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Training a deep network policy for robot manipulation is notoriously costly and time consuming as it depends on collecting a significant amount of real world data.
The cross-entropy method: A unified approach to combinatorial optimization, monte-carlo simulation, and machine learning
R. Rubinstein and D. Kroese · 2004
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Robotic grasping of novel objects using vision
A. Saxena, J. Driemeyer, and A. Y. Ng · 2008
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The columbia grasp database
C. Goldfeder, M. Ciocarlie, H. Dang, and P. K. Allen · 2009
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J. Bohg and D. Kragic · 2010
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Kinectfusion: Real-time dense surface mapping and tracking
R. A. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, D. Kim, A. J. Davison, P. Kohi, J. Shotton, S. Hodges, and A. Fitzgibbon · 2011
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Physics-based grasp planning through clutter
M. Dogar, K. Hsiao, M. Ciocarlie, and S. Srinivasa · 2012
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Active learning of visual descriptors for grasping using non-parametric smoothed beta distributions
L. Montesano and M. Lopes · 2012
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Semantic grasping: planning task-specific stable robotic grasps
H. Dang and P. K. Allen · 2014
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Perceiving, learning, and exploiting object affordances for autonomous pile manipulation
D. Katz, A. Venkatraman, M. Kazemi, J. A. Bagnell, and A. Stentz · 2014
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Shapenet: An information-rich 3d model repository
A. X. Chang, T. A. Funkhouser, L. J. Guibas, P. Hanrahan, Q.-X. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu · 2015
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Predicting depth, surface normals and semantic labels with a common multi-scale convolutional architecture
D. Eigen and R. Fergus · 2015
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Deep learning for detecting robotic grasps
I. Lenz, H. Lee, and A. Saxena · 2015
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Database-assisted object retrieval for real-time 3d reconstruction
Y. Li, A. Dai, L. Guibas, and M. Niessner · 2015
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Category-based task specific grasping
E. Nikandrova and V. Kyrki · 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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Unsupervised cnn for single view depth estimation: Geometry to the rescue
R. Garg, V. K. BG, G. Carneiro, and I. Reid · 2016
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High precision grasp pose detection in dense clutter
M. Gualtieri, A. ten Pas, K. Saenko, and R. Platt · 2016
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High precision grasp pose detection in dense clutter
M. Gualtieri, A. ten Pas, K. Saenko, and R. Platt · 2016
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Deep learning a grasp function for grasping under gripper pose uncertainty
E. Johns, S. Leutenegger, and A. J. Davison · 2016
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One-shot learning and generation of dexterous grasps for novel objects
M. Kopicki, R. Detry, M. Adjigble, R. Stolkin, A. Leonardis, and J. L. Wyatt · 2016
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Dexterous grasping under shape uncertainty
M. Li, K. Hang, D. Kragic, and A. Billard · 2016
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Dex-net 1.0: A cloud-based network of 3d objects for robust grasp planning using a multi-armed bandit model with correlated rewards
J. Mahler, F. T. Pokorny, B. Hou, M. Roderick, M. Laskey, M. Aubry, K. Kohlhoff, T. Kröger, J. Kuffner, and K. Goldberg · 2016
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A field model for repairing 3d shapes
D. T. Nguyen, B. Hua, M. Tran, Q. Pham, and S. Yeung · 2016
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Experiments with hierarchical reinforcement learning of multiple grasping policies
T. Osa, J. Peters, and G. Neumann · 2016
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Supersizing self-supervision: Learning to grasp from 50k tries and 700 robot hours
L. Pinto and A. Gupta · 2016
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Part-based grasp planning for familiar objects
Learning representations and generative models for 3d point clouds
P. Achlioptas, O. Diamanti, I. Mitliagkas, and L. J. Guibas · 2018
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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. P. Sampedro, K. Konolige, S. Levine, and V. Vanhoucke · 2018
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Scancomplete: Large-scale scene completion and semantic segmentation for 3d scans
A. Dai, D. Ritchie, M. Bokeloh, S. Reed, J. Sturm, and M. Nießner · 2018
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Grasp2vec: Learning object representations from self-supervised grasping
C. M. Devin, E. Jang, S. Levine, and V. Vanhoucke · 2018
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Neural scene representation and rendering
S. A. Eslami, D. J. Rezende, F. Besse, F. Viola, A. S. Morcos, M. Garnelo, A. Ruderman, A. A. Rusu, I. Danihelka, K. Gregor, et al · 2018
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N. Vahrenkamp, L. Westkamp, N. Yamanobe, E. E. Aksoy, and T. Asfour · 2016
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Shape completion enabled robotic grasping
J. Varley, C. DeChant, A. Richardson, A. Nair, J. Ruales, and P. Allen · 2016
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Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
J. Wu, C. Zhang, T. Xue, B. Freeman, and J. Tenenbaum · 2016
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Perspective transformer nets: Learning single-view 3d object reconstruction without 3d supervision
X. Yan, J. Yang, E. Yumer, Y. Guo, and H. Lee · 2016
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Domain adaptation for visual applications: A comprehensive survey
G. Csurka · 2017
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A point set generation network for 3d object reconstruction from a single image
H. Fan, H. Su, and L. J. Guibas · 2017
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Mask R-CNN
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
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Multi-task domain adaptation for deep learning of instance grasping from simulation
K. Fang, Y. Bai, S. Hinterstoißer, S. Savarese, and M. Kalakrishnan · 2018
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Multiresolution tree networks for 3d point cloud processing
M. Gadelha, R. Wang, and S. Maji · 2018
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Parsing geometry using structure-aware shape templates
V. Ganapathi-Subramanian, O. Diamanti, S. Pirk, C. Tang, M. Niessner, and L. Guibas · 2018
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Learning to generate and reconstruct 3d meshes with only 2d supervision
P. Henderson and V. Ferrari · 2018
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Sim-to-real via sim-to-sim: Data-efficient robotic grasping via randomized-to-canonical adaptation networks
S. James, P. Wohlhart, M. Kalakrishnan, D. Kalashnikov, A. Irpan, J. Ibarz, S. Levine, R. Hadsell, and K. Bousmalis · 2018
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Gal: Geometric adversarial loss for single-view 3d-object reconstruction
L. Jiang, S. Shi, X. Qi, and J. Jia · 2018
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Neural 3d mesh renderer
H. Kato, Y. Ushiku, and T. Harada · 2018
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Pixel2mesh: Generating 3d mesh models from single rgb images
N. Wang, Y. Zhang, Z. Li, Y. Fu, W. Liu, and Y.-G. Jiang · 2018
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3d shape perception from monocular vision, touch, and shape priors
S. Wang, J. Wu, X. Sun, W. Yuan, W. T. Freeman, J. B. Tenenbaum, and E. H. Adelson · 2018
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Pointfusion: Deep sensor fusion for 3d bounding box estimation
D. Xu, D. Anguelov, and A. Jain · 2018
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Learning 6-dof grasping interaction via deep geometry-aware 3d representations
X. Yan, J. Hsu, M. Khansari, Y. Bai, A. Pathak, A. Gupta, J. Davidson, and H. Lee · 2018
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Pybullet, a python module for physics simulation for games, robotics and machine learning
E. Coumans and Y. Bai · 2019
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Soft rasterizer: Differentiable rendering for unsupervised single-view mesh reconstruction
S. Liu, W. Chen, T. Li, and H. Li · 2019
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Densefusion: 6d object pose estimation by iterative dense fusion
C. Wang, D. Xu, Y. Zhu, R. Martín-Martín, C. Lu, L. Fei-Fei, and S. Savarese · 2019
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