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To determine if a skill can be executed in any given environment, a robot needs to learn the preconditions for the skill.
A volumetric method for building complex models from range images
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Cram—a cognitive robot abstract machine for everyday manipulation in human environments
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Active learning for teaching a robot grounded relational symbols
J. Kulick, M. Toussaint, T. Lang, and M. Lopes · 2013
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Learning grounded relational symbols from continuous data for abstract reasoning
N. Jetchev, T. Lang, and M. Toussaint · 2013
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V-rep: A versatile and scalable robot simulation framework
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Learning spatial relationships from 3d vision using histograms
S. Fichtl, A. McManus, W. Mustafa, D. Kraft, N. Krüger, and F. Guerin · 2014
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Very deep convolutional networks for large-scale image recognition
K. Simonyan and A. Zisserman · 2014
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Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
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Learning the spatial semantics of manipulation actions through preposition grounding
Deep residual learning for image recognition
K. He, X. Zhang, S. Ren, and J. Sun · 2016
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Deep metric learning via lifted structured feature embedding
H. Oh Song, Y. Xiang, S. Jegelka, and S. Savarese · 2016
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Semi-supervised classification with graph convolutional networks
T. N. Kipf and M. Welling · 2016
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Mask R-CNN
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
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Metric learning for generalizing spatial relations to new objects
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A simple neural network module for relational reasoning
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K. Zampogiannis, Y. Yang, C. Fermüller, and Y. Aloimonos · 2015
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Facenet: A unified embedding for face recognition and clustering
F. Schroff, D. Kalenichenko, and J. Philbin · 2015
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Do what i want, not what i did: Imitation of skills by planning sequences of actions
C. Paxton, F. Jonathan, M. Kobilarov, and G. D. Hager · 2016
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Learning spatial preconditions of manipulation skills using random forests
O. Kroemer and G. S. Sukhatme · 2016
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Optimization beyond the convolution: Generalizing spatial relations with end-to-end metric learning
P. Jund, A. Eitel, N. Abdo, and W. Burgard · 2018
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Can spatiotemporal 3d cnns retrace the history of 2d cnns and imagenet?
K. Hara, H. Kataoka, and Y. Satoh · 2018
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Relational inductive biases, deep learning, and graph networks
P. W. Battaglia, J. B. Hamrick, V. Bapst, A. Sanchez-Gonzalez, V. Zambaldi, M. Malinowski, A. Tacchetti, D. Raposo, A. Santoro, R. Faulkner, et al · 2018
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