex-net 2.0: Deep learning to plan robust grasps with synthetic point clouds and analytic grasp metrics
Jeffrey Mahler, Jacky Liang, Sherdil Niyaz, Michael Laskey, Richard Doan, Xinyu Liu, Juan Aparicio Ojea, and Ken Goldberg · 2017
Later among the works it cites.
Learning to push by grasping: Using multiple tasks for effective learning
Lerrel Pinto and Abhinav Gupta · 2017
Later among the works it cites.
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
Cited alongside, same era.
Yolo9000: Better, faster, stronger
Joseph Redmon and Ali Farhadi · 2016
Cited alongside, same era.
Dex-net 1.0: A cloud-based network of 3d objects for robust grasp planning using a multi-armed bandit model with correlated rewards
Jeffrey Mahler, Florian T Pokorny, Brian Hou, Melrose Roderick, Michael Laskey, Mathieu Aubry, Kai Kohlhoff, Torsten Kröger, James Kuffner, and Ken Goldberg · 2016
Cited alongside, same era.
Domain randomization for transferring deep neural networks from simulation to the real world
Josh Tobin, Rachel Fong, Alex Ray, Jonas Schneider, Wojciech Zaremba, and Pieter Abbeel · 2017
Cited alongside, same era.
Sim-to-real transfer of robotic control with dynamics randomization
Xue Bin Peng, Marcin Andrychowicz, Wojciech Zaremba, and Pieter Abbeel · 2017
Cited alongside, same era.
CASSL: Curriculum accelerated self-supervised learning
Adithyavairavan Murali, Lerrel Pinto, Dhiraj Gandhi, and Abhinav Gupta
Cited in the paper.
End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel
Cited in the paper.
Collective robot reinforcement learning with distributed asynchronous guided policy search
Ali Yahya, Adrian Li, Mrinal Kalakrishnan, Yevgen Chebotar, and Sergey Levine · 2017
Later among the works it cites.
Semi-supervised haptic material recognition for robots using generative adversarial networks
Zackory Erickson, Sonia Chernova, and Charles C Kemp · 2017
Later among the works it cites.
The feeling of success: Does touch sensing help predict grasp outcomes?
Roberto Calandra, Andrew Owens, Manu Upadhyaya, Wenzhen Yuan, Justin Lin, Edward Adelson, and Sergey Levine · 2017
Later among the works it cites.
Asymmetric actor critic for image-based robot learning
Lerrel Pinto, Marcin Andrychowicz, Peter Welinder, Wojciech Zaremba, and Pieter Abbeel · 2018
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