Fetching the paper…
Reading the bibliography…
We learn end-to-end point-to-point and path-following navigation behaviors that avoid moving obstacles.
Real-time obstacle avoidance for manipulators and mobile robots
O. Khatib · 1986
Earlier work this paper cites.
Efficient training of artificial neural networks for autonomous navigation
D. A. Pomerleau · 1991
Earlier work this paper cites.
On the adaptation of arbitrary normal mutation distributions in evolution strategies: The generating set adaptation
N. Hansen, A. Ostermeier, and A. Gawelczyk · 1995
Earlier work this paper cites.
Social force model for pedestrian dynamics
D. Helbing and P. Molnar · 1995
Earlier work this paper cites.
Probabilistic roadmaps for path planning in high-dimensional configuration spaces
L. E. Kavraki, P. Švestka, J. C. Latombe, and M. H. Overmars · 1996
Earlier work this paper cites.
The dynamic window approach to collision avoidance
D. Fox, W. Burgard, and S. Thrun · 1997
Earlier work this paper cites.
Policy invariance under reward transformations: Theory and application to reward shaping
A. Y. Ng, D. Harada, and S. Russell · 1999
Earlier work this paper cites.
Social potential fields: A distributed behavioral control for autonomous robots
J. H. Reif and H. Wang · 1999
Earlier work this paper cites.
Randomized kinodynamic planning
S. M. LaValle and J. James J. Kuffner · 2001
Earlier work this paper cites.
Effective reinforcement learning for mobile robots
W. D. Smart and L. P. Kaelbling · 2002
Earlier work this paper cites.
Robust artificial life via artificial programmed death
M. Olsen, N. Siegelmann-Danieli, and H. Siegelmann · 2008
Earlier work this paper cites.
The office marathon: Robust navigation in an indoor office environment
E. Marder-Eppstein, E. Berger, T. Foote, B. Gerkey, and K. Konolige · 2010
Earlier work this paper cites.
Hierarchical reinforcement learning for robot navigation
B. Bischoff, D. Nguyen-Tuong, I. Lee, F. Streichert, A. Knoll, et al · 2013
Earlier work this paper cites.
Imitation learning with demonstrations and shaping rewards
K. Judah, A. P. Fern, P. Tadepalli, and R. Goetschalckx · 2014
Earlier work this paper cites.
On learning navigation behaviors for small mobile robots with reservoir computing architectures
E. A. Antonelo and B. Schrauwen · 2015
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.
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.
Human-level control through deep reinforcement learning
V. Mnih, K. Kavukcuoglu, D. Silver, A. A. Rusu, J. Veness, M. G. Bellemare, A. Graves, M. Riedmiller, A. K. Fidjeland, G. Ostrovski, et al · 2015
Cited alongside, same era.
Value iteration networks
A. Tamar, Y. Wu, G. Thomas, S. Levine, and P. Abbeel · 2016
Later among the works it cites.
Learning to reinforcement learn
J. X. Wang, Z. Kurth-Nelson, D. Tirumala, H. Soyer, J. Z. Leibo, R. Munos, C. Blundell, D. Kumaran, and M. Botvinick · 2016
Later among the works it cites.
Safety, challenges, and performance of motion planners in dynamic environments
H.-T. L. Chiang, B. HomChaudhuri, L. Smith, and L. Tapia · 2017
Later among the works it cites.
Reverse curriculum generation for reinforcement learning
C. Florensa, D. Held, M. Wulfmeier, M. Zhang, and P. Abbeel · 2017
Later among the works it cites.
Intention-net: Integrating planning and deep learning for goal-directed autonomous navigation
W. Gao, D. Hsu, W. S. Lee, S. Shen, and K. Subramanian · 2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Reward shaping with recurrent neural networks for speeding up on-line policy learning in spoken dialogue systems
P. Su, D. Vandyke, M. Gašić, N. Mrkšić, T. Wen, and S. Young · 2015
Cited alongside, same era.
Reinforcement learning-based mobile robot navigation
N. Altuntaş, E. Imal, N. Emanet, and C. N. Öztürk · 2016
Cited alongside, same era.
Learning to learn by gradient descent by gradient descent
M. Andrychowicz, M. Denil, S. Gomez, M. W. Hoffman, D. Pfau, T. Schaul, and N. de Freitas · 2016
Cited alongside, same era.
Unifying count-based exploration and intrinsic motivation
M. Bellemare, S. Srinivasan, G. Ostrovski, T. Schaul, D. Saxton, and R. Munos · 2016
Cited alongside, same era.
Past, present, and future of simultaneous localization and mapping: Toward the robust-perception age
C. Cadena, L. Carlone, H. Carrillo, Y. Latif, D. Scaramuzza, J. Neira, I. Reid, and J. J. Leonard · 2016
Cited alongside, same era.
Supervised fuzzy reinforcement learning for robot navigation
F. Fathinezhad, V. Derhami, and M. Rezaeian · 2016
Cited alongside, same era.
Avoiding moving obstacles with stochastic hybrid dynamics using pearl: preference appraisal reinforcement learning
A. Faust, H.-T. Chiang, N. Rackley, and L. Tapia · 2016
Cited alongside, same era.
D. Golovin, B. Solnik, S. Moitra, G. Kochanski, J. Karro, and D. Sculley · 2017
Later among the works it cites.
Automated curriculum learning for neural networks
A. Graves, M. G. Bellemare, J. Menick, R. Munos, and K. Kavukcuoglu · 2017
Later among the works it cites.
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
Later among the works it cites.
Large-scale evolution of image classifiers
E. Real, S. Moore, A. Selle, S. Saxena, Y. L. Suematsu, J. Tan, Q. V. Le, and A. Kurakin · 2017
Later among the works it cites.
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
Closest in time.
Barc: Backward reachability curriculum for robotic reinforcement learning
B. Ivanovic, J. Harrison, A. Sharma, M. Chen, and M. Pavone · 2018
Closest in time.
Visual representations for semantic target driven navigation
A. Mousavian, A. Toshev, M. Fiser, J. Kosecka, and J. Davidson · 2018
Closest in time.
TF-Agents: A library for reinforcement learning in tensorflow
Sergio Guadarrama, Anoop Korattikara, Oscar Ramirez, Pablo Castro, Ethan Holly, Sam Fishman, Ke Wang, Ekaterina Gonina, Chris Harris, Vincent Vanhoucke, Eugene Brevdo · 2018
Closest in time.