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Learning policies that effectively utilize language instructions in complex, multi-task environments is an important problem in sequential decision-making.
Learning latent plans from play
Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, and Pierre Sermanet · 1903
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Learning latent plans from play
Corey Lynch, Mohi Khansari, Ted Xiao, Vikash Kumar, Jonathan Tompson, Sergey Levine, and Pierre Sermanet · 1903
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Efficient training of artificial neural networks for autonomous navigation
Dean A Pomerleau · 1991
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Quantization
R.M. Gray and D.L. Neuhoff · 1998
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Between mdps and semi-mdps: A framework for temporal abstraction in reinforcement learning
Richard Sutton, Doina Precup, and Satinder Singh · 1999
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Variational information maximization for neural coding
Felix Agakov and David Barber · 2004
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Language conditioned imitation learning over unstructured data
Corey Lynch and Pierre Sermanet · 2005
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Walk the talk: Connecting language, knowledge, and action in route instructions
Matt MacMahon, Brian Stankiewicz, and Benjamin Kuipers · 2006
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Efficient reductions for imitation learning
Stéphane Ross and Drew Bagnell · 2010
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A reduction of imitation learning and structured prediction to no-regret online learning
Stéphane Ross, Geoffrey Gordon, and Drew Bagnell · 2011
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Generative adversarial imitation learning, 2016
Jonathan Ho and Stefano Ermon · 2016
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Learning language games through interaction, 2016
Sida I. Wang, Percy Liang, and Christopher D. Manning · 2016
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Accurately and efficiently interpreting human-robot instructions of varying granularities
Dilip Arumugam, Siddharth Karamcheti, Nakul Gopalan, Lawson Wong, and Stefanie Tellex · 2017
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One-Shot imitation learning
Yan Duan, Marcin Andrychowicz, Bradly Stadie, Openai Jonathan Ho, Jonas Schneider, Ilya Sutskever, Pieter Abbeel, and Wojciech Zaremba · 2017
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One-shot visual imitation learning via meta-learning, 2017
Chelsea Finn, Tianhe Yu, Tianhao Zhang, Pieter Abbeel, and Sergey Levine · 2017
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Grounded language learning in a simulated 3d world, 2017
Karl Moritz Hermann, Felix Hill, Simon Green, Fumin Wang, Ryan Faulkner, Hubert Soyer, David Szepesvari, Wojciech Marian Czarnecki, Max Jaderberg, Denis Teplyashin, Marcus Wainwright, Chris Apps, Demis Hassabis, and Phil Blunsom · 2017
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Infogail: Interpretable imitation learning from visual demonstrations, 2017
Yunzhu Li, Jiaming Song, and Stefano Ermon · 2017
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Mapping instructions and visual observations to actions with reinforcement learning, 2017
Dipendra Misra, John Langford, and Yoav Artzi · 2017
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Zero-shot task generalization with multi-task deep reinforcement learning, 2017
Junhyuk Oh, Satinder Singh, Honglak Lee, and Pushmeet Kohli · 2017
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Following high-level navigation instructions on a simulated quadcopter with imitation learning, 2018
Valts Blukis, Nataly Brukhim, Andrew Bennett, Ross A. Knepper, and Yoav Artzi · 2018
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Gated-attention architectures for task-oriented language grounding, 2018
Devendra Singh Chaplot, Kanthashree Mysore Sathyendra, Rama Kumar Pasumarthi, Dheeraj Rajagopal, and Ruslan Salakhutdinov · 2018
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Diversity is all you need: Learning skills without a reward function, 2018
Benjamin Eysenbach, Abhishek Gupta, Julian Ibarz, and Sergey Levine · 2018
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Speaker-follower models for vision-and-language navigation, 2018
Daniel Fried, Ronghang Hu, Volkan Cirik, Anna Rohrbach, Jacob Andreas, Louis-Philippe Morency, Taylor Berg-Kirkpatrick, Kate Saenko, Dan Klein, and Trevor Darrell · 2018
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Mapping instructions to actions in 3d environments with visual goal prediction, 2019
Dipendra Misra, Andrew Bennett, Valts Blukis, Eyvind Niklasson, Max Shatkhin, and Yoav Artzi · 2019
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Prospection: Interpretable plans from language by predicting the future, 2019
Chris Paxton, Yonatan Bisk, Jesse Thomason, Arunkumar Byravan, and Dieter Fox · 2019
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Learning to navigate unseen environments: Back translation with environmental dropout, 2019
Hao Tan, Licheng Yu, and Mohit Bansal · 2019
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A multiscale visualization of attention in the transformer model
Jesse Vig · 2019
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A unifying mutual information view of metric learning: cross-entropy vs. pairwise losses
Malik Boudiaf, Jérôme Rony, Imtiaz Masud Ziko, Eric Granger, Marco Pedersoli, Pablo Piantanida, and Ismail Ben Ayed · 2020
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Data-efficient hierarchical reinforcement learning, 2018
Ofir Nachum, Shixiang Gu, Honglak Lee, and Sergey Levine · 2018
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Visual reinforcement learning with imagined goals, 2018
Ashvin Nair, Vitchyr Pong, Murtaza Dalal, Shikhar Bahl, Steven Lin, and Sergey Levine · 2018
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Situated mapping of sequential instructions to actions with single-step reward observation, 2018
Alane Suhr and Yoav Artzi · 2018
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Neural discrete representation learning, 2018
Aaron van den Oord, Oriol Vinyals, and Koray Kavukcuoglu · 2018
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Neural task programming: Learning to generalize across hierarchical tasks
Danfei Xu, Suraj Nair, Yuke Zhu, Julian Gao, Animesh Garg, Li Fei-Fei, and Silvio Savarese · 2018
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One-shot imitation from observing humans via domain-adaptive meta-learning, 2018
Tianhe Yu, Chelsea Finn, Annie Xie, Sudeep Dasari, Tianhao Zhang, Pieter Abbeel, and Sergey Levine · 2018
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Grounding natural language instructions to semantic goal representations for abstraction and generalization
Dilip Arumugam, Siddharth Karamcheti, Nakul Gopalan, Edward C. Williams, Mina Rhee, Lawson L. Wong, and Stefanie Tellex · 2019
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Tom B. Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, Sandhini Agarwal, Ariel Herbert-Voss, Gretchen Krueger, Tom Henighan, Rewon Child, Aditya Ramesh, Daniel M. Ziegler, Jeffrey Wu, Clemens Winter, Christopher Hesse, Mark Chen, Eric Sigler, Mateusz Litwin, Scott Gray, Benjamin Chess, Jack Clark, Christopher Berner, Sam McCandlish, Alec Radford, Ilya Sutskever, and Dario Amodei · 2020
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Touchdown: Natural language navigation and spatial reasoning in visual street environments, 2020
Howard Chen, Alane Suhr, Dipendra Misra, Noah Snavely, and Yoav Artzi · 2020
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Sub-policy adaptation for hierarchical reinforcement learning, 2020
Alexander C. Li, Carlos Florensa, Ignasi Clavera, and Pieter Abbeel · 2020
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Distilbert, a distilled version of bert: smaller, faster, cheaper and lighter, 2020
Victor Sanh, Lysandre Debut, Julien Chaumond, and Thomas Wolf · 2020
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Dynamics-aware unsupervised discovery of skills, 2020
Archit Sharma, Shixiang Gu, Sergey Levine, Vikash Kumar, and Karol Hausman · 2020
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Alfred: A benchmark for interpreting grounded instructions for everyday tasks, 2020
Mohit Shridhar, Jesse Thomason, Daniel Gordon, Yonatan Bisk, Winson Han, Roozbeh Mottaghi, Luke Zettlemoyer, and Dieter Fox · 2020
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Language-conditioned imitation learning for robot manipulation tasks, 2020
Simon Stepputtis, Joseph Campbell, Mariano Phielipp, Stefan Lee, Chitta Baral, and Heni Ben Amor · 2020
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Iq-learn: Inverse soft-q learning for imitation
Divyansh Garg, Shuvam Chakraborty, Chris Cundy, Jiaming Song, and Stefano Ermon · 2021
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Mt-opt: Continuous multi-task robotic reinforcement learning at scale, 2021
Dmitry Kalashnikov, Jacob Varley, Yevgen Chebotar, Benjamin Swanson, Rico Jonschkowski, Chelsea Finn, Sergey Levine, and Karol Hausman · 2021
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Oier Mees, Lukas Hermann, Erick Rosete-Beas, and Wolfram Burgard · 2021
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Learning language-conditioned robot behavior from offline data and crowd-sourced annotation, 2021
Suraj Nair, Eric Mitchell, Kevin Chen, Brian Ichter, Silvio Savarese, and Chelsea Finn · 2021
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Hierarchical reinforcement learning by discovering intrinsic options, 2021
Jesse Zhang, Haonan Yu, and Wei Xu · 2021
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