Representation, learning, and planning algorithms for geometric task and motion planning
暂无分享,去创建一个
[1] Leslie Pack Kaelbling,et al. Integrated Task and Motion Planning , 2020, Annu. Rev. Control. Robotics Auton. Syst..
[2] Leslie Pack Kaelbling,et al. CAMPs: Learning Context-Specific Abstractions for Efficient Planning in Factored MDPs , 2020, CoRL.
[3] 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.
[4] 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).
[5] Caelan Reed Garrett,et al. Online Replanning in Belief Space for Partially Observable Task and Motion Problems , 2019, 2020 IEEE International Conference on Robotics and Automation (ICRA).
[6] Leslie Pack Kaelbling,et al. Adversarial Actor-Critic Method for Task and Motion Planning Problems Using Planning Experience , 2019, AAAI.
[7] Zhiyuan Liu,et al. Graph Neural Networks: A Review of Methods and Applications , 2018, AI Open.
[8] Leslie Pack Kaelbling,et al. Learning Quickly to Plan Quickly Using Modular Meta-Learning , 2018, 2019 International Conference on Robotics and Automation (ICRA).
[9] Jessica B. Hamrick,et al. Relational inductive biases, deep learning, and graph networks , 2018, ArXiv.
[10] Leslie Pack Kaelbling,et al. Guiding Search in Continuous State-Action Spaces by Learning an Action Sampler From Off-Target Search Experience , 2018, AAAI.
[11] Demis Hassabis,et al. Mastering the game of Go without human knowledge , 2017, Nature.
[12] Léon Bottou,et al. Wasserstein Generative Adversarial Networks , 2017, ICML.
[13] Caelan Reed Garrett,et al. Sample-Based Methods for Factored Task and Motion Planning , 2017, Robotics: Science and Systems.
[14] Yiming Yang,et al. MMD GAN: Towards Deeper Understanding of Moment Matching Network , 2017, NIPS.
[15] 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).
[16] Samuel S. Schoenholz,et al. Neural Message Passing for Quantum Chemistry , 2017, ICML.
[17] Aaron C. Courville,et al. Improved Training of Wasserstein GANs , 2017, NIPS.
[18] Max Welling,et al. Semi-Supervised Classification with Graph Convolutional Networks , 2016, ICLR.
[19] Leslie Pack Kaelbling,et al. FFRob: Leveraging symbolic planning for efficient task and motion planning , 2016, Int. J. Robotics Res..
[20] Leslie Pack Kaelbling,et al. Learning to Rank for Synthesizing Planning Heuristics , 2016, IJCAI.
[21] Siddhartha S. Srinivasa,et al. Rearrangement planning using object-centric and robot-centric action spaces , 2016, 2016 IEEE International Conference on Robotics and Automation (ICRA).
[22] 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).
[23] Demis Hassabis,et al. Mastering the game of Go with deep neural networks and tree search , 2016, Nature.
[24] Yuval Tassa,et al. Continuous control with deep reinforcement learning , 2015, ICLR.
[25] Marc Toussaint,et al. Logic-Geometric Programming: An Optimization-Based Approach to Combined Task and Motion Planning , 2015, IJCAI.
[26] Kostas E. Bekris,et al. Dealing with Difficult Instances of Object Rearrangement , 2015, Robotics: Science and Systems.
[27] Jimmy Ba,et al. Adam: A Method for Stochastic Optimization , 2014, ICLR.
[28] Pieter Abbeel,et al. Motion planning with sequential convex optimization and convex collision checking , 2014, Int. J. Robotics Res..
[29] Aaron C. Courville,et al. Generative adversarial networks , 2014, Commun. ACM.
[30] Kris K. Hauser,et al. The minimum constraint removal problem with three robotics applications , 2014, Int. J. Robotics Res..
[31] Max Welling,et al. Auto-Encoding Variational Bayes , 2013, ICLR.
[32] Leslie Pack Kaelbling,et al. Integrated task and motion planning in belief space , 2013, Int. J. Robotics Res..
[33] Siddhartha S. Srinivasa,et al. CHOMP: Covariant Hamiltonian optimization for motion planning , 2013, Int. J. Robotics Res..
[34] Bernhard Nebel,et al. The FF Planning System: Fast Plan Generation Through Heuristic Search , 2011, J. Artif. Intell. Res..
[35] Leslie Pack Kaelbling,et al. Hierarchical task and motion planning in the now , 2011, 2011 IEEE International Conference on Robotics and Automation.
[36] Erez Karpas,et al. To Max or Not to Max: Online Learning for Speeding Up Optimal Planning , 2010, AAAI.
[37] Andreas Krause,et al. Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting , 2009, IEEE Transactions on Information Theory.
[38] R. Alami,et al. A Hybrid Approach to Intricate Motion, Manipulation and Task Planning , 2009, Int. J. Robotics Res..
[39] Tamim Asfour,et al. Manipulation Planning Among Movable Obstacles , 2007, Proceedings 2007 IEEE International Conference on Robotics and Automation.
[40] Michael Fink,et al. Online Learning of Search Heuristics , 2007, AISTATS.
[41] Robert Givan,et al. Learning Heuristic Functions from Relaxed Plans , 2006, ICAPS.
[42] Malte Helmert,et al. The Fast Downward Planning System , 2006, J. Artif. Intell. Res..
[43] F. Scarselli,et al. A new model for learning in graph domains , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..
[44] Thomas Hofmann,et al. Large Margin Methods for Structured and Interdependent Output Variables , 2005, J. Mach. Learn. Res..
[45] 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..
[46] 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).
[47] Alessandro Sperduti,et al. Supervised neural networks for the classification of structures , 1997, IEEE Trans. Neural Networks.
[48] M. Veloso,et al. Robotics and autonomous systems , 1988, Robotics Auton. Syst..
[49] Beomjoon Kim,et al. Learning value functions with relational state representations for guiding task-and-motion planning , 2019, CoRL.
[50] Philip S. Yu,et al. A Comprehensive Survey on Graph Neural Networks , 2019, IEEE Transactions on Neural Networks and Learning Systems.
[51] Derong Liu,et al. Neural Information Processing , 2017, Lecture Notes in Computer Science.
[52] FernAlan,et al. Learning partial policies to speedup MDP tree search via reduction to I.I.D. learning , 2017 .
[53] Takeo Kanade,et al. Automated Construction of Robotic Manipulation Programs , 2010 .
[54] Ah Chung Tsoi,et al. The Graph Neural Network Model , 2009, IEEE Transactions on Neural Networks.
[55] Rachid Alami,et al. aSyMov: A Planner That Deals with Intricate Symbolic and Geometric Problems , 2003, ISRR.
[56] Richard S. Sutton,et al. Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.