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In this paper, we propose a learning algorithm that speeds up the search in task and motion planning problems.
A case-based approach to robot motion planning
Pandya, S. and Hutchinson, S. (1992) · 1992
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The art of data augmentation
Van Dyk, D. A. and Meng, X.-L. (2001) · 2001
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asymov: A planner that deals with intricate symbolic and geometric problems
Gravot, F., Cambon, S., and Alami, R. (2005) · 2005
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Predicting partial paths from planning problem parameters
Finney, S., Kaelbling, L. P., and Lozano-Pérez, T. (2007) · 2007
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Using case-based reasoning for mobile robot path planning
Hodál, J. and Dvořák, J. (2008) · 2008
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Planning motion in environments with similar obstacles
Lien, J. and Lu, Y. (2009) · 2009
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Automated Construction of Robotic Manipulation Programs
Diankov, R. (2010) · 2010
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Gaussian process optimization in the bandit setting: No regret and experimental design
Srinivas, N., Krause, A., Kakade, S., and Seeger, M. (2010) · 2010
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Learning from experience in manipulation planning: Setting the right goals
Dragan, A., Gordon, G. J., and Srinivasa, S. S. (2011) · 2011
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Optimistic optimization of a deterministic function without the knowledge of its smoothness
Munos, R. (2011) · 2011
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A robot path planning framework that learns from experience
Berenson, D., Abbeel, P., and Goldberg, K. (2012) · 2012
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E-graphs: Bootstrapping planning with experience graphs
Phillips, M., Cohen, B., Chita, S., and Likhachev, M. (2012) · 2012
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Practical Bayesian optimization of machine learning algorithms
Snoek, J., Larochelle, H., and Adams, R. P. (2012) · 2012
Cited alongside, same era.
Fast motion planning from experience: trajectory prediction for speeding up movement generation
Jetchev, N. and Toussaint, M. (2013) · 2013
Cited alongside, same era.
Integrated task and motion planning in belief space
Kaelbling, L. P. and Lozano-Pérez, T. (2013) · 2013
Motion planning with sequential convex optimization and convex collision checking
Schulman, J., Duan, Y., Ho, J., Lee, A., Awwal, I., Bradlow, H., Pan, J., Patil, S., Goldberg, K., and Abbeel, P. (2014) · 2014
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Combined task and motion planning through an extensible planner-independent interface layer
Srivastava, S., Fang, E., Riano, L., Chitnis, R., Russell, S., and Abbeel, P. (2014) · 2014
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Logic-geometric programming: An optimization-based approach to combined task and motion planning
Toussaint, M. (2015) · 2015
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Meta-level priors for learning manipulation skills with sparse features
Kroemer, O. and Sukhatme, G. S. (2016) · 2016
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Incremental task and motion planning: A constraint-based approach
Dantam, N. T., Kingston, Z., Chaudhuri, S., and Kavraki., L. (2017) · 2017
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Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
Cited alongside, same era.
A constraint-based method for solving sequential manipulation planning problems
Lozano-Pérez, T. and Kaelbling, L. (2014) · 2014
Cited alongside, same era.
From bandits to Monte-carlo Tree Search: the optimistic principle applied to optimization and planning
Munos, R. (2014) · 2014
Cited alongside, same era.
Efficient optimization of control libraries
Dey, D., Liu, T. Y., Sofman, B., and Bagnell, J. A. (2012a)
Cited in the paper.
Contextual sequence prediction with application to control library optimization
Dey, D., Liu, T. Y., Sofman, B., Hebert, M., and Bagnell, J. A. (2012b)
Cited in the paper.
Learning to guide task and motion planning using score-space representation
Kim, B., Kaelbling, L. P., and Lozano-Pérez, T. (2017) · 2017
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Feature selection for learning versatile manipulation skills based on observed and desired trajectories
Kroemer, O. and Sukhatme, G. S. (2017) · 2017
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Optimization as estimation with Gaussian processes in bandit settings
Wang, Z., Zhou, B., and Jegelka, S. (2017) · 2017
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Visual semantic planning using deep successor representations
Zhu, Y., Gordon, D., Kolve, E., Fox, D., Fei-Fei, L., Gupta, A., Mottaghi, R., and Farhadi, A. (2017) · 2017
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