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Learning-based methods are promising to plan robot motion without performing extensive search, which is needed by many non-learning approaches.
A Markovian Decision Process
Richard Bellman · 1957
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Dynamic programming
Richard Bellman · 1957
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A formal basis for the heuristic determination of minimum cost paths
Peter E Hart, Nils J Nilsson, and Bertram Raphael · 1968
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Probabilistic roadmaps for path planning in high-dimensional configuration spaces
Lydia Kavraki, Petr Svestka, Jean-Clause Latombe, and Mark H Overmars · 1996
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Rapidly-exploring random trees: A new tool for path planning
Steven M LaValle · 1998
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Local Multiresolution Path Planning
Sven Behnke · 2004
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ImageNet classification with deep convolutional neural networks
Alex Krizhevsky, Ilya Sutskever, and Geoffrey E Hinton · 2012
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Lecture 6.5-rmsprop: Divide the gradient by a running average of its recent magnitude , 2012
Tijmen Tieleman and Geoffrey Hinton · 2012
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End to end learning for self-driving cars
Mariusz Bojarski, Davide Del Testa, Daniel Dworakowski, Bernhard Firner, Beat Flepp, Prasoon Goyal, Lawrence D Jackel, Mathew Monfort, Urs Muller, Jiakai Zhang, et al · 2016
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Continuous mapping and localization for autonomous navigation in rough terrain using a 3D laser scanner
David Droeschel, Max Schwarz, and Sven Behnke · 2016
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Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation
Tejas D Kulkarni, Karthik Narasimhan, Ardavan Saeedi, and Josh Tenenbaum · 2016
Cited alongside, same era.
End-to-end training of deep visuomotor policies
Sergey Levine, Chelsea Finn, Trevor Darrell, and Pieter Abbeel · 2016
Cited alongside, same era.
SGDR: Stochastic gradient descent with warm restarts
Ilya Loshchilov and Frank Hutter · 2016
Cited alongside, same era.
Value iteration networks
Aviv Tamar, Yi Wu, Garrett Thomas, Sergey Levine, and Pieter Abbeel · 2016
Cited alongside, same era.
Planning Hybrid Driving-Stepping Locomotion on Multiple Levels of Abstraction
Tobias Klamt and Sven Behnke · 2018
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Supervised autonomous locomotion and manipulation for disaster response with a centaur-like robot
Tobias Klamt, Diego Rodriguez, Max Schwarz, Christian Lenz, Dmytro Pavlichenko, David Droeschel, and Sven Behnke · 2018
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Gated path planning networks
Lisa Lee, Emilio Parisotto, Devendra S Chaplot, Eric Xing, and Rusian Salakhutdinov · 2018
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Generalized Value Iteration Networks: Life Beyond Lattices
Sufeng Niu, Siheng Chen, Hanyu Guo, Colin Targonski, Melissa C Smith, and Jelena Kovacevic · 2018
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RGB-D object detection and semantic segmentation for autonomous manipulation in clutter
Max Schwarz, Anton Milan, Arul Selvam Periyasamy, and Sven Behnke · 2018
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S. Gupta, J. Davidson, S. Levine, R. Sukthankar, and J. Malik · 2017
Cited alongside, same era.
QMDP-Net: Deep learning for planning under partial observability
Peter Karkus, David Hsu, and Wee Sun Lee · 2017
Cited alongside, same era.
Anytime Hybrid Driving-Stepping Locomotion Planning
Tobias Klamt and Sven Behnke · 2017
Cited alongside, same era.
Learning generalized reactive policies using deep neural networks
Edward Groshev, Aviv Tamar, Maxwell Goldstein, Siddharth Srivastava, and Pieter Abbeel · 2018
Cited alongside, same era.
Aravind Srinivas, Allan Jabri, Pieter Abbeel, Sergey Levine, and Chelsea Finn · 2018
Later among the works it cites.
Towards Learning Abstract Representations for Locomotion Planning in High-dimensional State Spaces
Tobias Klamt and Sven Behnke · 2019
Closest in time.
Bounded Suboptimal Search with Learned Heuristics for Multi-Agent Systems
Markus Spies, Marco Todescato, Hannes Becker, Patrick Kesper, Nicolai Waniek, and Meng Guo · 2019
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