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Long-range indoor navigation requires guiding robots with noisy sensors and controls through cluttered environments along paths that span a variety of buildings.
Real-time obstacle avoidance for manipulators and mobile robots
O. Khatib · 1986
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L. P. Kaelbling, M. L. Littman, and A. W. Moore · 1996
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L. E. Kavraki, P. Švestka, J. C. Latombe, and M. H. Overmars · 1996
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Probabilistic roadmap methods are embarrassingly parallel
N. M. Amato and L. K. Dale · 1999
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J. Kuffner and S. LaValle · 2000
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Rapidly-exploring random trees: Progress and prospects
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D. Hsu, R. Kindel, J.-C. Latombe, and S. M. Rock · 2002
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R. Geraerts and M. H. Overmars · 2004
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Motion planning for a sixlegged lunar robot
K. Hauser, T. Bretl, J. Claude Latombe, and B. Wilcox · 2006
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S. M. LaValle · 2006
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The stochastic motion roadmap: A sampling framework for planning with markov motion uncertainty
R. Alterovitz, T. Simeon, and K. Goldberg · 2007
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On the probabilistic foundations of probabilistic roadmap planning
D. Hsu, J.-C. Latombe, and H. Kurniawati · 2007
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A framework for planning motion in environments with moving obstacles
S. Rodríguez, J.-M. Lien, and N. M. Amato · 2007
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J.-J. Park, J.-H. Kim, and J.-B. Song · 2008
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L. Tapia, S. Thomas, and N. M. Amato · 2010
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Sampling-based algorithms for optimal motion planning
S. Karaman and E. Frazzoli · 2011
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Estimating bernoulli trial probability from a small sample
N. D. Megill and M. Pavicic · 2011
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A biomimetic approach to robot table tennis
K. Mülling, J. Kober, and J. Peters · 2011
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Motion planning algorithms for molecular simulations: A survey
I. Al-Bluwi, T. Siméon, and J. Cortés · 2012
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Parallel sampling-based motion planning with superlinear speedup
c. Ichnowski and c. Alterovitz · 2012
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https://www.eia.gov/consumption/commercial/reports/2012/buildstock/, 2012
A Look at the U.S. Commercial Building Stock: Results from EIA’s 2012 Commercial Buildings Energy Consumption Survey (CBECS) · 2012
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Implementation of an embodied general reinforcement learner on a serial link manipulator
N. Malone, B. Rohrer, L. Tapia, R. Lumia, and J. Wood · 2012
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Adaptive neighbor connection for prms: A natural fit for heterogeneous environments and parallelism
C. Ekenna, S. A. Jacobs, S. Thomas, and N. M. Amato · 2013
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Reinforcement learning in robotics: A survey
Collective robot reinforcement learning with distributed asynchronous guided policy search
A. Yahya, A. Li, M. Kalakrishnan, Y. Chebotar, and S. Levine · 2016
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Target-driven visual navigation in indoor scenes using deep reinforcement learning
Y. Zhu, R. Mottaghi, E. Kolve, J. J. Lim, A. Gupta, L. Fei-Fei, and A. Farhadi · 2016
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Deepnav: Learning to navigate large cities
S. Brahmbhatt and J. Hays · 2017
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Automated aerial suspended cargo delivery through reinforcement learning
A. Faust, I. Palunko, P. Cruz, R. Fierro, and L. Tapia · 2017
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Google vizier: A service for black-box optimization
D. Golovin, B. Solnik, S. Moitra, G. Kochanski, J. Karro, and D. Sculley · 2017
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J. Kober, J. A. Bagnell, and J. Peters · 2013
Cited alongside, same era.
Construction and use of roadmaps that incorporate workspace modeling errors
N. Malone, K. Manavi, J. Wood, and L. Tapia · 2013
Cited alongside, same era.
FIRM: Sampling-based feedback motion planning under motion uncertainty and imperfect measurements
A. Agha-mohammadi, S. Chakravorty, and N. Amato · 2014
Cited alongside, same era.
Efficient motion-based task learning for a serial link manipulator
N. Malone, A. Faust, B. Rohrer, R. Lumia, J. Wood, and L. Tapia · 2014
Cited alongside, same era.
Deepdriving: Learning affordance for direct perception in autonomous driving
C. Chen, A. Seff, A. Kornhauser, and J. Xiao · 2015
Cited alongside, same era.
Path-guided artificial potential fields with stochastic reachable sets for motion planning in highly dynamic environments
H.-T. Chiang, N. Malone, K. Lesser, M. Oishi, and L. Tapia · 2015
Cited alongside, same era.
Preference-balancing motion planning under stochastic disturbances
A. Faust, N. Malone, and L. Tapia · 2015
Cited alongside, same era.
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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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Autonomous robot navigation system with learning based on deep q-network and topological maps
Y. Kato, K. Kamiyama, and K. Morioka · 2017
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From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots
M. Pfeiffer, M. Schaeuble, J. I. Nieto, R. Siegwart, and C. Cadena · 2017
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Semantic scene completion from a single depth image
S. Song, F. Yu, A. Zeng, A. X. Chang, M. Savva, and T. Funkhouser · 2017
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Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation
L. Tai, G. Paolo, and M. Liu · 2017
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Deep reinforcement learning with successor features for navigation across similar environments
J. Zhang, J. T. Springenberg, J. Boedecker, and W. Burgard · 2017
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Target-driven visual navigation in indoor scenes using deep reinforcement learning
Y. Zhu, R. Mottaghi, E. Kolve, J. J. Lim, A. Gupta, L. Fei-Fei, and A. Farhadi · 2017
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http://f1tenth.org/about, 2018
F1/10 autonomous racing competition · 2018
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PEARL: PrEference appraisal reinforcement learning for motion planning
A. Faust, H.-T. Chiang, and L. Tapia · 2018
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PRM-RL: long-range robotic navigation tasks by combining reinforcement learning and sampling-based planning
A. Faust, O. Ramirez, M. Fiser, K. Oslund, A. Francis, J. Davidson, and L. Tapia · 2018
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https://www.alexirpan.com/2018/02/14/rl-hard.html, 2018
Deep Reinforcement Learning Doesn’t Work Yet · 2018
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Towards optimally decentralized multi-robot collision avoidance via deep reinforcement learning
P. Long, T. Fan, X. Liao, W. Liu, H. Zhang, and J. Pan · 2018
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Visual representations for semantic target driven navigation
A. Mousavian, A. Toshev, M. Fiser, J. Kosecka, and J. Davidson · 2018
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Learning navigation behaviors end-to-end with autorl
H.-T. L. Chiang, A. Faust, M. Fiser, and A. Francis · 2019
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Rl-rrt: Kinodynamic motion planning via learning reachability estimators from rl policies
H.-T. L. Chiang, J. Hsu, M. Fiser, L. Tapia, and A. Faust · 2019
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Getting robots unfrozen and unlost in dense pedestrian crowds
T. Fan, X. Cheng, J. Pan, P. Long, W. Liu, R. Yang, and D. Manocha · 2019
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http://wiki.ros.org/gmapping, 2019
gmapping - ROS Wiki · 2019
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Solving the optimal path planning of a mobile robot using improved q-learning
E. S. Low, P. Ong, and K. C. Cheah · 2019
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