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Well structured visual representations can make robot learning faster and can improve generalization.
Learning monocular reactive uav control in cluttered natural environments
S. Ross, N. Melik-Barkhudarov, K. S. Shankar, A. Wendel, D. Dey, J. A. Bagnell, and M. Hebert · 2013
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Distributed representations of words and phrases and their compositionality
T. Mikolov, I. Sutskever, K. Chen, G. S. Corrado, and J. Dean · 2013
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Microsoft coco: Common objects in context
T.-Y. Lin, M. Maire, S. Belongie, J. Hays, P. Perona, D. Ramanan, P. Dollár, and C. L. Zitnick · 2014
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Learning image representations tied to ego-motion
D. Jayaraman and K. Grauman · 2015
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Learning state representations with robotic priors
R. Jonschkowski and O. Brock · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
A. Radford, L. Metz, and S. Chintala · 2015
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Deep learning for detecting robotic grasps
I. Lenz, H. Lee, and A. Saxena · 2015
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Imagenet large scale visual recognition challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, et al · 2015
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End to end learning for self-driving cars
M. Bojarski, D. Del Testa, D. Dworakowski, B. Firner, B. Flepp, P. Goyal, L. D. Jackel, M. Monfort, U. Muller, J. Zhang, et al · 2016
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Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
S. Levine, P. Pastor, A. Krizhevsky, and D. Quillen · 2016
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Learning to poke by poking: Experiential learning of intuitive physics
P. Agrawal, A. V. Nair, P. Abbeel, J. Malik, and S. Levine · 2016
Cited alongside, same era.
The curious robot: Learning visual representations via physical interactions
L. Pinto, D. Gandhi, Y. Han, Y.-L. Park, and A. Gupta · 2016
Cited alongside, same era.
Actions˜ transformations
X. Wang, A. Farhadi, and A. Gupta · 2016
Cited alongside, same era.
Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
L. Pinto and A. Gupta · 2016
Cited alongside, same era.
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
Cited alongside, same era.
Improved deep metric learning with multi-class n-pair loss objective
K. Sohn · 2016
Cited alongside, same era.
Grasp pose detection in point clouds
A. ten Pas, M. Gualtieri, K. Saenko, and R. Platt · 2017
Later among the works it cites.
End-to-end learning of semantic grasping
E. Jang, S. Vijaynarasimhan, P. Pastor, J. Ibarz, and S. Levine · 2017
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Multi-task domain adaptation for deep learning of instance grasping from simulation
K. Fang, Y. Bai, S. Hinterstoisser, and M. Kalakrishnan · 2017
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Hindsight experience replay
M. Andrychowicz, F. Wolski, A. Ray, J. Schneider, R. Fong, P. Welinder, B. McGrew, J. Tobin, O. Pieter Abbeel, and W. Zaremba · 2017
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The intentional unintentional agent: Learning to solve many continuous control tasks simultaneously
S. Cabi, S. G. Colmenarejo, M. W. Hoffman, M. Denil, Z. Wang, and N. De Freitas · 2017
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Deep predictive policy training using reinforcement learning
A. Ghadirzadeh, A. Maki, D. Kragic, and M. Björkman · 2017
Cited alongside, same era.
Self-supervised visual planning with temporal skip connections
F. Ebert, C. Finn, A. X. Lee, and S. Levine · 2017
Cited alongside, same era.
Multi-view self-supervised deep learning for 6d pose estimation in the amazon picking challenge
A. Zeng, K.-T. Yu, S. Song, D. Suo, E. Walker, A. Rodriguez, and J. Xiao · 2017
Cited alongside, same era.
Later among the works it cites.
Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
J. Johnson, B. Hariharan, L. van der Maaten, L. Fei-Fei, C. L. Zitnick, and R. Girshick · 2017
Later among the works it cites.
Dense object nets: Learning dense visual object descriptors by and for robotic manipulation
P. R. Florence, L. Manuelli, and R. Tedrake · 2018
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Qt-opt: Scalable deep reinforcement learning for vision-based robotic manipulation
D. Kalashnikov, A. Irpan, P. Pastor, J. Ibarz, A. Herzog, E. Jang, D. Quillen, E. Holly, M. Kalakrishnan, V. Vanhoucke, et al · 2018
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Pybullet, a python module for physics simulation for games, robotics and machine learning
E. Coumans and Y. Bai · 2018
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