Fetching the paper…
Reading the bibliography…
Learning from demonstration is an effective method for human users to instruct desired robot behaviour.
Saying what you mean in dialogue: A study in conceptual and semantic coordination
S. Garrod and A. Anderson · 1987
Earlier work this paper cites.
The symbol grounding problem
S. Harnad · 1990
Earlier work this paper cites.
The physical symbol grounding problem
P. Vogt · 2002
Earlier work this paper cites.
Toward a mechanistic psychology of dialogue
M. J. Pickering and S. Garrod · 2004
Earlier work this paper cites.
Research on Language and Computation , 2(4):575–596, 2004
S. Oepen, D. Flickinger, K. Toutanova, and C. D. Manning · 2004
Earlier work this paper cites.
A survey of robot learning from demonstration
B. D. Argall, S. Chernova, M. Veloso, and B. Browning · 2009
Earlier work this paper cites.
Learning spatial relationships between objects
B. Rosman and S. Ramamoorthy · 2011
Earlier work this paper cites.
Moveit![ros topics]
S. Chitta, I. Sucan, and S. Cousins · 2012
Earlier work this paper cites.
Auto-encoding variational bayes
D. P. Kingma and M. Welling · 2013
Earlier work this paper cites.
Interactive visual grounding of referring expressions for human-robot interaction
M. Shridhar and D. Hsu · 2014
Earlier work this paper cites.
Adam: A method for stochastic optimization
D. P. Kingma and J. Ba · 2014
Earlier work this paper cites.
Siamese neural networks for one-shot image recognition
G. Koch, R. Zemel, and R. Salakhutdinov · 2015
Cited alongside, same era.
Concrete problems in ai safety
D. Amodei, C. Olah, J. Steinhardt, P. Christiano, J. Schulman, and D. Mané · 2016
Cited alongside, same era.
Infogan: Interpretable representation learning by information maximizing generative adversarial nets
X. Chen, Y. Duan, R. Houthooft, J. Schulman, I. Sutskever, and P. Abbeel · 2016
Cited alongside, same era.
Neural module networks
J. Andreas, M. Rohrbach, T. Darrell, and D. Klein · 2016
Cited alongside, same era.
Interactive task learning
J. E. Laird, K. Gluck, J. Anderson, K. D. Forbus, O. C. Jenkins, C. Lebiere, D. Salvucci, M. Scheutz, A. Thomaz, G. Trafton, et al · 2017
Cited alongside, same era.
A simple neural network module for relational reasoning
ERG semantic documentation, 2014
D. Flickinger, E. M. Bender, and S. Oepen · 2017
Later among the works it cites.
Isolating sources of disentanglement in variational autoencoders
T. Q. Chen, X. Li, R. B. Grosse, and D. K. Duvenaud · 2018
Later among the works it cites.
oi-VAE: Output interpretable VAEs for nonlinear group factor analysis
S. K. Ainsworth, N. J. Foti, A. K. C. Lee, and E. B. Fox · 2018
Later among the works it cites.
Interpretable latent spaces for learning from demonstration
Y. Hristov, A. Lascarides, and S. Ramamoorthy · 2018
Later among the works it cites.
Classical planning in deep latent space: Bridging the subsymbolic-symbolic boundary
M. Asai and A. Fukunaga · 2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
A. Santoro, D. Raposo, D. G. Barrett, M. Malinowski, R. Pascanu, P. Battaglia, and T. Lillicrap · 2017
Cited alongside, same era.
Discovering objects and their relations from entangled scene representations
D. Raposo, A. Santoro, D. Barrett, R. Pascanu, T. Lillicrap, and P. Battaglia · 2017
Cited alongside, same era.
Clevr: A diagnostic dataset for compositional language and elementary visual reasoning
J. Johnson, B. Hariharan, L. van der Maaten, L. Fei-Fei, C. Lawrence Zitnick, and R. Girshick · 2017
Cited alongside, same era.
beta-vae: Learning basic visual concepts with a constrained variational framework
I. Higgins, L. Matthey, A. Pal, C. Burgess, X. Glorot, M. Botvinick, S. Mohamed, and A. Lerchner · 2017
Cited alongside, same era.
Unsupervised learning of disentangled representations from video
E. L. Denton et al · 2017
Cited alongside, same era.
C. P. Burgess, I. Higgins, A. Pal, L. Matthey, N. Watters, G. Desjardins, and A. Lerchner · 2018
Later among the works it cites.
Monet: Unsupervised scene decomposition and representation
C. P. Burgess, L. Matthey, N. Watters, R. Kabra, I. Higgins, M. Botvinick, and A. Lerchner · 2019
Closest in time.
Multi-object representation learning with iterative variational inference
K. Greff, R. L. Kaufmann, R. Kabra, N. Watters, C. Burgess, D. Zoran, L. Matthey, M. Botvinick, and A. Lerchner · 2019
Closest in time.
Unsupervised grounding of plannable first-order logic representation from images
M. Asai · 2019
Closest in time.
Spatial broadcast decoder: A simple architecture for learning disentangled representations in vaes
N. Watters, L. Matthey, C. P. Burgess, and A. Lerchner · 2019
Closest in time.