Automatic encoding and repair of reactive high-level tasks with learned abstract representations
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[1] M. Rosenblatt. Remarks on Some Nonparametric Estimates of a Density Function , 1956 .
[2] E. Parzen. On Estimation of a Probability Density Function and Mode , 1962 .
[3] Richard Fikes,et al. STRIPS: A New Approach to the Application of Theorem Proving to Problem Solving , 1971, IJCAI.
[4] Fred Kröger,et al. Temporal Logic of Programs , 1987, EATCS Monographs on Theoretical Computer Science.
[5] Craig A. Knoblock,et al. PDDL-the planning domain definition language , 1998 .
[6] Erkki Oja,et al. Independent component analysis: algorithms and applications , 2000, Neural Networks.
[7] Philip H. Ramsey. Nonparametric Statistical Methods , 1974, Technometrics.
[8] Maria Fox,et al. PDDL2.1: An Extension to PDDL for Expressing Temporal Planning Domains , 2003, J. Artif. Intell. Res..
[9] Corinna Cortes,et al. Support-Vector Networks , 1995, Machine Learning.
[10] Stefan Edelkamp,et al. Automated Planning: Theory and Practice , 2007, Künstliche Intell..
[11] Robert Givan,et al. FF-Replan: A Baseline for Probabilistic Planning , 2007, ICAPS.
[12] Krishnendu Chatterjee,et al. Environment Assumptions for Synthesis , 2008, CONCUR.
[13] Roderick Bloem,et al. Debugging formal specifications using simple counterstrategies , 2009, 2009 Formal Methods in Computer-Aided Design.
[14] Hadas Kress-Gazit,et al. Temporal-Logic-Based Reactive Mission and Motion Planning , 2009, IEEE Transactions on Robotics.
[15] Autonomously Learning an Action Hierarchy Using a Learned Qualitative State Representation , 2009, IJCAI.
[16] Manuel Mazo,et al. PESSOA: A Tool for Embedded Controller Synthesis , 2010, CAV.
[17] Hadas Kress-Gazit,et al. LTLMoP: Experimenting with language, Temporal Logic and robot control , 2010, 2010 IEEE/RSJ International Conference on Intelligent Robots and Systems.
[18] Ufuk Topcu,et al. Receding horizon control for temporal logic specifications , 2010, HSCC '10.
[19] Sanjit A. Seshia,et al. Mining assumptions for synthesis , 2011, Ninth ACM/IEEE International Conference on Formal Methods and Models for Codesign (MEMPCODE2011).
[20] Georgios E. Fainekos,et al. Revising temporal logic specifications for motion planning , 2011, 2011 IEEE International Conference on Robotics and Automation.
[21] Edwin Olson,et al. AprilTag: A robust and flexible visual fiducial system , 2011, 2011 IEEE International Conference on Robotics and Automation.
[22] Amir Pnueli,et al. Synthesis of Reactive(1) designs , 2006, J. Comput. Syst. Sci..
[23] Calin Belta,et al. Temporal Logic Motion Planning and Control With Probabilistic Satisfaction Guarantees , 2012, IEEE Transactions on Robotics.
[24] Benjamin Kuipers,et al. Autonomous Learning of High-Level States and Actions in Continuous Environments , 2012, IEEE Transactions on Autonomous Mental Development.
[25] Ufuk Topcu,et al. Counter-strategy guided refinement of GR(1) temporal logic specifications , 2013, 2013 Formal Methods in Computer-Aided Design.
[26] Marc Toussaint,et al. Learning Grounded Relational Symbols from Continuous Data for Abstract Reasoning , 2013 .
[27] Hadas Kress-Gazit,et al. Explaining Impossible High-Level Robot Behaviors , 2013, IEEE Transactions on Robotics.
[28] Justus H. Piater,et al. Bottom-up learning of object categories, action effects and logical rules: From continuous manipulative exploration to symbolic planning , 2015, 2015 IEEE International Conference on Robotics and Automation (ICRA).
[29] Sriram Sankaranarayanan,et al. On the minimal revision problem of specification automata , 2014, Int. J. Robotics Res..
[30] Justus H. Piater,et al. Refining discovered symbols with multi-step interaction experience , 2015, 2015 IEEE-RAS 15th International Conference on Humanoid Robots (Humanoids).
[31] Paolo Traverso,et al. Automated Planning and Acting , 2016 .
[32] Edwin Olson,et al. AprilTag 2: Efficient and robust fiducial detection , 2016, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[33] Vasumathi Raman,et al. Slugs: Extensible GR(1) Synthesis , 2016, CAV.
[34] Hadas Kress-Gazit,et al. Iterative Temporal Planning in Uncertain Environments With Partial Satisfaction Guarantees , 2016, IEEE Transactions on Robotics.
[35] Hadas Kress-Gazit,et al. Nonlinear Controller Synthesis and Automatic Workspace Partitioning for Reactive High-Level Behaviors , 2016, HSCC.
[36] G. Konidaris,et al. Learning to Plan with Portable Symbols , 2018 .
[37] Hadas Kress-Gazit,et al. Synthesis for Robots: Guarantees and Feedback for Robot Behavior , 2018, Annu. Rev. Control. Robotics Auton. Syst..
[38] Alex S. Fukunaga,et al. Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary , 2017, AAAI.
[39] George Konidaris,et al. Learning Symbolic Representations for Planning with Parameterized Skills , 2018, 2018 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).
[40] Leslie Pack Kaelbling,et al. From Skills to Symbols: Learning Symbolic Representations for Abstract High-Level Planning , 2018, J. Artif. Intell. Res..
[41] Lydia E. Kavraki,et al. Efficient Symbolic Reactive Synthesis for Finite-Horizon Tasks , 2019, 2019 International Conference on Robotics and Automation (ICRA).
[42] Lydia E. Kavraki,et al. Automated Abstraction of Manipulation Domains for Cost-Based Reactive Synthesis , 2019, IEEE Robotics and Automation Letters.
[43] Masataro Asai,et al. Unsupervised Grounding of Plannable First-Order Logic Representation from Images , 2019, ICAPS.
[44] George Konidaris,et al. Learning Portable Representations for High-Level Planning , 2019, ICML.
[45] J. Piater,et al. DeepSym: Deep Symbol Generation and Rule Learning from Unsupervised Continuous Robot Interaction for Planning , 2020, ArXiv.
[46] Hadas Kress-Gazit,et al. Finding Missing Skills for High-Level Behaviors , 2020, 2020 IEEE International Conference on Robotics and Automation (ICRA).