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Recent large language models (LLMs) have demonstrated remarkable performance on a variety of natural language processing (NLP) tasks, leading to intense excitement about their applicability across various domains.
Blocks world revisited
John Slaney and Sylvie Thiébaux · 2001
Earlier work this paper cites.
Pddl2. 1: An extension to pddl for expressing temporal planning domains
Maria Fox and Derek Long · 2003
Earlier work this paper cites.
The fast downward planning system
Malte Helmert · 2006
Earlier work this paper cites.
A concise introduction to models and methods for automated planning
Hector Geffner and Blai Bonet · 2013
Earlier work this paper cites.
Bert: Pre-training of deep bidirectional transformers for language understanding
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova · 2018
Earlier work this paper cites.
Extracting action sequences from texts based on deep reinforcement learning
Wenfeng Feng, Hankz Hankui Zhuo, and Subbarao Kambhampati · 2018
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Sequence-to-sequence language grounding of non-markovian task specifications
Nakul Gopalan, Dilip Arumugam, Lawson LS Wong, and Stefanie Tellex · 2018
Earlier work this paper cites.
Language models are few-shot learners
Tom Brown, Benjamin Mann, Nick Ryder, Melanie Subbiah, Jared D Kaplan, Prafulla Dhariwal, Arvind Neelakantan, Pranav Shyam, Girish Sastry, Amanda Askell, et al · 2020
Earlier work this paper cites.
Nltopddl: One-shot learning of pddl models from natural language process manuals
Shivam Miglani and Neil Yorke-Smith · 2020
Earlier work this paper cites.
Grounding language to non-markovian tasks with no supervision of task specifications
Roma Patel, Ellie Pavlick, and Stefanie Tellex · 2020
Earlier work this paper cites.
Exploring the limits of transfer learning with a unified text-to-text transformer
Colin Raffel, Noam Shazeer, Adam Roberts, Katherine Lee, Sharan Narang, Michael Matena, Yanqi Zhou, Wei Li, and Peter J Liu · 2020
Cited alongside, same era.
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
Cited alongside, same era.
Planning domain generation from natural language step-by-step instructions
Maurício Steinert and Felipe Rech Meneguzzi · 2020
Cited alongside, same era.
Evaluating large language models trained on code
Mark Chen, Jerry Tworek, Heewoo Jun, Qiming Yuan, Henrique Ponde de Oliveira Pinto, Jared Kaplan, Harri Edwards, Yuri Burda, Nicholas Joseph, Greg Brockman, et al · 2021
Cited alongside, same era.
Gpt3-to-plan: Extracting plans from text using gpt-3
Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati · 2021
Pre-trained language models for interactive decision-making
Shuang Li, Xavier Puig, Yilun Du, Clinton Wang, Ekin Akyurek, Antonio Torralba, Jacob Andreas, and Igor Mordatch · 2022
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Lang2ltl: Translating natural language commands to temporal specification with large language models
Jason Xinyu Liu, Ziyi Yang, Benjamin Schornstein, Sam Liang, Ifrah Idrees, Stefanie Tellex, and Ankit Shah · 2022
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Plansformer: Generating symbolic plans using transformers
Vishal Pallagani, Bharath Muppasani, Keerthiram Murugesan, Francesca Rossi, Lior Horesh, Biplav Srivastava, Francesco Fabiano, and Andrea Loreggia · 2022
Later among the works it cites.
Planning with large language models via corrective re-prompting
Shreyas Sundara Raman, Vanya Cohen, Eric Rosen, Ifrah Idrees, David Paulius, and Stefanie Tellex · 2022
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PDDL planning with pretrained large language models
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Cited alongside, same era.
Do as I can, not as I say: Grounding language in robotic affordances
Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alexander Herzog, Daniel Ho, Jasmine Hsu, Julian Ibarz, Brian Ichter, Alex Irpan, Eric Jang, Rosario Jauregui Ruano, Kyle Jeffrey, Sally Jesmonth, Nikhil J. Joshi, Ryan Julian, Dmitry Kalashnikov, Yuheng Kuang, Kuang-Huei Lee, Sergey Levine, Yao Lu, Linda Luu, Carolina Parada, Peter Pastor, Jornell Quiambao, Kanishka Rao, Jarek Rettinghouse, Diego Reyes, Pierre Sermanet, Nicolas Sievers, Clayton Tan, Alexander Toshev, Vincent Vanhoucke, Fei Xia, Ted Xiao, Peng Xu, Sichun Xu, and Mengyuan Yan · 2022
Cited alongside, same era.
Palm: Scaling language modeling with pathways
Aakanksha Chowdhery, Sharan Narang, Jacob Devlin, Maarten Bosma, Gaurav Mishra, Adam Roberts, Paul Barham, Hyung Won Chung, Charles Sutton, Sebastian Gehrmann, et al · 2022
Cited alongside, same era.
Katherine M. Collins, Catherine Wong, Jiahai Feng, Megan Wei, and Joshua B. Tenenbaum · 2022
Cited alongside, same era.
Towards reasoning in large language models: A survey
Jie Huang and Kevin Chen-Chuan Chang · 2022
Cited alongside, same era.
URL https://planning.wiki/
Planning.wiki - the ai planning & pddl wiki
Cited in the paper.
Open-vocabulary queryable scene representations for real world planning
Boyuan Chen, Fei Xia, Brian Ichter, Kanishka Rao, Keerthana Gopalakrishnan, Michael S. Ryoo, Austin Stone, and Daniel Kappler
Cited in the paper.
Mirror: Differentiable deep social projection for assistive human-robot communication
Kaiqi Chen, Jeffrey Fong, and Harold Soh
Cited in the paper.
Tom Silver, Varun Hariprasad, Reece S Shuttleworth, Nishanth Kumar, Tomás Lozano-Pérez, and Leslie Pack Kaelbling · 2022
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Lamda: Language models for dialog applications
Romal Thoppilan, Daniel De Freitas, Jamie Hall, Noam Shazeer, Apoorv Kulshreshtha, Heng-Tze Cheng, Alicia Jin, Taylor Bos, Leslie Baker, Yu Du, et al · 2022
Later among the works it cites.
Large language models still can’t plan (a benchmark for LLMs on planning and reasoning about change)
Karthik Valmeekam, Alberto Olmo, Sarath Sreedharan, and Subbarao Kambhampati · 2022
Later among the works it cites.
Nl2ltl–a python package for converting natural language (nl) instructions to linear temporal logic (ltl) formulas
Francesco Fuggitti and Tathagata Chakraborti · 2023
Closest in time.
Dissociating language and thought in large language models: a cognitive perspective
Kyle Mahowald, Anna A. Ivanova, Idan A. Blank, Nancy Kanwisher, Joshua B. Tenenbaum, and Evelina Fedorenko · 2023
Closest in time.