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Instrumenting and collecting annotated visual grasping datasets to train modern machine learning algorithms can be extremely time-consuming and expensive.
A. Bicchi, “On the Closure Properties of Robotic Grasping,”
1995
Earlier work this paper cites.
B. Siciliano and O. Khatib,
2007
Earlier work this paper cites.
A. Saxena, J. Driemeyer, and A. Y. Ng, “Robotic Grasping of Novel Objects using Vision,”
2008
Earlier work this paper cites.
R. Gopalan, R. Li, and R. Chellappa, “Domain Adaptation for Object Recognition: An Unsupervised Approach,” in
2011
Earlier work this paper cites.
A. Rodriguez, M. T. Mason, and S. Ferry, “From Caging to Grasping,”
2012
Earlier work this paper cites.
B. Gong, Y. Shi, F. Sha, and K. Grauman, “Geodesic flow kernel for unsupervised domain adaptation,” in
2012
Earlier work this paper cites.
J. Bohg, A. Morales, T. Asfour, and D. Kragic, “Data-driven grasp synthesis-a survey,”
2014
Earlier work this paper cites.
I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, “Generative adversarial nets,” in
2014
Earlier work this paper cites.
D. Kappler, J. Bohg, and S. Schaal, “Leveraging Big Data for Grasp Planning,” in
2015
Earlier work this paper cites.
I. Lenz, H. Lee, and A. Saxena, “Deep Learning for Detecting Robotic Grasps,”
2015
Earlier work this paper cites.
V. M. Patel, R. Gopalan, R. Li, and R. Chellappa, “Visual domain adaptation: A survey of recent advances,”
2015
Earlier work this paper cites.
R. Caseiro, J. F. Henriques, P. Martins, and J. Batista, “Beyond the shortest path: Unsupervised Domain Adaptation by Sampling Subspaces Along the Spline Flow,” in
2015
Earlier work this paper cites.
M. Long and J. Wang, “Learning transferable features with deep adaptation networks,”
2015
Earlier work this paper cites.
A. X. Chang, T. A. Funkhouser, L. J. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu, “ShapeNet: An Information-Rich 3D Model Repository,”
2015
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch Normalization: Accelerating Network Training by Reducing Internal Covariate Shift,” in
2015
Cited alongside, same era.
O. Ronneberger, P. Fischer, and T. Brox, “U-Net: Convolutional Networks for Biomedical Image Segmentation,” in
2015
Cited alongside, same era.
P. L. and A. Gupta, “Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours,” in
2016
Cited alongside, same era.
S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen, “Learning Hand-Eye Coordination for Robotic Grasping with Deep Learning and Large-Scale Data Collection,”
2016
Cited alongside, same era.
M. Gualtieri, A. ten Pas, K. Saenko, and R. Platt, “High precision grasp pose detection in dense clutter,” in
2016
Cited alongside, same era.
2016
Later among the works it cites.
2016
Later among the works it cites.
2017
Closest in time.
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,” in
2017
Closest in time.
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2016
Cited alongside, same era.
B. Sun, J. Feng, and K. Saenko, “Return of frustratingly easy domain adaptation,” in
2016
Cited alongside, same era.
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, “Domain-adversarial training of neural networks,”
2016
Cited alongside, same era.
K. Bousmalis, G. Trigeorgis, N. Silberman, D. Krishnan, and D. Erhan, “Domain separation networks,” in
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2017
Closest in time.
2017
Closest in time.
G. Csurka, “Domain adaptation for visual applications: A comprehensive survey,”
2017
Closest in time.
Y. Taigman, A. Polyak, and L. Wolf, “Unsupervised cross-domain image generation,” in
2017
Closest in time.
K. Bousmalis, N. Silberman, D. Dohan, D. Erhan, and D. Krishnan, “Unsupervised pixel-level domain adaptation with generative adversarial neural networks,” in
2017
Closest in time.
A. Shrivastava, T. Pfister, O. Tuzel, J. Susskind, W. Wang, and R. Webb, “Learning from simulated and unsupervised images through adversarial training,” in
2017
Closest in time.
J.-Y. Zhu, T. Park, P. Isola, and A. A. Efros, “Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks,” in
2017
Closest in time.
E. Coumans and Y. Bai, “pybullet, a python module for physics simulation in robotics, games and machine learning,”
2017
Closest in time.