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Imitation learning and instruction-following are two common approaches to communicate a user's intent to a learning agent.
“Alvinn: An autonomous land vehicle in a neural network”, 1989
Dean Pomerleau · 1989
Earlier work this paper cites.
“The symbol grounding problem”
Stevan Harnad · 1990
Earlier work this paper cites.
“Apprenticeship learning via inverse reinforcement learning”
Pieter Abbeel and Andrew Ng · 2004
Earlier work this paper cites.
“Walk the talk: Connecting language, knowledge, and action in route instructions”
Matt MacMahon, Brian Stankiewicz and Benjamin Kuipers · 2006
Earlier work this paper cites.
“Bayesian Inverse Reinforcement Learning.”
Deepak Ramachandran and Eyal Amir · 2007
Earlier work this paper cites.
“Maximum entropy inverse reinforcement learning.”
Brian Ziebart, Andrew Maas, J Bagnell and Anind Dey · 2008
Earlier work this paper cites.
“A survey of robot learning from demonstration”
Brenna Argall, Sonia Chernova, Manuela Veloso and Brett Browning · 2009
Earlier work this paper cites.
“A survey on transfer learning”
Sinno Pan and Qiang Yang · 2009
Earlier work this paper cites.
“Understanding the difficulty of training deep feedforward neural networks”
Xavier Glorot and Yoshua Bengio · 2010
Earlier work this paper cites.
“Efficient reductions for imitation learning”
Stéphane Ross and Drew Bagnell · 2010
Earlier work this paper cites.
“Learning to follow navigational directions”
Adam Vogel and Dan Jurafsky · 2010
Earlier work this paper cites.
“Learning to interpret natural language navigation instructions from observations”
David Chen and Raymond Mooney · 2011
Earlier work this paper cites.
“A reduction of imitation learning and structured prediction to no-regret online learning”
Stéphane Ross, Geoffrey Gordon and Drew Bagnell · 2011
Earlier work this paper cites.
“Understanding natural language commands for robotic navigation and mobile manipulation”
Stefanie Tellex, Thomas Kollar, Steven Dickerson, Matthew Walter, Ashis Banerjee, Seth Teller and Nicholas Roy · 2011
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“Learning to win by reading manuals in a Monte-Carlo framework”
SRK Branavan, David Silver and Regina Barzilay · 2012
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“Learning models for following natural language directions in unknown environments”
Sachithra Hemachandra, Felix Duvallet, Thomas Howard, Nicholas Roy, Anthony Stentz and Matthew Walter · 2015
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“Adam: A method for stochastic optimization”
Diederik Kingma and Jimmy Ba · 2015
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“Guided cost learning: Deep inverse optimal control via policy optimization”
Chelsea Finn, Sergey Levine and Pieter Abbeel · 2016
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Karthik Narasimhan, Regina Barzilay and Tommi Jaakkola · 2018
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“Robobarista: Object part based transfer of manipulation trajectories from crowd-sourcing in 3d pointclouds”
Jaeyong Sung, Seok Jin and Ashutosh Saxena · 2018
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Joaquin Vanschoren · 2018
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“Using natural language for reward shaping in reinforcement learning”
Prasoon Goyal, Scott Niekum and Raymond Mooney · 2019
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“A review of robot learning for manipulation: Challenges, representations, and algorithms”
Oliver Kroemer, Scott Niekum and George Konidaris · 2019
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Jonathan Ho and Stefano Ermon · 2016
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“Tell me dave: Context-sensitive grounding of natural language to manipulation instructions”
Dipendra Misra, Jaeyong Sung, Kevin Lee and Ashutosh Saxena · 2016
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“Contextual awareness: Understanding monologic natural language instructions for autonomous robots”
Jacob Arkin, Matthew Walter, Adrian Boteanu, Michael Napoli, Harel Biggie, Hadas Kress-Gazit and Thomas Howard · 2017
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“Learning robust rewards with adversarial inverse reinforcement learning”
Justin Fu, Katie Luo and Sergey Levine · 2017
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Russell Kaplan, Christopher Sauer and Alexander Sosa · 2017
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Peter Anderson, Qi Wu, Damien Teney, Jake Bruce, Mark Johnson, Niko Sünderhauf, Ian Reid, Stephen Gould and Anton Van · 2018
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“Learning with latent language”
Jacob Andreas, Dan Klein and Sergey Levine · 2018
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“A Survey of Reinforcement Learning Informed by Natural Language”
Jelena Luketina, Nantas Nardelli, Gregory Farquhar, Jakob Foerster, Jacob Andreas, Edward Grefenstette, Shimon Whiteson and Tim Rocktaschel · 2019
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“Reinforced cross-modal matching and self-supervised imitation learning for vision-language navigation”
Xin Wang, Qiuyuan Huang, Asli Celikyilmaz, Jianfeng Gao, Dinghan Shen, Yuan-Fang Wang, William Wang and Lei Zhang · 2019
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“PixL2R: Guiding Reinforcement Learning Using Natural Language by Mapping Pixels to Rewards”
Prasoon Goyal, Scott Niekum and Raymond Mooney · 2020
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“Alfred: A benchmark for interpreting grounded instructions for everyday tasks”
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”
Simon Stepputtis, Joseph Campbell, Mariano Phielipp, Stefan Lee, Chitta Baral and Heni Amor · 2020
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“Generalizing from a few examples: A survey on few-shot learning”
Yaqing Wang, Quanming Yao, James Kwok and Lionel Ni · 2020
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“robosuite: A modular simulation framework and benchmark for robot learning”
Yuke Zhu, Josiah Wong, Ajay Mandlekar and Roberto Martin-Martin · 2020
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“Grounding Language to Entities and Dynamics for Generalization in Reinforcement Learning”
HJ Wang and Karthik Narasimhan · 2021
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