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Language instructions and demonstrations are two natural ways for users to teach robots personalized tasks.
Procedures as a representation for data in a computer program for understanding natural language
Terry Winograd · 1971
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Bleu: a method for automatic evaluation of machine translation
Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu · 2002
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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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Toward understanding natural language directions
Thomas Kollar, Stefanie Tellex, Deb Roy, and Nicholas Roy · 2010
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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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Luke S Zettlemoyer and Michael Collins · 2012
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Learning to parse natural language commands to a robot control system
Cynthia Matuszek, Evan Herbst, Luke Zettlemoyer, and Dieter Fox · 2013
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Grounding english commands to reward functions
Shawn Squire, Stefanie Tellex, Dilip Arumugam, and Lei Yang · 2015
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Learning to interpret natural language commands through human-robot dialog
Jesse Thomason, Shiqi Zhang, Raymond Mooney, and Peter Stone · 2015
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Deepcoder: Learning to write programs
Matej Balog, Alexander L Gaunt, Marc Brockschmidt, Sebastian Nowozin, and Daniel Tarlow · 2016
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Social robotics
Cynthia Breazeal, Kerstin Dautenhahn, and Takayuki Kanda · 2016
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Openai gym, 2016
Greg Brockman, Vicki Cheung, Ludwig Pettersson, Jonas Schneider, John Schulman, Jie Tang, and Wojciech Zaremba · 2016
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Neuro-symbolic program synthesis
Emilio Parisotto, Abdel-rahman Mohamed, Rishabh Singh, Lihong Li, Dengyong Zhou, and Pushmeet Kohli · 2016
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Jacob Andreas, Dan Klein, and Sergey Levine · 2017
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Robustfill: Neural program learning under noisy i/o
Jacob Devlin, Jonathan Uesato, Surya Bhupatiraju, Rishabh Singh, Abdel-rahman Mohamed, and Pushmeet Kohli · 2017
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From language to programs: Bridging reinforcement learning and maximum marginal likelihood
Kelvin Guu, Panupong Pasupat, Evan Zheran Liu, and Percy Liang · 2017
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Mapping instructions and visual observations to actions with reinforcement learning
Dipendra Kumar Misra, John Langford, and Yoav Artzi · 2017
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Neural scene de-rendering
Jiajun Wu, Joshua B. Tenenbaum, and Pushmeet Kohli · 2017
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A syntactic neural model for general-purpose code generation
Pengcheng Yin and Graham Neubig · 2017
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Learning to infer graphics programs from hand-drawn images
Kevin Ellis, Daniel Ritchie, Armando Solar-Lezama, and Josh Tenenbaum · 2018
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Synthesizing programs for images using reinforced adversarial learning
Yaroslav Ganin, Tejas Kulkarni, Igor Babuschkin, SM Ali Eslami, and Oriol Vinyals · 2018
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Mapping instructions to actions in 3d environments with visual goal prediction
Dipendra Misra, Andrew Bennett, Valts Blukis, Eyvind Niklasson, Max Shatkhin, and Yoav Artzi · 2018
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Bayesian inference of temporal task specifications from demonstrations
Ankit Shah, Pritish Kamath, Julie A Shah, and Shen Li · 2018
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Neural program synthesis from diverse demonstration videos
Shao-Hua Sun, Hyeonwoo Noh, Sriram Somasundaram, and Joseph Lim · 2018
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SQLNet: Generating structured queries from natural language without reinforcement learning, 2018
Xiaojun Xu, Chang Liu, and Dawn Song · 2018
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Self-educated language agent with hindsight experience replay for instruction following
Geoffrey Cideron, Mathieu Seurin, Florian Strub, and Olivier Pietquin · 2019
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Relay policy learning: Solving long horizon tasks via imitation and reinforcement learning
Abhishek Gupta, Vikash Kumar, Corey Lynch, Sergey Levine, and Karol Hausman · 2019
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Language as an abstraction for hierarchical deep reinforcement learning
Yiding Jiang, Shixiang Shane Gu, Kevin P Murphy, and Chelsea Finn · 2019
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Learning to describe scenes with programs
Yunchao Liu, Jiajun Wu, Zheng Wu, Daniel Ritchie, William T. Freeman, and Joshua B. Tenenbaum · 2019
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A survey of reinforcement learning informed by natural language
Jelena Luketina, Nantas Nardelli, Gregory Farquhar, Jakob N. Foerster, Jacob Andreas, Edward Grefenstette, S. Whiteson, and Tim Rocktäschel · 2019
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Specification patterns for robotic missions, 2019
Claudio Menghi, Christos Tsigkanos, Patrizio Pelliccione, Carlo Ghezzi, and Thorsten Berger · 2019
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Multi-hop reading comprehension through question decomposition and rescoring
Sewon Min, Victor Zhong, Luke Zettlemoyer, and Hannaneh Hajishirzi · 2019
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Learning to infer and execute 3d shape programs
Yonglong Tian, Andrew Luo, Xingyuan Sun, Kevin Ellis, William T. Freeman, Joshua B. Tenenbaum, and Jiajun Wu · 2019
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Can foundation models perform zero-shot task specification for robot manipulation?
Yuchen Cui, Scott Niekum, Abhinav Gupta, Vikash Kumar, and Aravind Rajeswaran · 2022
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Rescaling egocentric vision: Collection, pipeline and challenges for epic-kitchens-100
Dima Damen, Hazel Doughty, Giovanni Maria Farinella, Antonino Furnari, Jian Ma, Evangelos Kazakos, Davide Moltisanti, Jonathan Munro, Toby Perrett, Will Price, and Michael Wray · 2022
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Language models as zero-shot planners: Extracting actionable knowledge for embodied agents
Wenlong Huang, Pieter Abbeel, Deepak Pathak, and Igor Mordatch · 2022
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Inner monologue: Embodied reasoning through planning with language models
Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, Pierre Sermanet, Noah Brown, Tomas Jackson, Linda Luu, Sergey Levine, Karol Hausman, and Brian Ichter · 2022
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Ahmed Akakzia, Cédric Colas, Pierre-Yves Oudeyer, Mohamed Chetouani, and Olivier Sigaud · 2020
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PyMT5: multi-mode translation of natural language and python code with transformers
Colin Clement, Dawn Drain, Jonathan Timcheck, Alexey Svyatkovskiy, and Neel Sundaresan · 2020
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CodeBERT: A pre-trained model for programming and natural languages
Zhangyin Feng, Daya Guo, Duyu Tang, Nan Duan, Xiaocheng Feng, Ming Gong, Linjun Shou, Bing Qin, Ting Liu, Daxin Jiang, and Ming Zhou · 2020
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Pixl2r: Guiding reinforcement learning using natural language by mapping pixels to rewards
Prasoon Goyal, Scott Niekum, and Raymond J Mooney · 2020
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Transferable task execution from pixels through deep planning domain learning, 2020
Kei Kase, Chris Paxton, Hammad Mazhar, Tetsuya Ogata, and Dieter Fox · 2020
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Language conditioned imitation learning over unstructured data
Corey Lynch and Pierre Sermanet · 2020
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Unsupervised question decomposition for question answering
Ethan Perez, Patrick Lewis, Wen-tau Yih, Kyunghyun Cho, and Douwe Kiela · 2020
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Do as i can, not as i say: Grounding language in robotic affordances
Brian Ichter, Anthony Brohan, Yevgen Chebotar, Chelsea Finn, Karol Hausman, Alexander Herzog, Daniel Ho, Julian Ibarz, Alex Irpan, Eric Jang, Ryan Julian, Dmitry Kalashnikov, Sergey Levine, Yao Lu, Carolina Parada, Kanishka Rao, Pierre Sermanet, Alexander T Toshev, Vincent Vanhoucke, Fei Xia, Ted Xiao, Peng Xu, Mengyuan Yan, Noah Brown, Michael Ahn, Omar Cortes, Nicolas Sievers, Clayton Tan, Sichun Xu, Diego Reyes, Jarek Rettinghouse, Jornell Quiambao, Peter Pastor, Linda Luu, Kuang-Huei Lee, Yuheng Kuang, Sally Jesmonth, Kyle Jeffrey, Rosario Jauregui Ruano, Jasmine Hsu, Keerthana Gopalakrishnan, Byron David, Andy Zeng, and Chuyuan Kelly Fu · 2022
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Bc-z: Zero-shot task generalization with robotic imitation learning
Eric Jang, Alex Irpan, Mohi Khansari, Daniel Kappler, Frederik Ebert, Corey Lynch, Sergey Levine, and Chelsea Finn · 2022
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Large language models are zero-shot reasoners
Takeshi Kojima, Shixiang Shane Gu, Machel Reid, Yutaka Matsuo, and Yusuke Iwasawa · 2022
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Coderl: Mastering code generation through pretrained models and deep reinforcement learning
Hung Le, Yue Wang, Akhilesh Deepak Gotmare, Silvio Savarese, and Steven CH Hoi · 2022
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Code as policies: Language model programs for embodied control
Jacky Liang, Wenlong Huang, Fei Xia, Peng Xu, Karol Hausman, Brian Ichter, Pete Florence, and Andy Zeng · 2022
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Grounding predicates through actions, 2022
Toki Migimatsu and Jeannette Bohg · 2022
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Learning language-conditioned robot behavior from offline data and crowd-sourced annotation
Suraj Nair, Eric Mitchell, Kevin Chen, Silvio Savarese, Chelsea Finn, et al · 2022
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Codegen: An open large language model for code with multi-turn program synthesis
Erik Nijkamp, Bo Pang, Hiroaki Hayashi, Lifu Tu, Huan Wang, Yingbo Zhou, Silvio Savarese, and Caiming Xiong · 2022
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Synchromesh: Reliable code generation from pre-trained language models
Gabriel Poesia, Alex Polozov, Vu Le, Ashish Tiwari, Gustavo Soares, Christopher Meek, and Sumit Gulwani · 2022
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Planning with large language models via corrective re-prompting, 2022
Shreyas Sundara Raman, Vanya Cohen, Eric Rosen, Ifrah Idrees, David Paulius, and Stefanie Tellex · 2022
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Lm-nav: Robotic navigation with large pre-trained models of language, vision, and action, 2022
Dhruv Shah, Blazej Osinski, Brian Ichter, and Sergey Levine · 2022
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Correcting robot plans with natural language feedback
Pratyusha Sharma, Balakumar Sundaralingam, Valts Blukis, Chris Paxton, Tucker Hermans, Antonio Torralba, Jacob Andreas, and Dieter Fox · 2022
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PDDL planning with pretrained large language models
Tom Silver, Varun Hariprasad, Reece S Shuttleworth, Nishanth Kumar, Tomás Lozano-Pérez, and Leslie Pack Kaelbling · 2022
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Progprompt: Generating situated robot task plans using large language models, 2022
Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, and Animesh Garg · 2022
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Chain of thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Brian Ichter, Fei Xia, Ed Chi, Quoc Le, and Denny Zhou · 2022
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Socratic models: Composing zero-shot multimodal reasoning with language
Andy Zeng, Adrian Wong, Stefan Welker, Krzysztof Choromanski, Federico Tombari, Aveek Purohit, Michael Ryoo, Vikas Sindhwani, Johnny Lee, Vincent Vanhoucke, et al · 2022
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Least-to-most prompting enables complex reasoning in large language models
Denny Zhou, Nathanael Schärli, Le Hou, Jason Wei, Nathan Scales, Xuezhi Wang, Dale Schuurmans, Olivier Bousquet, Quoc Le, and Ed Chi · 2022
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Language models can solve computer tasks, 2023
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Text2motion: From natural language instructions to feasible plans, 2023
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