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Robotic motion planning problems are typically solved by constructing a search tree of valid maneuvers from a start to a goal configuration.
First results on the effect of error in heuristic search
I. Pohl · 1970
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Heuristics: intelligent search strategies for computer problem solving
J. Pearl · 1984
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Learning policies for partially observable environments: Scaling up
M. L. Littman, A. R. Cassandra, and L. P. Kaelbling · 1995
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RRT-Connect: An efficient approach to single-query path planning
J. J. Kuffner and S. M. LaValle · 2000
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The ff planning system: Fast plan generation through heuristic search
J. Hoffmann and B. Nebel · 2001
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Planning Algorithms
S. M. LaValle · 2006
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Learning heuristic functions from relaxed plans
S. W. Yoon, A. Fern, and R. Givan · 2006
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Discriminative learning of beam-search heuristics for planning
Y. Xu, A. Fern, and S. W. Yoon · 2007
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Practical search techniques in path planning for autonomous driving
D. Dolgov, S. Thrun, M. Montemerlo, and J. Diebel · 2008
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Learning control knowledge for forward search planning
S. Yoon, A. Fern, and R. Givan · 2008
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Planning long dynamically feasible maneuvers for autonomous vehicles
M. Likhachev and D. Ferguson · 2009
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Learning linear ranking functions for beam search with application to planning
Y. Xu, A. Fern, and S. Yoon · 2009
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Iterative learning of weighted rule sets for greedy search
Y. Xu, A. Fern, and S. W. Yoon · 2010
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Learning heuristic functions for large state spaces
S. J. Arfaee, S. Zilles, and R. C. Holte · 2011
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A reduction of imitation learning and structured prediction to no-regret online learning
S. Ross, G. J. Gordon, and D. Bagnell · 2011
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Learning inadmissible heuristics during search
J. T. Thayer, A. J. Dionne, and W. Ruml · 2011
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A review of machine learning for automated planning
S. Jiménez, T. De La Rosa, S. Fernández, F. Fernández, and D. Borrajo · 2012
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E-graphs: Bootstrapping planning with experience graphs
M. Phillips, B. J. Cohen, S. Chitta, and M. Likhachev · 2012
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Lecture 6.5—RmsProp: Divide the gradient by a running average of its recent magnitude
T. Tielman and G. Hinton · 2012
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Sparse tangential network (spartan): Motion planning for micro aerial vehicles
H. Cover, S. Choudhury, S. Scherer, and S. Singh · 2013
Cited alongside, same era.
Guided policy search
S. Levine and V. Koltun · 2013
Cited alongside, same era.
Playing atari with deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. Graves, I. Antonoglou, D. Wierstra, and M. Riedmiller · 2013
Deep reinforcement learning with double q-learning
H. van Hasselt, A. Guez, and D. Silver · 2015
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Building a heuristic for greedy search
C. M. Wilt and W. Ruml · 2015
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Tensorflow: Large-scale machine learning on heterogeneous distributed systems
M. Abadi, A. Agarwal, P. Barham, E. Brevdo, Z. Chen, C. Citro, G. S. Corrado, A. Davis, J. Dean, M. Devin, S. Ghemawat, I. J. Goodfellow, A. Harp, G. Irving, M. Isard, Y. Jia, R. Józefowicz, L. Kaiser, M. Kudlur, J. Levenberg, D. Mané, R. Monga, S. Moore, D. G. Murray, C. Olah, M. Schuster, J. Shlens, B. Steiner, I. Sutskever, K. Talwar, P. A. Tucker, V. Vanhoucke, V. Vasudevan, F. B. Viégas, O. Vinyals, P. Warden, M. Wattenberg, M. Wicke, Y. Yu, and X. Zheng · 2016
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Multi-heuristic a
S. Aine, S. Swaminathan, V. Narayanan, V. Hwang, and M. Likhachev · 2016
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Openai gym, 2016
G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, and W. Zaremba · 2016
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Cited alongside, same era.
Learning heuristic functions for cost-based planning
J. ús Virseda, D. Borrajo, and V. Alcázar · 2013
Cited alongside, same era.
Pre- and post-contact policy decomposition for planar contact manipulation under uncertainty
M. Koval, N. Pollard, and S. Srinivasa · 2014
Cited alongside, same era.
Reinforcement and imitation learning via interactive no-regret learning
S. Ross and J. A. Bagnell · 2014
Cited alongside, same era.
Dynamic multi-heuristic a*
F. Islam, V. Narayanan, and M. Likhachev · 2015
Cited alongside, same era.
Shared autonomy via hindsight optimization
S. Javdani, S. S. Srinivasa, and J. A. Bagnell · 2015
Cited alongside, same era.
Continuous control with deep reinforcement learning
T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra · 2015
Cited alongside, same era.
Pareto-optimal search over configuration space beliefs for anytime motion planning
S. Choudhury, C. M. Dellin, and S. S. Srinivasa · 2016
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A unifying formalism for shortest path problems with expensive edge evaluations via lazy best-first search over paths with edge selectors
C. M. Dellin and S. S. Srinivasa · 2016
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Guided manipulation planning at the darpa robotics challenge trials
C. M. Dellin, K. Strabala, G. C. Haynes, D. Stager, and S. S. Srinivasa · 2016
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Learning to rank for synthesizing planning heuristics
C. R. Garrett, L. P. Kaelbling, and T. Lozano-Pérez · 2016
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Theano: A Python framework for fast computation of mathematical expressions
Theano Development Team · 2016
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Dueling network architectures for deep reinforcement learning
Z. Wang, T. Schaul, M. Hessel, H. Hasselt, M. Lanctot, and N. Freitas · 2016
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Learning deep control policies for autonomous aerial vehicles with mpc-guided policy search
T. Zhang, G. Kahn, S. Levine, and P. Abbeel · 2016
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Cognitive mapping and planning for visual navigation
S. Gupta, J. Davidson, S. Levine, R. Sukthankar, and J. Malik · 2017
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Plato: Policy learning using adaptive trajectory optimization
G. Kahn, T. Zhang, S. Levine, and P. Abbeel · 2017
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Verification and synthesis of admissible heuristics for kinodynamic motion planning
B. Paden, V. Varricchio, and E. Frazzoli · 2017
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