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We propose to directly map raw visual observations and text input to actions for instruction execution.
Understanding natural language
Terry Winograd. 1972 · 1972
Earlier work this paper cites.
Finding structure in time
Jeffrey L. Elman. 1990 · 1990
Earlier work this paper cites.
Function optimization using connectionist reinforcement learning algorithms
Ronald J Williams and Jing Peng. 1991 · 1991
Earlier work this paper cites.
Simple statistical gradient-following algorithms for connectionist reinforcement learning
Ronald J. Williams. 1992 · 1992
Earlier work this paper cites.
Instructions, intentions and expectations
Bonnie Webber, Norman Badler, Barbara Di Eugenio, Christopher Geib, Libby Levison, and Michael Moore. 1995 · 1995
Earlier work this paper cites.
Long short-term memory
Sepp Hochreiter and Jürgen Schmidhuber. 1997 · 1997
Earlier work this paper cites.
Reinforcement learning: An introduction
Richard S. Sutton and Andrew G. Barto. 1998 · 1998
Earlier work this paper cites.
A sparse sampling algorithm for near-optimal planning in large markov decision processes
Michael Kearns, Yishay Mansour, and Andrew Y. Ng. 1999 · 1999
Earlier work this paper cites.
Policy invariance under reward transformations: Theory and application to reward shaping
Andrew Y. Ng, Daishi Harada, and Stuart J. Russell. 1999 · 1999
Earlier work this paper cites.
Policy gradient methods for reinforcement learning with function approximation
Richard S. Sutton, David A. McAllester, Satinder P. Singh, and Yishay Mansour. 1999 · 1999
Earlier work this paper cites.
The nonstochastic multiarmed bandit problem
Peter Auer, Nicolò Cesa-Bianchi, Yoav Freund, and Robert E. Schapire. 2002 · 2002
Earlier work this paper cites.
Approximately optimal approximate reinforcement learning
Sham Kakade and John Langford. 2002 · 2002
Earlier work this paper cites.
Principled methods for advising reinforcement learning agents
Eric Wiewiora, Garrison W. Cottrell, and Charles Elkan. 2003 · 2003
Earlier work this paper cites.
Target-driven visual navigation in indoor scenes using deep reinforcement learning
Yuke Zhu, Roozbeh Mottaghi, Eric Kolve, Joseph J. Lim, Abhinav Gupta, Li Fei-Fei, and Ali Farhadi. 2017 · 2003
Earlier work this paper cites.
Walk the talk: Connecting language, knowledge, action in route instructions
Matthew MacMahon, Brian Stankiewics, and Benjamin Kuipers. 2006 · 2006
Earlier work this paper cites.
The epoch-greedy algorithm for multi-armed bandits with side information
John Langford and Tong Zhang. 2007 · 2007
Earlier work this paper cites.
Reinforcement learning for mapping instructions to actions
S.R.K. Branavan, Harr Chen, Luke Zettlemoyer, and Regina Barzilay. 2009 · 2009
Earlier work this paper cites.
Reading between the lines: Learning to map high-level instructions to commands
S.R.K. Branavan, Luke Zettlemoyer, and Regina Barzilay. 2010 · 2010
Earlier work this paper cites.
Following directions using statistical machine translation
Cynthia Matuszek, Dieter Fox, and Karl Koscher. 2010 · 2010
Earlier work this paper cites.
Learning to follow navigational directions
Adam Vogel and Daniel Jurafsky. 2010 · 2010
Earlier work this paper cites.
Learning to interpret natural language navigation instructions from observations
David L. Chen and Raymond J. Mooney. 2011 · 2011
Cited alongside, same era.
Understanding natural language commands for robotic navigation and mobile manipulation
Stefanie Tellex, Thomas Kollar, Steven Dickerson, Matthew Walter, Ashis G. Banerjee, Seth Teller, and Nicholas Roy. 2011 · 2011
Cited alongside, same era.
Unsupervised PCFG induction for grounded language learning with highly ambiguous supervision
Joohyun Kim and Raymond Mooney. 2012 · 2012
Cited alongside, same era.
Learning to parse natural language commands to a robot control system
Cynthia Matuszek, Evan Herbst, Luke S. Zettlemoyer, and Dieter Fox. 2012 · 2012
Cited alongside, same era.
Weakly supervised learning of semantic parsers for mapping instructions to actions
Yoav Artzi and Luke Zettlemoyer. 2013 · 2013
Cited alongside, same era.
Robobarista: Object part based transfer of manipulation trajectories from crowd-sourcing in 3d pointclouds
Jaeyong Sung, Seok Hyun Jin, and Ashutosh Saxena. 2015 · 2015
Later among the works it cites.
Show, attend and tell: Neural image caption generation with visual attention
Kelvin Xu, Jimmy Ba, Jamie Ryan Kiros, Kyunghyun Cho, Aaron C. Courville, Ruslan Salakhutdinov, Richard S. Zemel, and Yoshua Bengio. 2015 · 2015
Later among the works it cites.
Learning to compose neural networks for question answering
Jacob Andreas, Marcus Rohrbach, Trevor Darrell, and Dan Klein. 2016a · 2016
Later among the works it cites.
Natural language communication with robots
Yonatan Bisk, Deniz Yuret, and Daniel Marcu. 2016 · 2016
Later among the works it cites.
Bootstrap, review, decode: Using out-of-domain textual data to improve image captioning
Wenhu Chen, Aurélien Lucchi, and Thomas Hofmann. 2016 · 2016
Later among the works it cites.
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Joohyun Kim and Raymond Mooney. 2013 · 2013
Cited alongside, same era.
Reinforcement learning in robotics: A survey
Jens Kober, J. Andrew Bagnell, and Jan Peters. 2013 · 2013
Cited alongside, same era.
Playing atari with deep reinforcement learning
Volodymyr Mnih, Koray Kavukcuoglu, David Silver, Alex Graves, Ioannis Antonoglou, Daan Wierstra, and Martin A. Riedmiller. 2013 · 2013
Cited alongside, same era.
Taming the monster: A fast and simple algorithm for contextual bandits
Alekh Agarwal, Daniel J. Hsu, Satyen Kale, John Langford, Lihong Li, and Robert E. Schapire. 2014 · 2014
Cited alongside, same era.
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Yoav Artzi, Dipanjan Das, and Slav Petrov. 2014a · 2014
Cited alongside, same era.
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Later among the works it cites.
Deep reinforcement learning with a natural language action space
Ji He, Jianshu Chen, Xiaodong He, Jianfeng Gao, Lihong Li, Li Deng, and Mari Ostendorf. 2016 · 2016
Later among the works it cites.
CLEVR: A diagnostic dataset for compositional language and elementary visual reasoning
Justin Johnson, Bharath Hariharan, Laurens van der Maaten, Li Fei-Fei, C. Lawrence Zitnick, and Ross B. Girshick. 2016 · 2016
Later among the works it cites.
PAC reinforcement learning with rich observations
Akshay Krishnamurthy, Alekh Agarwal, and John Langford. 2016 · 2016
Later among the works it cites.
End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel. 2016 · 2016
Later among the works it cites.
Deep reinforcement learning for dialogue generation
Jiwei Li, Will Monroe, Alan Ritter, Dan Jurafsky, Michel Galley, and Jianfeng Gao. 2016 · 2016
Later among the works it cites.
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Hongyuan Mei, Mohit Bansal, and R. Matthew Walter. 2016 · 2016
Later among the works it cites.
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Dipendra K. Misra, Jaeyong Sung, Kevin Lee, and Ashutosh Saxena. 2016 · 2016
Later among the works it cites.
Asynchronous methods for deep reinforcement learning
Volodymyr Mnih, Adrià Puigdomènech Badia, Mehdi Mirza, Alex Graves, Timothy P. Lillicrap, Tim Harley, David Silver, and Koray Kavukcuoglu. 2016 · 2016
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Improving information extraction by acquiring external evidence with reinforcement learning
Karthik Narasimhan, Adam Yala, and Regina Barzilay. 2016 · 2016
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Control of memory, active perception, and action in minecraft
Junhyuk Oh, Valliappa Chockalingam, Satinder P. Singh, and Honglak Lee. 2016 · 2016
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Efficient grounding of abstract spatial concepts for natural language interaction with robot manipulators
Rohan Paul, Jacob Arkin, Nicholas Roy, and Thomas M. Howard. 2016 · 2016
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Sim-to-real robot learning from pixels with progressive nets
Andrei A. Rusu, Matej Vecerik, Thomas Rothörl, Nicolas Heess, Razvan Pascanu, and Raia Hadsell. 2016 · 2016
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Mastering the game of go with deep neural networks and tree search
David Silver, Aja Huang, Chris J Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, Ioannis Antonoglou, Veda Panneershelvam, Marc Lanctot, Sander Dieleman, Dominik Grewe, John Nham, Nal Kalchbrenner, Ilya Sutskever, Timothy Lillicrap, Madeleine Leach, Koray Kavukcuoglu, Thore Graepel, and Demis Hassabis. 2016 · 2016
Later among the works it cites.
A corpus of compositional language for visual reasoning
Alane Suhr, Mike Lewis, James Yeh, and Yoav Artzi. 2017 · 2017
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