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Real world data, especially in the domain of robotics, is notoriously costly to collect.
The cross-entropy method: A unified approach to monte carlo simulation, randomized optimization and machine learning
R. Y. Rubinstein and D. P. Kroese · 2004
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Grasping
D. Prattichizzo and J. C. Trinkle · 2008
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Robotic grasping of novel objects using vision
A. Saxena, J. Driemeyer, and A. Y. Ng · 2008
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Collaborative grasp planning with multiple object representations
P. Brook, M. Ciocarlie, and K. Hsiao · 2011
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Domain Adaptation for Object Recognition: An Unsupervised Approach
R. Gopalan, R. Li, and R. Chellappa · 2011
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Multimodal templates for real-time detection of texture-less objects in heavily cluttered scenes
S. Hinterstoisser, S. Holzer, C. Cagniart, S. Ilic, K. Konolige, N. Navab, and V. Lepetit · 2011
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Geodesic flow kernel for unsupervised domain adaptation
B. Gong, Y. Shi, F. Sha, and K. Grauman · 2012
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Imagenet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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From caging to grasping
A. Rodriguez, M. T. Mason, and S. Ferry · 2012
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Cloud-based robot grasping with the google object recognition engine
B. Kehoe, A. Matsukawa, S. Candido, J. Kuffner, and K. Goldberg · 2013
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Data-driven grasp synthesis—a survey
J. Bohg, A. Morales, T. Asfour, and D. Kragic · 2014
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Towards reliable grasping and manipulation in household environments
M. Ciocarlie, K. Hsiao, E. G. Jones, S. Chitta, R. B. Rusu, and I. A. Şucan · 2014
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Generative adversarial nets
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio · 2014
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Beyond the shortest path: Unsupervised Domain Adaptation by Sampling Subspaces Along the Spline Flow
R. Caseiro, J. F. Henriques, P. Martins, and J. Batista · 2015
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ShapeNet: An information-rich 3D model repository
A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
S. Ioffe and C. Szegedy · 2015
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Leveraging big data for grasp planning
D. Kappler, J. Bohg, and S. Schaal · 2015
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Deep learning for detecting robotic grasps
I. Lenz, H. Lee, and A. Saxena · 2015
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Continuous control with deep reinforcement learning
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra · 2015
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Learning transferable features with deep adaptation networks
M. Long and J. Wang · 2015
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Ensemble-cio: Full-body dynamic motion planning that transfers to physical humanoids
I. Mordatch, K. Lowrey, and E. Todorov · 2015
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Visual domain adaptation: A survey of recent advances
V. M. Patel, R. Gopalan, R. Li, and R. Chellappa · 2015
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U-net: Convolutional networks for biomedical image segmentation
O. Ronneberger, P. Fischer, and T. Brox · 2015
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Domain separation networks
K. Bousmalis, G. Trigeorgis, N. Silberman, D. Krishnan, and D. Erhan · 2016
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Domain-adversarial training of neural networks
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky · 2016
Cited alongside, same era.
High precision grasp pose detection in dense clutter
M. Gualtieri, A. ten Pas, K. Saenko, and R. Platt · 2016
Cited alongside, same era.
Team delft’s robot winner of the amazon picking challenge 2016
C. Hernandez, M. Bharatheesha, W. Ko, H. Gaiser, J. Tan, K. van Deurzen, M. de Vries, B. Van Mil, J. van Egmond, R. Burger, et al · 2016
Cited alongside, same era.
3d simulation for robot arm control with deep q-learning
S. James and E. Johns · 2016
Cited alongside, same era.
Autoencoding beyond pixels using a learned similarity metric
A. B. L. Larsen, S. K. Sønderby, H. Larochelle, and O. Winther · 2016
Cited alongside, same era.
Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection
Unsupervised cross-domain image generation
Y. Taigman, A. Polyak, and L. Wolf · 2017
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Grasp pose detection in point clouds
A. ten Pas, M. Gualtieri, K. Saenko, and R. Platt · 2017
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Domain randomization for transferring deep neural networks from simulation to the real world
J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba, and P. Abbeel · 2017
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Learning a visuomotor controller for real world robotic grasping using easily simulated depth images
U. Viereck, A. t. Pas, K. Saenko, and R. Platt · 2017
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Learning a visuomotor controller for real world robotic grasping using simulated depth images
U. Viereck, A. ten Pas, K. Saenko, and R. Platt · 2017
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Dualgan: Unsupervised dual learning for image-to-image translation
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S. Levine, P. Pastor, A. Krizhevsky, and D. Quillen · 2016
Cited alongside, same era.
Asynchronous methods for deep reinforcement learning
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu · 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.
Return of frustratingly easy domain adaptation
B. Sun, J. Feng, and K. Saenko · 2016
Cited alongside, same era.
Adapting deep visuomotor representations with weak pairwise constraints
E. Tzeng, C. Devin, J. Hoffman, C. Finn, P. Abbeel, S. Levine, K. Saenko, and T. Darrell · 2016
Cited alongside, same era.
Instance normalization: The missing ingredient for fast stylization
D. Ulyanov, A. Vedaldi, and V. Lempitsky · 2016
Cited alongside, same era.
Pixel-Level Domain Transfer
D. Yoo, N. Kim, S. Park, A. S. Paek, and I. S. Kweon · 2016
Cited alongside, same era.
Z. Yi, H. R. Zhang, P. Tan, and M. Gong · 2017
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Preparing for the unknown: Learning a universal policy with online system identification
W. Yu, J. Tan, C. K. Liu, and G. Turk · 2017
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Unpaired image-to-image translation using cycle-consistent adversarial networks
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros · 2017
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Using Simulation and Domain Adaptation to Improve Efficiency of Deep Robotic Grasping
K. Bousmalis, A. Irpan, P. Wohlhart, Y. Bai, M. Kelcey, M. Kalakrishnan, L. Downs, J. Ibarz, P. Pastor, K. Konolige, S. Levine, and V. Vanhoucke · 2018
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Closing the sim-to-real loop: Adapting simulation randomization with real world experience
Y. Chebotar, A. Handa, V. Makoviychuk, M. Macklin, J. Issac, N. Ratliff, and D. Fox · 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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Cycada: Cycle-consistent adversarial domain adaptation
J. Hoffman, E. Tzeng, T. Park, J.-Y. Zhu, P. Isola, K. Saenko, A. A. Efros, and T. Darrell · 2018
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Task-embedded control networks for few-shot imitation learning
S. James, M. Bloesch, and A. J. Davison · 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, and S. Levine · 2018
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Sim-to-real reinforcement learning for deformable object manipulation
J. Matas, S. James, and A. J. Davison · 2018
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Cartman: The low-cost cartesian manipulator that won the amazon robotics challenge
D. Morrison, A. W. Tow, M. McTaggart, R. Smith, N. Kelly-Boxall, S. Wade-McCue, J. Erskine, R. Grinover, A. Gurman, T. Hunn, et al · 2018
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Sim-to-real transfer of robotic control with dynamics randomization
X. B. Peng, M. Andrychowicz, W. Zaremba, and P. Abbeel · 2018
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Sim2real viewpoint invariant visual servoing by recurrent control
F. Sadeghi, A. Toshev, E. Jang, and S. Levine · 2018
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A DIRT-t approach to unsupervised domain adaptation
R. Shu, H. Bui, H. Narui, and S. Ermon · 2018
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Genesis-rt: Generating synthetic images for training secondary real-world tasks
G. J. Stein and N. Roy · 2018
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The limits and potentials of deep learning for robotics
N. Sünderhauf, O. Brock, W. Scheirer, R. Hadsell, D. Fox, J. Leitner, B. Upcroft, P. Abbeel, W. Burgard, M. Milford, et al · 2018
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Learning synergies between pushing and grasping with self-supervised deep reinforcement learning
A. Zeng, S. Song, S. Welker, J. Lee, A. Rodriguez, and T. Funkhouser · 2018
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Vr goggles for robots: Real-to-sim domain adaptation for visual control
J. Zhang, L. Tai, Y. Xiong, M. Liu, J. Boedecker, and W. Burgard · 2019
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