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This article presents a method for grasping novel objects by learning from experience.
Using experience for assessing grasp reliability
A. Morales, E. Chinellato, A. H. Fagg, and A. P. Del Pobil · 2004
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Dimensionality reduction by learning an invariant mapping
R. Hadsell, S. Chopra, and Y. LeCun · 2006
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Robotic grasping of novel objects using vision
A. Saxena, J. Driemeyer, and A. Y. Ng · 2008
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Grasping familiar objects using shape context
J. Bohg and D. Kragic · 2009
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ImageNet: A large-scale hierarchical image database
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei · 2009
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Real-time CAD model matching for mobile manipulation and grasping
U. Klank, D. Pangercic, R. B. Rusu, and M. Beetz · 2009
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ROS: An open-source robot operating system
M. Quigley, K. Conley, B. P. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, and A. Y. Ng · 2009
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Perception for mobile manipulation and grasping using active stereo
R. B. Rusu, A. Holzbach, R. Diankov, G. Bradski, and M. Beetz · 2009
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Learning task constraints for robot grasping using graphical models
D. Song, K. Huebner, V. Kyrki, and D. Kragic · 2010
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HERB: A home exploring robotic butler
S. S. Srinivasa, D. Ferguson, C. J. Helfrich, D. Berenson, A. Collet, R. Diankov, G. Gallagher, G. Hollinger, J. Kuffner, and M. V. Weghe · 2010
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Efficient grasping from rgbd images: Learning using a new rectangle representation
Y. Jiang, S. Moseson, and A. Saxena · 2011
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Mobile manipulation in unstructured environments: Perception, planning, and execution
S. Chitta, E. G. Jones, M. Ciocarlie, and K. Hsiao · 2012
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Moveit! [ros topics]
S. Chitta, I. Sucan, and S. Cousins · 2012
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Semantic grasping: Planning robotic grasps functionally suitable for an object manipulation task
H. Dang and P. K. Allen · 2012
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Generalizing grasps across partly similar objects
R. Detry, C. H. Ek, M. Madry, J. Piater, and D. Kragic · 2012
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Template-based learning of grasp selection
A. Herzog, P. Pastor, M. Kalakrishnan, L. Righetti, T. Asfour, and S. Schaal · 2012
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A kernel-based approach to direct action perception
O. Kroemer, E. Ugur, E. Oztop, and J. Peters · 2012
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Learning a dictionary of prototypical grasp-predicting parts from grasping experience
R. Detry, C. H. Ek, M. Madry, and D. Kragic · 2013
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Unsupervised learning of predictive parts for cross-object grasp transfer
R. Detry and J. Piater · 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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Learning to manipulate unknown objects in clutter by reinforcement
A. Boularias, J. A. Bagnell, and A. Stentz · 2015
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Learning grasps with topographic features
D. Fischinger, A. Weiss, and M. Vincze · 2015
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Adam: A method for stochastic optimization
D. P. Kingma and J. L. Ba · 2015
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Deep learning for detecting robotic grasps
I. Lenz, H. Lee, and A. Saxena · 2015
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RGB-D object modelling for object recognition and tracking
J. Prankl, A. Aldoma, A. Svejda, and M. Vincze · 2015
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Real-time grasp detection using convolutional neural networks
J. Redmon and A. Angelova · 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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Learning descriptors for object recognition and 3D pose estimation
P. Wohlhart and V. Lepetit · 2015
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Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 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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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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Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection
S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen · 2018
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Grasping unknown objects in clutter by superquadric representation
A. Makhal, F. Thomas, and A. P. Gracia · 2018
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Closing the loop for robotic grasping: A real-time, generative grasp synthesis approach
D. Morrison, J. Leitner, and P. Corke · 2018
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Deep object pose estimation for semantic robotic grasping of household objects
J. Tremblay, T. To, B. Sundaralingam, Y. Xiang, D. Fox, and S. T. Birchfield · 2018
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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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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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Supersizing self-supervision: Learning to grasp from 50K tries and 700 robot hours
L. Pinto and A. Gupta · 2016
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Pose guided RGBD feature learning for 3D object pose estimation
V. Balntas, A. Doumanoglou, C. Sahin, J. Sock, R. Kouskouridas, and T.-K. Kim · 2017
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Yale-CMU-Berkeley dataset for robotic manipulation research
B. Calli, A. Singh, J. Bruce, A. Walsman, K. Konolige, S. Srinivasa, P. Abbeel, and A. M. Dollar · 2017
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Transferring end-to-end visuomotor control from simulation to real world for a multi-stage task
S. James, A. J. Davison, and E. Johns · 2017
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PoseCNN: A convolutional neural network for 6D object pose estimation in cluttered scenes
Y. Xiang, T. Schmidt, V. Narayanan, and D. Fox · 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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Robotic pick-and-place of novel objects in clutter with multi-affordance grasping and cross-domain image matching
A. Zeng, S. Song, K. Yu, E. Donlon, F. R. Hogan, M. Bauza, D. Ma, O. Taylor, M. Liu, E. Romo, N. Fazeli, F. Alet, N. C. Dafle, R. Holladay, I. Morena, P. Qu Nair, D. Green, I. Taylor, W. Liu, T. Funkhouser, and A. Rodriguez · 2018
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MetaGrasp: Data efficient grasping by affordance interpreter network
J. Cai, H. Cheng, Z. Zhang, and J. Su · 2019
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Directional semantic grasping of real-world objects: From simulation to reality
S. Iqbal, J. Tremblay, T. To, J. Cheng, E. Leitch, A. Campbell, K. Leung, D. McKay, and S. Birchfield · 2019
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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 · 2019
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CDPN: Coordinates-based disentangled pose network for real-time RGB-based 6-DoF object pose estimation
Z. Li, G. Wang, and X. Ji · 2019
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PointNetGPD: Detecting grasp configurations from point sets
H. Liang, X. Ma, S. Li, M. Görner, S. Tang, B. Fang, F. Sun, and J. Zhang · 2019
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Learning to grasp familiar objects based on experience and objects’ shape affordance
C. Liu, B. Fang, F. Sun, X. Li, and W. Huang · 2019
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kPAM: Keypoint affordances for category-level robotic manipulation
L. Manuelli, W. Gao, P. R. Florence, and R. Tedrake · 2019
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6-DOF GraspNet: Variational grasp generation for object manipulation
A. Mousavian, C. Eppner, and D. Fox · 2019
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Multi-task template matching for object detection, segmentation and pose estimation using depth images
K. Park, T. Patten, J. Prankl, and M. Vincze · 2019
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Pix2Pose: Pixel-wise coordinate regression of objects for 6D pose estimation
K. Park, T. Patten, and M. Vincze · 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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In-hand object scanning via RGB-D video segmentation
F. Wang and K. Hauser · 2019
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Normalized object coordinate space for category-level 6D object pose and size estimation
H. Wang, S. Sridhar, J. Huang, J. Valentin, S. Song, and L. J. Guibas · 2019
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Development of human support robot as the research platform of a domestic mobile manipulator
T. Yamamoto, K. Terada, A. Ochiai, F. Saito, Y. Asahara, and K. Murase · 2019
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DPOD: 6D pose object detector and refiner
S. Zakharov, I. Shugurov, and S. Ilic · 2019
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