暂无分享,去创建一个
Leslie Pack Kaelbling | Tom Silver | Rohan Chitnis | Tomas Lozano-Perez | Beomjoon Kim | L. Kaelbling | Tomas Lozano-Perez | Rohan Chitnis | Tom Silver | Beomjoon Kim
[1] Lydia E. Kavraki,et al. Learning Feasibility for Task and Motion Planning in Tabletop Environments , 2019, IEEE Robotics and Automation Letters.
[2] James J. Kuffner,et al. Navigation among movable obstacles: real-time reasoning in complex environments , 2004, 4th IEEE/RAS International Conference on Humanoid Robots, 2004..
[3] Nicolas Mansard,et al. Learning Feasibility Constraints for Multicontact Locomotion of Legged Robots , 2017, Robotics: Science and Systems.
[4] Jesse Hoey,et al. SPUDD: Stochastic Planning using Decision Diagrams , 1999, UAI.
[5] Dylan Hadfield-Menell,et al. Guided search for task and motion plans using learned heuristics , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).
[6] Jung-Su Ha,et al. Deep Visual Reasoning: Learning to Predict Action Sequences for Task and Motion Planning from an Initial Scene Image , 2020, Robotics: Science and Systems.
[7] Andrew G. Barto,et al. Efficient skill learning using abstraction selection , 2009, IJCAI 2009.
[8] Craig Boutilier,et al. Correlated Action Effects in Decision Theoretic Regression , 1997, UAI.
[9] Leslie Pack Kaelbling,et al. Relational envelope-based planning , 2008 .
[10] Thomas J. Walsh,et al. Towards a Unified Theory of State Abstraction for MDPs , 2006, AI&M.
[11] Malte Helmert,et al. The Fast Downward Planning System , 2006, J. Artif. Intell. Res..
[12] Craig Boutilier,et al. Decision-Theoretic Planning: Structural Assumptions and Computational Leverage , 1999, J. Artif. Intell. Res..
[13] Steven M. LaValle,et al. RRT-connect: An efficient approach to single-query path planning , 2000, Proceedings 2000 ICRA. Millennium Conference. IEEE International Conference on Robotics and Automation. Symposia Proceedings (Cat. No.00CH37065).
[14] David Poole,et al. Context-specific approximation in probabilistic inference , 1998, UAI.
[15] Joost Broekens,et al. Think Too Fast Nor Too Slow: The Computational Trade-off Between Planning And Reinforcement Learning , 2020, ArXiv.
[16] Jörg Hoffmann,et al. FF: The Fast-Forward Planning System , 2001, AI Mag..
[17] D. Bertsekas,et al. Adaptive aggregation methods for infinite horizon dynamic programming , 1989 .
[18] Jung-Su Ha,et al. Deep Visual Heuristics: Learning Feasibility of Mixed-Integer Programs for Manipulation Planning , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).
[19] Leslie Pack Kaelbling,et al. Guiding Search in Continuous State-Action Spaces by Learning an Action Sampler From Off-Target Search Experience , 2018, AAAI.
[20] Nevin Lianwen Zhang,et al. On the Role of Context-Specific Independence in Probabilistic Inference , 1999, IJCAI.
[21] Peter Stone,et al. State Abstraction Discovery from Irrelevant State Variables , 2005, IJCAI.
[22] George Konidaris,et al. Constructing Abstraction Hierarchies Using a Skill-Symbol Loop , 2015, IJCAI.
[23] Doina Precup,et al. Value Preserving State-Action Abstractions , 2020, AISTATS.
[24] Trevor I. Dix,et al. Proximity-Based Non-uniform Abstractions for Approximate Planning , 2014, J. Artif. Intell. Res..
[25] Leslie Pack Kaelbling,et al. Learning to guide task and motion planning using score-space representation , 2017, 2017 IEEE International Conference on Robotics and Automation (ICRA).
[26] Carmel Domshlak,et al. Efficient probabilistic reasoning in BNs with mutual exclusion and context‐specific independence , 2004, Int. J. Intell. Syst..
[27] Nan Jiang,et al. Abstraction Selection in Model-based Reinforcement Learning , 2015, ICML.
[28] Craig Boutilier,et al. Context-Specific Independence in Bayesian Networks , 1996, UAI.
[29] Beomjoon Kim,et al. Learning value functions with relational state representations for guiding task-and-motion planning , 2019, CoRL.
[30] Shobha Venkataraman,et al. Efficient Solution Algorithms for Factored MDPs , 2003, J. Artif. Intell. Res..
[31] Kurt Steinkraus,et al. Solving large stochastic planning problems using multiple dynamic abstractions , 2005 .
[32] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[33] Michael L. Littman,et al. Near Optimal Behavior via Approximate State Abstraction , 2016, ICML.
[34] Pieter Abbeel,et al. Combined task and motion planning through an extensible planner-independent interface layer , 2014, 2014 IEEE International Conference on Robotics and Automation (ICRA).
[35] Craig A. Knoblock,et al. PDDL-the planning domain definition language , 1998 .
[36] Robert L. Smith,et al. Aggregation in Dynamic Programming , 1987, Oper. Res..
[37] Simon M. Lucas,et al. A Survey of Monte Carlo Tree Search Methods , 2012, IEEE Transactions on Computational Intelligence and AI in Games.
[38] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.