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Efficient planning in continuous state and action spaces is fundamentally hard, even when the transition model is deterministic and known.
Learning first-order symbolic representations for planning from the structure of the state space
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Learning symbolic models of stochastic domains
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Landmarks, critical paths and abstractions: what’s the difference anyway?
Helmert, M.; and Domshlak, C. 2009 · 2009
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Probabilistic plan recognition using off-the-shelf classical planners
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Ahmetoglu, A.; Seker, M. Y.; Piater, J.; Oztop, E.; and Ugur, E. 2020 · 2012
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From skills to symbols: Learning symbolic representations for abstract high-level planning
Konidaris, G.; Kaelbling, L. P.; and Lozano-Perez, T. 2018 · 2018
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A Survey on Hierarchical Planning-One Abstract Idea, Many Concrete Realizations
Bercher, P.; Alford, R.; and Höller, D. 2019 · 2019
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Learning quickly to plan quickly using modular meta-learning
Chitnis, R.; Kaelbling, L. P.; and Lozano-Pérez, T. 2019 · 2019
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Discovering a symbolic planning language from continuous experience
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Generalized lazy search for robot motion planning: Interleaving search and edge evaluation via event-based toggles
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Pyperplan
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Learning grounded relational symbols from continuous data for abstract reasoning
Jetchev, N.; Lang, T.; and Toussaint, M. 2013 · 2013
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A machine learning framework for programming by example
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Adam: A method for stochastic optimization
Kingma, D. P.; and Ba, J. 2014 · 2014
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Combined task and motion planning through an extensible planner-independent interface layer
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Fast and accurate deep network learning by exponential linear units (elus)
Clevert, D.-A.; Unterthiner, T.; and Hochreiter, S. 2015 · 2015
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Bottom-up learning of object categories, action effects and logical rules: From continuous manipulative exploration to symbolic planning
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Alkhazraji, Y.; Frorath, M.; Grützner, M.; Helmert, M.; Liebetraut, T.; Mattmüller, R.; Ortlieb, M.; Seipp, J.; Springenberg, T.; Stahl, P.; and Wülfing, J. 2020 · 2020
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Online replanning in belief space for partially observable task and motion problems
Garrett, C. R.; Paxton, C.; Lozano-Pérez, T.; Kaelbling, L. P.; and Fox, D. 2020 · 2020
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Learning constraint-based planning models from demonstrations
Loula, J.; Allen, K.; Silver, T.; and Tenenbaum, J. 2020 · 2020
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Online bayesian goal inference for boundedly rational planning agents
Zhi-Xuan, T.; Mann, J.; Silver, T.; Tenenbaum, J.; and Mansinghka, V. 2020 · 2020
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Discovering State and Action Abstractions for Generalized Task and Motion Planning
Curtis, A.; Silver, T.; Tenenbaum, J. B.; Lozano-Perez, T.; and Kaelbling, L. P. 2021 · 2021
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Integrated task and motion planning
Garrett, C. R.; Chitnis, R.; Holladay, R.; Kim, B.; Silver, T.; Kaelbling, L. P.; and Lozano-Pérez, T. 2021 · 2021
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Extended Tree Search for Robot Task and Motion Planning
Ren, T.; Chalvatzaki, G.; and Peters, J. 2021 · 2021
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Learning symbolic operators for task and motion planning
Silver, T.; Chitnis, R.; Tenenbaum, J.; Kaelbling, L. P.; and Lozano-Pérez, T. 2021 · 2021
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Learning a Symbolic Planning Domain through the Interaction with Continuous Environments
Umili, E.; Antonioni, E.; Riccio, F.; Capobianco, R.; Nardi, D.; and De Giacomo, G. 2021 · 2021
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Learning compositional models of robot skills for task and motion planning
Wang, Z.; Garrett, C. R.; Kaelbling, L. P.; and Lozano-Pérez, T. 2021 · 2021
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A Comprehensive Framework for Learning Declarative Action Models
Aineto, D.; Jiménez, S.; and Onaindia, E. 2022 · 2022
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Learning Neuro-Symbolic Relational Transition Models for Bilevel Planning
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Structured deep generative models for sampling on constraint manifolds in sequential manipulation
Ortiz-Haro, J.; Ha, J.-S.; Driess, D.; and Toussaint, M. 2022 · 2022
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Learning Neuro-Symbolic Skills for Bilevel Planning
Silver, T.; Athalye, A.; Tenenbaum, J. B.; Lozano-Perez, T.; and Kaelbling, L. P. 2022 · 2022
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