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Effective planning in the real world requires not only world knowledge, but the ability to leverage that knowledge to build the right representation of the task at hand.
STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving
Richard E Fikes and Nils J Nilsson · 1971
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On the Semantics of STRIPS
Vladimir Lifschitz · 1986
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HTN Planning: Complexity and Expressivity
Kutluhan Erol, James Hendler, and Dana S Nau · 1994
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Rapidly-Exploring Random Trees: A New Tool for Path Planning
Steven LaValle · 1998
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Between MDPs and Semi-MDPs: A Framework for Temporal Abstraction in Reinforcement Learning
Richard S Sutton, Doina Precup, and Satinder Singh · 1999
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Hierarchical Reinforcement Learning with the MAXQ Value Function Decomposition
Thomas G Dietterich · 2000
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The Fast Downward Planning System
Malte Helmert · 2006
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Learning Hierarchical Task Networks by Observation
Negin Nejati, Pat Langley, and Tolga Konik · 2006
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Learning Domain Control Knowledge for TLPlan and Beyond
Tomás de la Rosa and Sheila McIlraith · 2011
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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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Hierarchical Planning: Relating Task and Goal Decomposition with Task Sharing
Ron Alford, Vikas Shivashankar, Mark Roberts, Jeremy Frank, and David W Aha · 2016
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Modular Multitask Reinforcement Learning with Policy Sketches
Jacob Andreas, Dan Klein, and Sergey Levine · 2017
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Hindsight Experience Replay
Marcin Andrychowicz, Filip Wolski, Alex Ray, Jonas Schneider, Rachel Fong, Peter Welinder, Bob McGrew, Josh Tobin, OpenAI Pieter Abbeel, and Wojciech Zaremba · 2017
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Mapping Instructions and Visual Observations to Actions with Reinforcement Learning
Dipendra Misra, John Langford, and Yoav Artzi · 2017
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From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning
George Konidaris, Leslie Pack Kaelbling, and Tomas Lozano-Perez · 2018
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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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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 Rocktäschel · 2019
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Learning Neural-Symbolic Descriptive Planning Models via Cube-Space Priors: The Voyage Home (To Strips)
Masataro Asai and Christian Muise · 2020
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Learning First-Order Symbolic Representations for Planning from the Structure of the State Space
Blai Bonet and Hector Geffner · 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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Robots That Use Language
Stefanie Tellex, Nakul Gopalan, Hadas Kress-Gazit, and Cynthia Matuszek · 2020
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Learning Language-Conditioned Robot Behavior from Offline Data and Crowd-Sourced Annotation
Suraj Nair, Eric Mitchell, Kevin Chen, Silvio Savarese, and Chelsea Finn · 2022
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Skill Induction and Planning with Latent Language
Pratyusha Sharma, Antonio Torralba, and Jacob Andreas · 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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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
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Learning and Leveraging Verifiers to Improve Planning Capabilities of Pre-trained Language Models
Daman Arora and Subbarao Kambhampati · 2023
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Ask Your Humans: Using Human Instructions to Improve Generalization in Reinforcement Learning
Valerie Chen, Abhinav Gupta, and Kenneth Marino · 2021
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GLiB: Efficient Exploration for Relational Model-Based Reinforcement Learning via Goal-Literal Babbling
Rohan Chitnis, Tom Silver, Joshua B Tenenbaum, Leslie Pack Kaelbling, and Tomás Lozano-Pérez · 2021
Cited alongside, same era.
Modular Networks for Compositional Instruction Following
Rodolfo Corona, Daniel Fried, Coline Devin, Dan Klein, and Trevor Darrell · 2021
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Integrated Task and Motion Planning
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Learning Symbolic Operators for Task and Motion Planning
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Leveraging Language to Learn Program Abstractions and Search Heuristics
Catherine Wong, Kevin M Ellis, Joshua Tenenbaum, and Jacob Andreas · 2021
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Do as I Can, Not as I Say: Grounding Language in Robotic Affordances
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Top-Down Synthesis for Library Learning
Matthew Bowers, Theo X Olausson, Lionel Wong, Gabriel Grand, Joshua B Tenenbaum, Kevin Ellis, and Armando Solar-Lezama · 2023
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Babble: Learning Better Abstractions with E-graphs and Anti-unification
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LLM+ P: Empowering Large Language Models with Optimal Planning Proficiency
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LLM-Planner: Few-Shot Grounded Planning for Embodied Agents with Large Language Models
Chan Hee Song, Jiaman Wu, Clayton Washington, Brian M Sadler, Wei-Lun Chao, and Yu Su · 2023
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Translating Natural Language to Planning Goals with Large-Language Models
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Ghost in the minecraft: Generally capable agents for open-world environments via large language models with text-based knowledge and memory, 2023
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