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Motivated by the substantial achievements observed in Large Language Models (LLMs) in the field of natural language processing, recent research has commenced investigations into the application of LLMs for complex, long-horizon sequential task planning challenges in robotics.
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 · 1901
Earlier work this paper cites.
Logic-Geometric Programming: An Optimization-Based Approach to Combined Task and Motion Planning.. In IJCAI . 1930–1936
Marc Toussaint. 2015 · 1936
Earlier work this paper cites.
Pddl| the planning domain definition language
Constructions Aeronautiques, Adele Howe, Craig Knoblock, ISI Drew McDermott, Ashwin Ram, Manuela Veloso, Daniel Weld, David Wilkins SRI, Anthony Barrett, Dave Christianson, et al · 1998
Earlier work this paper cites.
State-space search: Algorithms, complexity, extensions, and applications
Weixiong Zhang. 1999 · 1999
Earlier work this paper cites.
Blocks world revisited
John Slaney and Sylvie Thiébaux. 2001 · 2001
Earlier work this paper cites.
PDDL2. 1: An extension to PDDL for expressing temporal planning domains
Maria Fox and Derek Long. 2003 · 2003
Earlier work this paper cites.
Learning action strategies for planning domains using genetic programming. In Workshops on Applications of Evolutionary Computation . Springer, 684–695
John Levine and David Humphreys. 2003 · 2003
Earlier work this paper cites.
VAL: Automatic plan validation, continuous effects and mixed initiative planning using PDDL. In 16th IEEE International Conference on Tools with Artificial Intelligence . IEEE, 294–301
Richard Howey, Derek Long, and Maria Fox. 2004 · 2004
Earlier work this paper cites.
Planning with loops. In IJCAI . 509–515
Hector J Levesque. 2005 · 2005
Earlier work this paper cites.
The fast downward planning system
Malte Helmert. 2006 · 2006
Earlier work this paper cites.
Learning domain-specific planners from example plans
Elly Zoe Winner. 2008 · 2008
Earlier work this paper cites.
Search-based planning for manipulation with motion primitives. In 2010 IEEE international conference on robotics and automation . IEEE, 2902–2908
Benjamin J Cohen, Sachin Chitta, and Maxim Likhachev. 2010 · 2010
Earlier work this paper cites.
Answer set programming at a glance
Gerhard Brewka, Thomas Eiter, and Mirosław Truszczyński. 2011 · 2011
Earlier work this paper cites.
Heuristic search: theory and applications
Stefan Edelkamp and Stefan Schrödl. 2011 · 2011
Earlier work this paper cites.
A survey of multi-objective sequential decision-making
Diederik M Roijers, Peter Vamplew, Shimon Whiteson, and Richard Dazeley. 2013 · 2013
Earlier work this paper cites.
Mobile Robot Planning Using Action Language with an Abstraction Hierarchy. In International Conference on Logic Programming and Nonmonotonic Reasoning . Springer, 502–516
Shiqi Zhang, Fangkai Yang, Piyush Khandelwal, and Peter Stone. 2015 · 2015
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 · 2018
Earlier work this paper cites.
Commonsense knowledge mining from pretrained models. In Proceedings of the 2019 conference on empirical methods in natural language processing and the 9th international joint conference on natural language processing (EMNLP-IJCNLP) . 1173–1178
Joe Davison, Joshua Feldman, and Alexander M Rush. 2019 · 2019
Cited alongside, same era.
Hierarchical task and motion planning using logic-geometric programming (hlgp). In RSS Workshop on Robust Task and Motion Planning
Danny Driess, Ozgur Oguz, and Marc Toussaint. 2019 · 2019
Cited alongside, same era.
An introduction to the planning domain definition language . Vol. 13
Patrik Haslum, Nir Lipovetzky, Daniele Magazzeni, Christian Muise, Ronald Brachman, Francesca Rossi, and Peter Stone. 2019 · 2019
Cited alongside, same era.
Task planning in robotics: an empirical comparison of pddl-and asp-based systems
Yu-qian Jiang, Shi-qi Zhang, Piyush Khandelwal, and Peter Stone. 2019 · 2019
Cited alongside, same era.
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, et al · 2022
Later among the works it cites.
Pre-trained language models for interactive decision-making
Shuang Li, Xavier Puig, Chris Paxton, Yilun Du, Clinton Wang, Linxi Fan, Tao Chen, De-An Huang, Ekin Akyürek, Anima Anandkumar, et al · 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 · 2022
Later among the works it cites.
PDDL planning with pretrained large language models. In NeurIPS 2022 Foundation Models for Decision Making Workshop
Tom Silver, Varun Hariprasad, Reece S Shuttleworth, Nishanth Kumar, Tomás Lozano-Pérez, and Leslie Pack Kaelbling. 2022 · 2022
Later among the works it cites.
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Fabio Petroni, Tim Rocktäschel, Patrick Lewis, Anton Bakhtin, Yuxiang Wu, Alexander H Miller, and Sebastian Riedel. 2019 · 2019
Cited alongside, same era.
Language models are unsupervised multitask learners
Alec Radford, Jeffrey Wu, Rewon Child, David Luan, Dario Amodei, Ilya Sutskever, et al · 2019
Cited alongside, same era.
Task-motion planning for safe and efficient urban driving. In 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) . IEEE, 2119–2125
Yan Ding, Xiaohan Zhang, Xingyue Zhan, and Shiqi Zhang. 2020 · 2020
Cited alongside, same era.
Pddlstream: Integrating symbolic planners and blackbox samplers via optimistic adaptive planning. In Proceedings of the International Conference on Automated Planning and Scheduling , Vol. 30. 440–448
Caelan Reed Garrett, Tomás Lozano-Pérez, and Leslie Pack Kaelbling. 2020 · 2020
Cited alongside, same era.
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 · 2020
Cited alongside, same era.
On the opportunities and risks of foundation models
Rishi Bommasani, Drew A Hudson, Ehsan Adeli, Russ Altman, Simran Arora, Sydney von Arx, Michael S Bernstein, Jeannette Bohg, Antoine Bosselut, Emma Brunskill, et al · 2021
Cited alongside, same era.
Integrated task and motion planning
Caelan Reed Garrett, Rohan Chitnis, Rachel Holladay, Beomjoon Kim, Tom Silver, Leslie Pack Kaelbling, and Tomás Lozano-Pérez. 2021 · 2021
Cited alongside, same era.
Generalized planning as heuristic search. In Proceedings of the International Conference on Automated Planning and Scheduling , Vol. 31. 569–577
Javier Segovia-Aguas, Sergio Jiménez, and Anders Jonsson. 2021 · 2021
Cited alongside, same era.
Expectation vs. experience: Evaluating the usability of code generation tools powered by large language models. In Chi conference on human factors in computing systems extended abstracts . 1–7
Priyan Vaithilingam, Tianyi Zhang, and Elena L Glassman. 2022 · 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 · 2022
Later among the works it cites.
Chain-of-thought prompting elicits reasoning in large language models
Jason Wei, Xuezhi Wang, Dale Schuurmans, Maarten Bosma, Fei Xia, Ed Chi, Quoc V Le, Denny Zhou, et al · 2022
Later among the works it cites.
Star: Bootstrapping reasoning with reasoning
Eric Zelikman, Yuhuai Wu, Jesse Mu, and Noah Goodman. 2022 · 2022
Later among the works it cites.
Socratic models: Composing zero-shot multimodal reasoning with language
Andy Zeng, Maria Attarian, Brian Ichter, Krzysztof Choromanski, Adrian Wong, Stefan Welker, Federico Tombari, Aveek Purohit, Michael Ryoo, Vikas Sindhwani, et al · 2022
Later among the works it cites.
PDDL. jl: An Extensible Interpreter and Compiler Interface for Fast and Flexible AI Planning
Tan Zhi-Xuan. 2022 · 2022
Later among the works it cites.
Task and motion planning with large language models for object rearrangement
Yan Ding, Xiaohan Zhang, Chris Paxton, and Shiqi Zhang. 2023 · 2023
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Text2motion: From natural language instructions to feasible plans
Kevin Lin, Christopher Agia, Toki Migimatsu, Marco Pavone, and Jeannette Bohg. 2023 · 2023
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Llm+ p: Empowering large language models with optimal planning proficiency
Bo Liu, Yuqian Jiang, Xiaohan Zhang, Qiang Liu, Shiqi Zhang, Joydeep Biswas, and Peter Stone. 2023 · 2023
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Self-refine: Iterative refinement with self-feedback
Aman Madaan, Niket Tandon, Prakhar Gupta, Skyler Hallinan, Luyu Gao, Sarah Wiegreffe, Uri Alon, Nouha Dziri, Shrimai Prabhumoye, Yiming Yang, et al · 2023
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Generalized Planning in PDDL Domains with Pretrained Large Language Models
Tom Silver, Soham Dan, Kavitha Srinivas, Joshua B Tenenbaum, Leslie Pack Kaelbling, and Michael Katz. 2023 · 2023
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Progprompt: Generating situated robot task plans using large language models. In 2023 IEEE International Conference on Robotics and Automation (ICRA) . IEEE, 11523–11530
Ishika Singh, Valts Blukis, Arsalan Mousavian, Ankit Goyal, Danfei Xu, Jonathan Tremblay, Dieter Fox, Jesse Thomason, and Animesh Garg. 2023 · 2023
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