Fetching the paper…
Reading the bibliography…
Humans effortlessly "program" one another by communicating goals and desires in natural language.
Algorithms for inverse reinforcement learning
A. Y. Ng and S. J. Russell · 2000
Earlier work this paper cites.
The development of embodied cognition: Six lessons from babies
L. Smith and M. Gasser · 2005
Earlier work this paper cites.
An object-oriented representation for efficient reinforcement learning
C. Diuk, A. Cohen, and M. L. Littman · 2008
Earlier work this paper cites.
Maximum entropy inverse reinforcement learning
B. D. Ziebart, A. Maas, J. A. Bagnell, and A. K. Dey · 2008
Earlier work this paper cites.
Reinforcement learning for mapping instructions to actions
S. R. K. Branavan, H. Chen, L. S. Zettlemoyer, and R. Barzilay · 2009
Earlier work this paper cites.
Understanding natural language commands for robotic navigation and mobile manipulation
S. Tellex, T. Kollar, S. Dickerson, M. R. Walter, A. G. Banerjee, S. Teller, and N. Roy · 2011
Earlier work this paper cites.
A database for fine grained activity detection of cooking activities
M. Rohrbach, S. Amin, M. Andriluka, and B. Schiele · 2012
Earlier work this paper cites.
Weakly supervised learning of semantic parsers for mapping instructions to actions
Y. Artzi and L. Zettlemoyer · 2013
Earlier work this paper cites.
Playing atari with deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. A. Riedmiller · 2013
Earlier work this paper cites.
Learning to select and generalize striking movements in robot table tennis
K. Mülling, J. Kober, O. Kroemer, and J. Peters · 2013
Earlier work this paper cites.
Translating video content to natural language descriptions
M. Rohrbach, Q. Wei, I. Titov, S. Thater, M. Pinkal, and B. Schiele · 2013
Earlier work this paper cites.
A fast and accurate dependency parser using neural networks
D. Chen and C. D. Manning · 2014
Earlier work this paper cites.
Deep learning for real-time atari game play using offline monte-carlo tree search planning
X. Guo, S. Singh, H. Lee, R. L. Lewis, and X. Wang · 2014
Earlier work this paper cites.
Tell me dave: Context-sensitive grounding of natural language to manipulation instructions
D. K. Misra, J. Sung, K. Lee, and A. Saxena · 2014
Earlier work this paper cites.
Vqa: Visual question answering
S. Antol, A. Agrawal, J. Lu, M. Mitchell, D. Batra, C. Lawrence Zitnick, and D. Parikh · 2015
Earlier work this paper cites.
ShapeNet: An Information-Rich 3D Model Repository
A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu · 2015
Earlier work this paper cites.
Microsoft COCO captions: Data collection and evaluation server
X. Chen, H. Fang, T. Lin, R. Vedantam, S. Gupta, P. Dollár, and C. L. Zitnick · 2015
Earlier work this paper cites.
Language models for image captioning: The quirks and what works
J. Devlin, H. Cheng, H. Fang, S. Gupta, L. Deng, X. He, G. Zweig, and M. Mitchell · 2015
Cited alongside, same era.
From captions to visual concepts and back
H. Fang, S. Gupta, F. Iandola, R. K. Srivastava, L. Deng, P. Dollár, J. Gao, X. He, M. Mitchell, J. C. Platt, et al · 2015
Cited alongside, same era.
Learning visual predictive models of physics for playing billiards
K. Fragkiadaki, P. Agrawal, S. Levine, and J. Malik · 2015
Cited alongside, same era.
Deepmpc: Learning deep latent features for model predictive control
I. Lenz, R. Knepper, and A. Saxena · 2015
Cited alongside, same era.
End-to-end training of deep visuomotor policies
S. Levine, C. Finn, T. Darrell, and P. Abbeel · 2015
Cited alongside, same era.
Visual genome: Connecting language and vision using crowdsourced dense image annotations
R. Krishna, Y. Zhu, O. Groth, J. Johnson, K. Hata, J. Kravitz, S. Chen, Y. Kalanditis, L.-J. Li, D. A. Shamma, M. Bernstein, and L. Fei-Fei · 2016
Later among the works it cites.
Learning relevant features for manipulation skills using meta-level priors
O. Kroemer and G. S. Sukhatme · 2016
Later among the works it cites.
Unsupervised perceptual rewards for imitation learning
P. Sermanet, K. Xu, and S. Levine · 2016
Later among the works it cites.
Movieqa: Understanding stories in movies through question-answering
M. Tapaswi, Y. Zhu, R. Stiefelhagen, A. Torralba, R. Urtasun, and S. Fidler · 2016
Later among the works it cites.
M. Andrychowicz, F. Wolski, A. Ray, J. Schneider, R. Fong, P. Welinder, B. McGrew, J. Tobin, P. Abbeel, and W. Zaremba · 2017
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Continuous control with deep reinforcement learning
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra · 2015
Cited alongside, same era.
How language programs the mind
G. Lupyan and B. Bergen · 2015
Cited alongside, same era.
Environment-driven lexicon induction for high-level instructions
D. K. Misra, K. Tao, P. Liang, and A. Saxena · 2015
Cited alongside, same era.
A dataset for movie description
A. Rohrbach, M. Rohrbach, N. Tandon, and B. Schiele · 2015
Cited alongside, same era.
Trust region policy optimization
J. Schulman, S. Levine, P. Abbeel, M. Jordan, and P. Moritz · 2015
Cited alongside, same era.
Unsupervised learning from narrated instruction videos
J.-B. Alayrac, P. Bojanowski, N. Agrawal, I. Laptev, J. Sivic, and S. Lacoste-Julien · 2016
Cited alongside, same era.
Learning to compose neural networks for question answering
J. Andreas, M. Rohrbach, T. Darrell, and D. Klein · 2016
Cited alongside, same era.
Later among the works it cites.
M. Andrychowicz, F. Wolski, A. Ray, J. Schneider, R. Fong, P. Welinder, B. McGrew, J. Tobin, P. Abbeel, and W. Zaremba · 2017
Later among the works it cites.
Deep object-centric representations for generalizable robot learning
C. Devin, P. Abbeel, T. Darrell, and S. Levine · 2017
Later among the works it cites.
Y. Duan, M. Andrychowicz, B. C. Stadie, J. Ho, J. Schneider, I. Sutskever, P. Abbeel, and W. Zaremba · 2017
Later among the works it cites.
Multi-modal imitation learning from unstructured demonstrations using generative adversarial nets
K. Hausman, Y. Chebotar, S. Schaal, G. S. Sukhatme, and J. J. Lim · 2017
Later among the works it cites.
Unsupervised visual-linguistic reference resolution in instructional videos
D. Huang, J. J. Lim, F. Li, and J. C. Niebles · 2017
Later among the works it cites.
Schema networks: Zero-shot transfer with a generative causal model of intuitive physics
K. Kansky, T. Silver, D. A. Mély, M. Eldawy, M. Lázaro-Gredilla, X. Lou, N. Dorfman, S. Sidor, D. S. Phoenix, and D. George · 2017
Later among the works it cites.
Mapping instructions and visual observations to actions with reinforcement learning
D. K. Misra, J. Langford, and Y. Artzi · 2017
Later among the works it cites.
Asymmetric actor critic for image-based robot learning
L. Pinto, M. Andrychowicz, P. Welinder, W. Zaremba, and P. Abbeel · 2017
Later among the works it cites.
Learning complex dexterous manipulation with deep reinforcement learning and demonstrations
A. Rajeswaran, V. Kumar, A. Gupta, J. Schulman, E. Todorov, and S. Levine · 2017
Later among the works it cites.
Generating descriptions with grounded and co-referenced people
A. Rohrbach, M. Rohrbach, S. Tang, S. J. Oh, and B. Schiele · 2017
Later among the works it cites.
Third-person imitation learning
B. C. Stadie, P. Abbeel, and I. Sutskever · 2017
Later among the works it cites.
Collective robot reinforcement learning with distributed asynchronous guided policy search
A. Yahya, A. Li, M. Kalakrishnan, Y. Chebotar, and S. Levine · 2017
Later among the works it cites.