R. Jonschkowski and O. Brock, Learning state representations with robotic priors, Autonomous Robots, vol.39, issue.3, pp.407-428, 2015.

F. Sadeghi and S. Levine, CAD2RL: Real single-image flight without a single real image, 2016.

J. Tobin, R. Fong, A. Ray, J. Schneider, W. Zaremba et al., Domain randomization for transferring deep neural networks from simulation to the real world, CoRR, 2017.

Y. Bengio, A. Courville, and P. Vincent, Representation learning: A review and new perspectives, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.35, issue.8, pp.1798-1828, 2013.

R. Jonschkowski, R. Hafner, J. Scholz, and M. Riedmiller, PVEs: Position-Velocity Encoders for Unsupervised Learning of Structured State Representations, 2017.

R. Jonschkowski and O. Brock, State representation learning in robotics: Using prior knowledge about physical interaction, Proceedings of Robotics: Science and Systems, 2014.

J. Scholz, M. Levihn, C. L. Isbell, and D. Wingate, A physics-based model prior for object-oriented mdps, ICML, 2014.

L. Pinto, D. Gandhi, Y. Han, Y. Park, and A. Gupta, The curious robot: Learning visual representations via physical interactions, CoRR, 2016.

T. Lesort, N. Díaz-rodríguez, J. Goudou, and D. Filliat, State representation learning for control: An overview, Neural Networks, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01858558

H. Seijen, S. Whiteson, and L. Kester, Efficient abstraction selection in reinforcement learning, Comput. Intell, vol.30, issue.4, pp.657-699, 2014.

G. Konidaris and A. Barto, Efficient skill learning using abstraction selection, Proceedings of the 21st International Joint Conference on Artificial Intelligence, 2009.

S. Lange, M. Riedmiller, and A. Voigtlander, Autonomous reinforcement learning on raw visual input data in a real world application

C. Finn, X. Y. Tan, Y. Duan, T. Darrell, S. Levine et al., Deep spatial autoencoders for visuomotor learning, 2016 IEEE International Conference on Robotics and Automation (ICRA), pp.512-519, 2016.

H. Van-hoof, N. Chen, M. Karl, P. Van-der-smagt, and J. Peters, Stable reinforcement learning with autoencoders for tactile and visual data, 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, IROS 2016, pp.3928-3934, 2016.

M. Watter, J. Springenberg, J. Boedecker, and M. Riedmiller, Embed to control: A locally linear latent dynamics model for control from raw images, Advances in Neural Information Processing Systems, vol.28, pp.2746-2754, 2015.

R. Goroshin, M. Mathieu, and Y. Lecun, Learning to linearize under uncertainty, CoRR, 2015.

B. Boots, S. M. Siddiqi, and G. J. Gordon, Closing the learningplanning loop with predictive state representations, CoRR, 2009.

S. P. Singh, M. R. James, and M. R. Rudary, Predictive state representations: A new theory for modeling dynamical systems, CoRR, 2012.

P. Sermanet, C. Lynch, J. Hsu, and S. Levine, Time-contrastive networks: Self-supervised learning from multi-view observation, CoRR, 2017.

P. Agrawal, J. Carreira, and J. Malik, Learning to see by moving, CoRR, 2015.

D. Jayaraman and K. Grauman, Learning image representations equivariant to ego-motion, CoRR, 2015.

S. Chopra, R. Hadsell, and Y. Lecun, Learning a similarity metric discriminatively, with application to face verification, CVPR, 2005.

E. P. Xing, A. Y. Ng, M. I. Jordan, and S. Russell, Distance metric learning, with application to clustering with side-information, ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 15, pp.505-512, 2003.

K. He, X. Zhang, S. Ren, and J. Sun, Deep residual learning for image recognition, CoRR, 2015.

P. Zhang, Y. Ren, and B. Zhang, A new embedding quality assessment method for manifold learning, CoRR, 2011.

P. Indyk, Algorithmic applications of low-distortion geometric embeddings, Proceedings 2001 IEEE International Conference on Cluster Computing, pp.10-33, 2001.

A. Raffin, A. Hill, R. Traoré, T. Lesort, N. Díaz-rodríguez et al., S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning, NeurIPS Workshop on Deep Reinforcement Learning, 2018.
URL : https://hal.archives-ouvertes.fr/hal-01931713

P. Baldi, Autoencoders, unsupervised learning, and deep architectures, Proceedings of ICML Workshop on Unsupervised and Transfer Learning, pp.37-49, 2012.

J. Munk, J. Kober, and R. Babuska, Learning state representation for deep actor-critic control, pp.4667-4673, 2016.