Fetching the paper…
Reading the bibliography…
In this paper, we focus on the challenging perception problem in robotic pouring.
A. P. French, “In vino veritas: A study of wineglass acoustics,”
1983
Earlier work this paper cites.
H. P. Saal, J.-A. Ting, and S. Vijayakumar, “Active estimation of object dynamics parameters with tactile sensors,” in
2010
Earlier work this paper cites.
M. Tamosiunaite, B. Nemec, A. Ude, and F. Wörgötter, “Learning to pour with a robot arm combining goal and shape learning for dynamic movement primitives,”
2011
Earlier work this paper cites.
A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in
2012
Earlier work this paper cites.
S. Griffith, V. Sukhoy, T. Wegter, and A. Stoytchev, “Object categorization in the sink: Learning behavior–grounded object categories with water,” in
2012
Earlier work this paper cites.
L. Rozo, P. Jiménez, and C. Torras, “Force-based robot learning of pouring skills using parametric hidden markov models,” in
2013
Earlier work this paper cites.
S. Brandi, O. Kroemer, and J. Peters, “Generalizing pouring actions between objects using warped parameters,” in
2014
Earlier work this paper cites.
J. D. Langsfeld, K. N. Kaipa, R. J. Gentili, J. A. Reggia, and S. K. Gupta, “Incorporating failure-to-success transitions in imitation learning for a dynamic pouring task,” in
2014
Earlier work this paper cites.
K. Cho, B. Van Merriënboer, D. Bahdanau, and Y. Bengio, “On the properties of neural machine translation: Encoder-decoder approaches,” in
2014
Earlier work this paper cites.
C. Szegedy, W. Liu, Y. Jia, P. Sermanet, S. Reed, D. Anguelov, D. Erhan, V. Vanhoucke, and A. Rabinovich, “Going deeper with convolutions,” in
2015
Cited alongside, same era.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in
2015
Cited alongside, same era.
A. Yamaguchi, C. G. Atkeson, and T. Ogasawara, “Pouring skills with planning and learning modeled from human demonstrations,”
2015
Cited alongside, same era.
C. Elbrechter, J. Maycock, R. Haschke, and H. Ritter, “Discriminating liquids using a robotic kitchen assistant,” in
2015
Cited alongside, same era.
S. Ikeno, R. Watanabe, R. Okazaki, T. Hachisu, M. Sato, and H. Kajimoto, “Change in the amount poured as a result of vibration when pouring a liquid,” in
2015
Cited alongside, same era.
R. Mottaghi, C. Schenck, D. Fox, and A. Farhadi, “See the glass half full: Reasoning about liquid containers, their volume and content,” in
2017
Later among the works it cites.
C. Schenck and D. Fox, “Reasoning about liquids via closed-loop simulation,” in
2017
Later among the works it cites.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in
2017
Later among the works it cites.
Y. Huang and Y. Sun, “Learning to pour,” in
2017
Later among the works it cites.
C. Schenck and D. Fox, “Perceiving and reasoning about liquids using fully convolutional networks,”
2018
Later among the works it cites.
C. Do, C. Gordillo, and W. Burgard, “Learning to pour using deep deterministic policy gradients,” in
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in
2016
Cited alongside, same era.
Z. Pan and D. Manocha, “Motion planning for fluid manipulation using simplified dynamics,” in
2016
Cited alongside, same era.
C. Do, T. Schubert, and W. Burgard, “A probabilistic approach to liquid level detection in cups using an rgb-d camera,” in
2016
Cited alongside, same era.
C. Schenck and D. Fox, “Visual closed-loop control for pouring liquids,” in
2017
Cited alongside, same era.
2018
Later among the works it cites.
S. Clarke, T. Rhodes, C. G. Atkeson, and O. Kroemer, “Learning audio feedback for estimating amount and flow of granular material,” in
2018
Later among the works it cites.
C. Do and W. Burgard, “Accurate pouring with an autonomous robot using an rgb-d camera,” in
2019
Closest in time.