Fetching the paper…
Reading the bibliography…
This work presents a case study of a learning-based approach for target driven map-less navigation.
V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu, “Asynchronous methods for deep reinforcement learning,” in
1937
Earlier work this paper cites.
R. J. Williams, “Simple statistical gradient-following algorithms for connectionist reinforcement learning,” in
1992
Earlier work this paper cites.
U. Muller, J. Ben, E. Cosatto, B. Flepp, and Y. L. Cun, “Off-road obstacle avoidance through end-to-end learning,” in
2005
Earlier work this paper cites.
S. M. LaValle,
2006
Earlier work this paper cites.
P. Abbeel, D. Dolgov, A. Ng, and S. Thrun, “Apprenticeship learning for motion planning with application to parking lot navigation,” in
2008
Earlier work this paper cites.
R. Vaughan, “Massively multi-robot simulation in stage,”
2008
Earlier work this paper cites.
D. B. Grimes and R. P. Rao, “Learning actions through imitation and exploration: Towards humanoid robots that learn from humans,” in
2009
Earlier work this paper cites.
M. Quigley, K. Conley, B. Gerkey, J. Faust, T. Foote, J. Leibs, R. Wheeler, and A. Y. Ng, “Ros: an open-source robot operating system,” in
2009
Earlier work this paper cites.
S. Ross, G. Gordon, and D. Bagnell, “A reduction of imitation learning and structured prediction to no-regret online learning,” in
2011
Earlier work this paper cites.
B. Balaguer and S. Carpin, “Combining imitation and reinforcement learning to fold deformable planar objects,”
2011
Earlier work this paper cites.
S. Ross, N. Melik-Barkhudarov, K. S. Shankar, A. Wendel, D. Dey, J. A. Bagnell, and M. Hebert, “Learning monocular reactive uav control in cluttered natural environments,” in
2013
Earlier work this paper cites.
B. Bischoff, D. Nguyen-Tuong, I.-H. Lee, F. Streichert, and A. Knoll, “Hierarchical reinforcement learning for robot navigation,” in
2013
Cited alongside, same era.
B. Zuo, J. Chen, L. Wang, and Y. Wang, “A reinforcement learning based robotic navigation system,”
2014
Cited alongside, same era.
C. Chen, A. Seff, A. Kornhauser, and J. Xiao, “Deepdriving: Learning affordance for direct perception in autonomous driving,” in
2015
Cited alongside, same era.
D. K. Kim and T. Chen, “Deep neural network for real-time autonomous indoor navigation,”
2015
Cited alongside, same era.
J. Sergeant, N. Sünderhauf, M. Milford, and B. Upcroft, “Multimodal deep autoencoders for control of a mobile robot,” in
2015
Cited alongside, same era.
J. Achiam, D. Held, A. Tamar, and P. Abbeel, “Constrained policy optimization,”
2017
Later among the works it cites.
2017
Later among the works it cites.
P. Mirowski, R. Pascanu, F. Viola, H. Soyer, A. J. Ballard, A. Banino, M. Denil, R. Goroshin, L. Sifre, K. Kavukcuoglu,
2017
Later among the works it cites.
2017
Later among the works it cites.
J. Zhang, J. T. Springenberg, J. Boedecker, and W. Burgard, “Deep reinforcement learning with successor features for navigation across similar environments,”
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
J. Schulman, S. Levine, P. Abbeel, M. Jordan, and P. Moritz, “Trust region policy optimization,” in
2015
Cited alongside, same era.
H. Kretzschmar, M. Spies, C. Sprunk, and W. Burgard, “Socially compliant mobile robot navigation via inverse reinforcement learning,”
2016
Cited alongside, same era.
J. Ho and S. Ermon, “Generative adversarial imitation learning,” in
2016
Cited alongside, same era.
M. Pfeiffer, M. Schaeuble, J. Nieto, R. Siegwart, and C. Cadena, “From perception to decision: A data-driven approach to end-to-end motion planning for autonomous ground robots,” in
2017
Cited alongside, same era.
L. Tai, G. Paolo, and M. Liu, “Virtual-to-real deep reinforcement learning: Continuous control of mobile robots for mapless navigation,” in
2017
Cited alongside, same era.
A. Kuefler, J. Morton, T. Wheeler, and M. Kochenderfer, “Imitating driver behavior with generative adversarial networks,” in
2017
Cited alongside, same era.
2017
Later among the works it cites.
Y. Zhu, R. Mottaghi, E. Kolve, J. J. Lim, A. Gupta, L. Fei-Fei, and A. Farhadi, “Target-driven visual navigation in indoor scenes using deep reinforcement learning,” in
2017
Later among the works it cites.
2017
Later among the works it cites.
F. Berkenkamp, M. Turchetta, A. Schoellig, and A. Krause, “Safe model-based reinforcement learning with stability guarantees,” in
2017
Later among the works it cites.
2018
Closest in time.
M. Wulfmeier, D. Z. Wang, and I. Posner, “Watch this: Scalable cost-function learning for path planning in urban environments,” in
2095
Closest in time.
M. Pfeiffer, U. Schwesinger, H. Sommer, E. Galceran, and R. Siegwart, “Predicting actions to act predictably: Cooperative partial motion planning with maximum entropy models,” in
2096
Closest in time.