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We propose a meta path planning algorithm named \emph{Neural Exploration-Exploitation Trees~(NEXT)} for learning from prior experience for solving new path planning problems in high dimensional continuous state and action spaces.
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The bridge test for sampling narrow passages with probabilistic roadmap planners
Hsu, D., Jiang, T., Reif, J., and Sun, Z · 2003
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Guided expansive spaces trees: A search strategy for motion-and cost-constrained state spaces
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Sampling-based motion planning using predictive models
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Hybrid prm sampling with a cost-sensitive adaptive strategy
Hsu, D., Sánchez-Ante, G., and Sun, Z · 2005
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Bandit based monte-carlo planning
Kocsis, L. and Szepesvári, C · 2006
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Randomized statistical path planning
Rosen Diankov and James Kuffner · 2007
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Predicting partial paths from planning problem parameters
Finney, S., Kaelbling, L. P., and Lozano-Perez, T · 2007
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Probabilistic navigation in dynamic environment using rapidly-exploring random trees and gaussian processes
Fulgenzi, Chiara and Tay, Christopher and Spalanzani, Anne and Laugier, Christian · 2008
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The epoch-greedy algorithm for multi-armed bandits with side information
Langford, J. and Zhang, T · 2008
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Balancing exploration and exploitation in motion planning
Rickert, Markus and Brock, Oliver and Knoll, Alois · 2008
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Adaptive workspace biasing for sampling-based planners
Zucker, M., Kuffner, J., and Bagnell, J. A · 2008
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Reachability-guided sampling for planning under differential constraints
Shkolnik, A., Walter, M., and Tedrake, R · 2009
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Gaussian process optimization in the bandit setting: No regret and experimental design
Srinivas, N., Krause, A., Kakade, S. M., and Seeger, M · 2009
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Algorithms for infinitely many-armed bandits
Wang, Y., Audibert, J.-Y., and Munos, R · 2009
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Automated Construction of Robotic Manipulation Programs
Rosen Diankov · 2010
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Contextual bandits with linear payoff functions
Chu, W., Li, L., Reyzin, L., and Schapire, R · 2011
Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age
Cadena Cesar, Luca Carlone, Henry Carrillo, Yasir Latif, Davide Scaramuzza, José Neira, Ian Reid, and John J. Leonard · 2016
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Value iteration networks
Tamar, A., Wu, Y., Thomas, G., Levine, S., and Abbeel, P · 2016
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Monte carlo tree search in continuous action spaces with execution uncertainty
Yee, T., Lisy, V., and Bowling, M · 2016
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Learning heuristic search via imitation
Mohak Bhardwaj, Sanjiban Choudhury, and Sebastian Scherer · 2017
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Qmdp-net: Deep learning for planning under partial observability
Karkus, P., Hsu, D., and Lee, W. S · 2017
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Continuous upper confidence trees
Couëtoux, A., Hoock, J.-B., Sokolovska, N., Teytaud, O., and Bonnard, N · 2011
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Sampling-based algorithms for optimal motion planning
Karaman, S. and Frazzoli, E · 2011
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Contextual gaussian process bandit optimization
Krause, A. and Ong, C. S · 2011
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E-graphs: Bootstrapping planning with experience graphs
Mike Phillips, Benjamin J Cohen, Sachin Chitta, and Maxim Likhachev · 2012
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The Open Motion Planning Library
Ioan A. Şucan, Mark Moll, and Lydia E. Kavraki · 2012
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Closed-loop global motion planning for reactive execution of learned tasks
Bowen, Chris, and Ron Alterovitz · 2014
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Paxton, Chris and Raman, Vasumathi and Hager, Gregory D and Kobilarov, Marin · 2017
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Mastering chess and shogi by self-play with a general reinforcement learning algorithm
Silver, David and Hubert, Thomas and Schrittwieser, Julian and Antonoglou, Ioannis and Lai, Matthew and Guez, Arthur and Lanctot, Marc and Sifre, Laurent and Kumaran, Dharshan and Graepel, Thore and others · 2017
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Guided motion planning
Ye, Gu and Alterovitz, Ron · 2017
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Data-driven planning via imitation learning
Sanjiban Choudhury, Mohak Bhardwaj, Sankalp Arora, Ashish Kapoor, Gireeja Ranade, Sebastian Scherer, and Debadeepta Dey · 2018
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Learning to search with mctsnets
Guez, Arthur and Weber, Théophane and Antonoglou, Ioannis and Simonyan, Karen and Vinyals, Oriol and Wierstra, Daan and Munos, Rémi and Silver, David · 2018
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Efficient Sampling With Q-Learning to Guide Rapidly Exploring Random Trees
Huh, Jinwook and Lee, Daniel · 2018
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Learning sampling distributions for robot motion planning
Ichter, B., Harrison, J., and Pavone, M · 2018
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Guiding search in continuous state-action spaces by learning an action sampler from off-target search experience
Kim, B., Kaelbling, L. P., and Lozano-Pérez, T · 2018
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Deep sequential models for sampling-based planning
Kuo, Yen-Ling and Barbu, Andrei and Katz, Boris · 2018
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Lee, L., Parisotto, E., Chaplot, D. S., Xing, E., and Salakhutdinov, R · 2018
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Learning to search via retrospective imitation
Jialin Song, Ravi Lanka, Albert Zhao, Yisong Yue, and Masahiro Ono · 2018
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Learning implicit sampling distributions for motion planning
Zhang, C., Huh, J., and Lee, D. D · 2018
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Motion planning networks
Qureshi, Ahmed H and Simeonov, Anthony and Bency, Mayur J and Yip, Michael C · 2019
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