Virtual-to-real Deep Reinforcement Learning: Continuous Control of Mobile Robots for Mapless Navigation

Y. LeCun, U. Muller, J. Ben, E. Cosatto, and B. Flepp, “Off-road obstacle avoidance through end-to-end learning,” in NIPS , 2005, pp. 739–746

2005

Earlier work this paper cites.

H. Durrant-Whyte and T. Bailey, “Simultaneous localization and mapping: part i,” IEEE robotics & automation magazine , vol. 13, no. 2, pp. 99–110, 2006

2006

Earlier work this paper cites.

C. E. Rasmussen, “Gaussian processes for machine learning,” 2006

2006

Earlier work this paper cites.

J. Kober, J. A. Bagnell, and J. Peters, “Reinforcement learning in robotics: A survey,” The International Journal of Robotics Research , vol. 32, no. 11, pp. 1238–1274, 2013

2013

Earlier work this paper cites.

E. Rohmer, S. P. Singh, and M. Freese, “V-rep: A versatile and scalable robot simulation framework,” in Intelligent Robots and Systems (IROS), 2013 IEEE/RSJ International Conference on . IEEE, 2013, pp. 1321–1326

2013

Earlier work this paper cites.

Y. Sun, M. Liu, and M. Q.-H. Meng, “Wifi signal strength-based robot indoor localization,” in Information and Automation (ICIA), 2014 IEEE International Conference on . IEEE, 2014, pp. 250–256

2014

Earlier work this paper cites.

D. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014

Original

2014

Earlier work this paper cites.

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. , “Human-level control through deep reinforcement learning,” Nature , vol. 518, no. 7540, pp. 529–533, 2015

2015

Earlier work this paper cites.

T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, and D. Wierstra, “Continuous control with deep reinforcement learning,” arXiv preprint arXiv:1509.02971 , 2015

Original

2015

Cited alongside, same era.

C. Chen, A. Seff, A. Kornhauser, and J. Xiao, “Deepdriving: Learning affordance for direct perception in autonomous driving,” in Proceedings of the IEEE International Conference on Computer Vision , 2015, pp. 2722–2730

2015

Cited alongside, same era.

M. Hausknecht and P. Stone, “Deep recurrent q-learning for partially observable mdps,” arXiv preprint arXiv:1507.06527 , 2015

Original

2015

Cited alongside, same era.

S. Gu, T. Lillicrap, I. Sutskever, and S. Levine, “Continuous deep q-learning with model-based acceleration,” in Proceedings of The 33rd International Conference on Machine Learning , 2016, pp. 2829–2838

2016

Cited alongside, same era.

V. Mnih, A. P. Badia, M. Mirza, A. Graves, T. P. Lillicrap, T. Harley, D. Silver, and K. Kavukcuoglu, “Asynchronous methods for deep reinforcement learning,” in International Conference on Machine Learning , 2016

2016

Later among the works it cites.

L. Tai, S. Li, and M. Liu, “A Deep-network Solution Towords Model-less Obstacle Avoidence,” in IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016 , 2016

2016

Later among the works it cites.

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,” arXiv preprint arXiv:1609.07910 , 2016

Original

2016

Later among the works it cites.

H. Kretzschmar, M. Spies, C. Sprunk, and W. Burgard, “Socially compliant mobile robot navigation via inverse reinforcement learning,” The International Journal of Robotics Research , vol. 35, no. 11, pp. 1289–1307, 2016

2016

Later among the works it cites.