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), 2016.
DOI : 10.1109/ICRA.2016.7487173

URL : http://arxiv.org/pdf/1509.06113

S. Levine, C. Finn, T. Darrell, and P. Abbeel, End-to-end Training of Deep Visuomotor Policies, J. Mach. Learn. Res, 2016.

A. Ghadirzadeh, A. Maki, D. Kragic, and M. Björkman, Deep predictive policy training using reinforcement learning, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017.
DOI : 10.1109/IROS.2017.8206046

URL : http://arxiv.org/pdf/1703.00727

Y. Chebotar, K. Hausman, M. Zhang, G. S. Sukhatme, S. Schaal et al., Combining Model-Based and Model-Free Updates for Trajectory-Centric Reinforcement Learning, ICML, 2017.

M. P. Deisenroth, C. E. Rasmussen, and D. Fox, Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning, Robotics: Science and Systems VII, 2011.
DOI : 10.15607/RSS.2011.VII.008

URL : https://doi.org/10.15607/rss.2011.vii.008

S. Gu, E. Holly, T. P. Lillicrap, and S. Levine, Deep reinforcement learning for robotic manipulation with asynchronous off-policy updates, 2017 IEEE International Conference on Robotics and Automation (ICRA), 2017.
DOI : 10.1109/ICRA.2017.7989385

URL : http://arxiv.org/pdf/1610.00633

Y. Tsurumine, Y. Cui, E. Uchibe, and T. Matsubara, Deep dynamic policy programming for robot control with raw images, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2017.
DOI : 10.1109/IROS.2017.8205960

S. Levine, N. Wagener, and P. Abbeel, Learning contact-rich manipulation skills with guided policy search, 2015 IEEE International Conference on Robotics and Automation (ICRA), 2015.
DOI : 10.1109/ICRA.2015.7138994

URL : http://arxiv.org/pdf/1501.05611

H. Hoffmann, W. Schenck, and R. Möller, Learning visuomotor transformations for gaze-control and grasping, Biological Cybernetics, vol.331, issue.1, 2005.
DOI : 10.1016/B978-0-444-88400-8.50047-9

D. Carey, R. Coleman, and S. D. Salla, Magnetic Misreaching Cortex, 2018.

K. Fischer, A theory of cognitive development: The control and construction of hierarchies of skills., Psychological Review, vol.87, issue.6, 1980.
DOI : 10.1037/0033-295X.87.6.477

F. Nori, L. Natale, G. Sandini, and G. Metta, Autonomous learning of 3D reaching in a humanoid robot, 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2007.
DOI : 10.1109/IROS.2007.4399467

J. Law, P. Shaw, M. Lee, and M. Sheldon, From Saccades to Grasping: A Model of Coordinated Reaching Through Simulated Development on a Humanoid Robot, IEEE Transactions on Autonomous Mental Development, vol.6, issue.2, 2014.
DOI : 10.1109/TAMD.2014.2301934

E. Chinellato, M. Antonelli, B. J. Grzyb, and A. P. , Implicit Sensorimotor Mapping of the Peripersonal Space by Gazing and Reaching, IEEE Transactions on Autonomous Mental Development, vol.3, issue.1, 2011.
DOI : 10.1109/TAMD.2011.2106781

S. Levine, P. Pastor, A. Krizhevsky, J. Ibarz, and D. Quillen, Learning hand-eye coordination for robotic grasping with deep learning and large-scale data collection, The International Journal of Robotics Research, vol.3361, issue.10, 2017.
DOI : 10.1109/ROBOT.1994.350995

A. Boularias, J. A. Bagnell, and A. T. Stentz, Learning to Manipulate Unknown Objects in Clutter by Reinforcement, AAAI, 2015.

M. Asada, S. Noda, S. Tawaratsumida, and K. Hosoda, Purposive Behavior Acquisition for a Real Robot by Vision-Based Reinforcement Learning, Machine Learning, 1996.

C. Florensa, D. Held, M. Wulfmeier, M. Zhang, and P. Abbeel, Reverse Curriculum Generation for Reinforcement Learning, CoRL, 2017.

T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez et al., Continuous control with deep reinforcement learning, ICLR, 2016.

D. Silver, G. Lever, N. Heess, T. Degris, D. Wierstra et al., Deterministic Policy Gradient Algorithms, ICML, 2014.
URL : https://hal.archives-ouvertes.fr/hal-00938992

V. Mnih, K. Kavukcuoglu, D. Silver, A. Rusu-andrei, J. Veness et al., Human-level control through deep reinforcement learning, Nature, vol.101, issue.7540, 2015.
DOI : 10.1016/S0004-3702(98)00023-X

M. J. Hausknecht and P. Stone, Deep Reinforcement Learning in Parameterized Action Space, ICLR, 2016.

F. De-la-bourdonnaye, C. Teulière, T. Chateau, and J. Triesch, Learning of binocular fixations using anomaly detection with deep reinforcement learning, 2017 International Joint Conference on Neural Networks (IJCNN), 2017.
DOI : 10.1109/IJCNN.2017.7965928

URL : https://hal.archives-ouvertes.fr/hal-01635610

G. Metta and P. Fitzpatrick, Early integration of vision and manipulation, Proceedings of the International Joint Conference on Neural Networks, 2003., 2003.
DOI : 10.1109/IJCNN.2003.1223994

URL : http://www.ai.mit.edu/people/paulfitz/pub/ab02early.pdf

M. Quigley, K. Conley, B. P. Gerkey, J. Faust, T. Foote et al., ROS: an open-source Robot Operating System, ICRA Workshop on Open Source Software, 2009.

A. Rajeswaran, K. Lowrey, E. V. Todorov, and S. M. Kakade, Towards Generalization and Simplicity in Continuous Control, Advances in NIPS, 2017.

Y. Jia, E. Shelhamer, J. Donahue, S. Karayev, J. Long et al., Caffe, Proceedings of the ACM International Conference on Multimedia, MM '14, 2014.
DOI : 10.1145/2647868.2654889

D. Kingma and J. Ba, Adam: A Method for Stochastic Optimization, ICLR, 2015.