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Broadly intelligent agents should form task-specific abstractions that selectively expose the essential elements of a task, while abstracting away the complexity of the raw sensorimotor space.
Pddl-the planning domain definition language
Drew McDermott, Malik Ghallab, Adele E. Howe, Craig A. Knoblock, Ashwin Ram, Manuela M. Veloso, Daniel S. Weld, and David E. Wilkins · 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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Recent advances in hierarchical reinforcement learning
Andrew G Barto and Sridhar Mahadevan · 2003
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In defense of pddl axioms
Sylvie Thiébaux, Jörg Hoffmann, and Bernhard Nebel · 2005
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Learning symbolic models of stochastic domains
Hanna M Pasula, Luke S Zettlemoyer, and Leslie Pack Kaelbling · 2007
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Landmarks, critical paths and abstractions: what’s the difference anyway?
Malte Helmert and Carmel Domshlak · 2009
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Pybullet, a python module for physics simulation for games, robotics and machine learning, 2016
Erwin Coumans and Yunfei Bai · 2016
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Reinforcement learning with parameterized actions
Warwick Masson, Pravesh Ranchod, and George Konidaris · 2016
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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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On the necessity of abstraction
George Konidaris · 2019
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Learning portable representations for high-level planning
Steven James, Benjamin Rosman, and George Konidaris · 2020
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Integrated task and motion planning
Caelan Reed Garrett, Rohan Chitnis, Rachel Holladay, Beomjoon Kim, Tom Silver, Leslie Pack Kaelbling, and Tomás Lozano-Pérez · 2021
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Learning symbolic operators for task and motion planning
Tom Silver, Rohan Chitnis, Joshua Tenenbaum, Leslie Pack Kaelbling, and Tomás Lozano-Pérez · 2021
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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, Chuyuan Fu, Keerthana Gopalakrishnan, Karol Hausman, et al · 2022
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Learning neuro-symbolic relational transition models for bilevel planning
Rohan Chitnis, Tom Silver, Joshua B Tenenbaum, Tomas Lozano-Perez, and Leslie Pack Kaelbling · 2022
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Long-horizon manipulation of unknown objects via task and motion planning with estimated affordances
Aidan Curtis, Xiaolin Fang, Leslie Pack Kaelbling, Tomás Lozano-Pérez, and Caelan Reed Garrett · 2022
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Visual programming: Compositional visual reasoning without training
Tanmay Gupta and Aniruddha Kembhavi · 2022
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Inner monologue: Embodied reasoning through planning with language models
Eureka: Human-level reward design via coding large language models
Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang, Osbert Bastani, Dinesh Jayaraman, Yuke Zhu, Linxi Fan, and Anima Anandkumar · 2023
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Generative skill chaining: Long-horizon skill planning with diffusion models
Utkarsh Aashu Mishra, Shangjie Xue, Yongxin Chen, and Danfei Xu · 2023
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Predicate invention for bilevel planning
Tom Silver, Rohan Chitnis, Nishanth Kumar, Willie McClinton, Tomás Lozano-Pérez, Leslie Kaelbling, and Joshua B Tenenbaum · 2023
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Vipergpt: Visual inference via python execution for reasoning
Dídac Surís, Sachit Menon, and Carl Vondrick · 2023
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Set-of-mark prompting unleashes extraordinary visual grounding in gpt-4v
Jianwei Yang, Hao Zhang, Feng Li, Xueyan Zou, Chunyuan Li, and Jianfeng Gao · 2023
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Wenlong Huang, Fei Xia, Ted Xiao, Harris Chan, Jacky Liang, Pete Florence, Andy Zeng, Jonathan Tompson, Igor Mordatch, Yevgen Chebotar, et al · 2022
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Autonomous learning of object-centric abstractions for high-level planning
Steven James, Benjamin Rosman, and GD Konidaris · 2022
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Augmenting reinforcement learning with behavior primitives for diverse manipulation tasks
Soroush Nasiriany, Huihan Liu, and Yuke Zhu · 2022
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Augmenting reinforcement learning with behavior primitives for diverse manipulation tasks
Soroush Nasiriany, Huihan Liu, and Yuke Zhu · 2022
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Learning neuro-symbolic skills for bilevel planning
Tom Silver, Ashay Athalye, Joshua B Tenenbaum, Tomas Lozano-Perez, and Leslie Pack Kaelbling · 2022
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Rt-2: Vision-language-action models transfer web knowledge to robotic control
Anthony Brohan, Noah Brown, Justice Carbajal, Yevgen Chebotar, Xi Chen, Krzysztof Choromanski, Tianli Ding, Danny Driess, Avinava Dubey, Chelsea Finn, et al · 2023
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Look before you leap: Unveiling the power of gpt-4v in robotic vision-language planning
Yingdong Hu, Fanqi Lin, Tong Zhang, Li Yi, and Yang Gao · 2023
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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 · 2023
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Interpret: Interactive predicate learning from language feedback for generalizable task planning
Muzhi Han, Yifeng Zhu, Song-Chun Zhu, Ying Nian Wu, and Yuke Zhu · 2024
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Rekep: Spatio-temporal reasoning of relational keypoint constraints for robotic manipulation
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Llms can’t plan, but can help planning in llm-modulo frameworks, 2024
Subbarao Kambhampati, Karthik Valmeekam, Lin Guan, Mudit Verma, Kaya Stechly, Siddhant Bhambri, Lucas Saldyt, and Anil Murthy · 2024
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Practice makes perfect: Planning to learn skill parameter policies, 2024
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Rapid motor adaptation for robotic manipulator arms
Yichao Liang, Kevin Ellis, and João Henriques · 2024
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Hao Tang, Darren Key, and Kevin Ellis · 2024
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Learning efficient abstract planning models that choose what to predict
Nishanth Kumar, Willie McClinton, Rohan Chitnis, Tom Silver, Tomás Lozano-Pérez, and Leslie Pack Kaelbling · 2095
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