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We consider the problem of detecting robotic grasps in an RGB-D view of a scene containing objects.
Mechanics of form closure
K. Lakshminarayana · 1978
Earlier work this paper cites.
Constructing stable force-closure grasps
V. Nguyen · 1986
Earlier work this paper cites.
Planning optimal grasps
C. Ferrari and J. Canny · 1992
Earlier work this paper cites.
On computing two-finger force-closure grasps of curved 2D objects
J. Ponce, D. Stam, and B. Faverjon · 1993
Earlier work this paper cites.
Learning to grasp using visual information
I. Kamon, T. Flash, and S. Edelman · 1996
Earlier work this paper cites.
Robot grasp synthesis algorithms: A survey
K. B. Shimoga · 1996
Earlier work this paper cites.
Robotic grasping and contact: a review
A. Bicchi and V. Kumar · 2000
Earlier work this paper cites.
Topographic independent component analysis
A. Hyvärinen, P. O. Hoyer, and M. Inki · 2001
Earlier work this paper cites.
Rapid object detection using a boosted cascade of simple features
P. Viola and M. Jones · 2001
Earlier work this paper cites.
Principal Component Analysis and Whitening , chapter 6, pages 125–144
A. Hyvärinen, J. Karhunen, and E. Oja · 2002
Earlier work this paper cites.
Vision-based computation of three-finger grasps on unknown planar objects
A. Morales, P. J. Sanz, and Àngel P. del Pobil · 2002
Earlier work this paper cites.
Learning visual features to predict hand orientations
J. H. Piater · 2002
Earlier work this paper cites.
Manipulation of unmodeled objects using intelligent grasping schemes
D. Bowers and R. Lumia · 2003
Earlier work this paper cites.
Robust visual servoing
D. Kragic and H. I. Christensen · 2003
Earlier work this paper cites.
Learning methods for generic object recognition with invariance to pose and lighting
Y. LeCun, F. Huang, and L. Bottou · 2004
Earlier work this paper cites.
Reducing the dimensionality of data with neural networks
G. Hinton and R. Salakhutdinov · 2006
Earlier work this paper cites.
Robotic grasping of novel objects
A. Saxena, J. Driemeyer, J. Kearns, and A. Ng · 2006
Earlier work this paper cites.
Learning and evaluation of the approach vector for automatic grasp generation and planning
S. Ekvall and D. Kragic · 2007
Earlier work this paper cites.
Synergistic face detection and pose estimation with energy-based models
M. Osadchy, Y. LeCun, and M. Miller · 2007
Earlier work this paper cites.
Selection of Robot Pre-Grasps using Box-Based Shape Approximation
K. Huebner and D. Kragic · 2008
Earlier work this paper cites.
Probabilistic models of object geometry for grasp planning
N. R. Jared Glover, Daniela Rus · 2008
Cited alongside, same era.
Learning deep architectures for AI
Y. Bengio · 2009
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Object recognition and full pose registration from a single image for robotic manipulation
A. Collet Romea, D. Berenson, S. Srinivasa, and D. Ferguson · 2009
Cited alongside, same era.
Learning object-specific grasp affordance densities
R. Detry, E. Baseski, M. Popovic, Y. Touati, N. Kruger, O. Kroemer, J. Peters, and J. Piater · 2009
Cited alongside, same era.
The Columbia grasp database
C. Goldfeder, M. Ciocarlie, H. Dang, and P. K. Allen · 2009
Cited alongside, same era.
Measuring invariances in deep networks
I. Goodfellow, Q. Le, A. Saxe, H. Lee, and A. Y. Ng · 2009
Cited alongside, same era.
From caging to grasping
A. Rodriguez, M. Mason, and S. Ferry · 2011
Later among the works it cites.
Efficient learning of sparse, distributed, convolutional feature representations for object recognition
K. Sohn, D. Y. Jung, H. Lee, and A. Hero III · 2011
Later among the works it cites.
Grasp evaluation with graspable feature matching
L. Zhang, M. Ciocarlie, and K. Hsiao · 2011
Later among the works it cites.
Unsupervised Feature Learning for RGB-D Based Object Recognition
L. Bo, X. Ren, and D. Fox · 2012
Later among the works it cites.
Physics-based grasp planning through clutter
M. Dogar, K. Hsiao, M. Ciocarlie, and S. Srinivasa · 2012
Later among the works it cites.
Building high-level features using large scale unsupervised learning
Q. Le, M. Ranzato, R. Monga, M. Devin, K. Chen, G. Corrado, J. Dean, and A. Ng · 2012
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R. B. Rusu, N. Blodow, and M. Beetz · 2009
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A dirty model for multi-task learning
A. Jalali, P. Ravikumar, S. Sanghavi, and C. Ruan · 2010
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Learning to grasp objects with multiple contact points
Q. V. Le, D. Kamm, A. F. Kara, and A. Y. Ng · 2010
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Cloth grasp point detection based on multiple-view geometric cues with application to robotic towel folding
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An overview of 3d object grasp synthesis algorithms
A. Sahbani, S. El-Khoury, and P. Bidaud · 2012
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Convolutional-recursive deep learning for 3D object classification
R. Socher, B. Huval, B. Bhat, C. D. Manning, and A. Y. Ng · 2012
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Multimodal learning with deep Boltzmann machines
N. Srivastava and R. Salakhutdinov · 2012
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Direct 3d servoing using dense depth maps
C. Teuliere and E. Marchand · 2012
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Pose error robust grasping from contact wrench space metrics
J. Weisz and P. K. Allen · 2012
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Semantic parsing for priming object detection in rgb-d scenes
C. Cadena and J. Kosecka · 2013
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3d mapping with an RGB-D camera
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