W. S. Cleveland and S. J. Devlin, Locally Weighted Regression: An Approach to Regression Analysis by Local Fitting, Journal of the American Statistical Association, vol.41, issue.810345, p.596610, 1988.
DOI : 10.1080/01621459.1988.10478639

C. G. Atkeson, A. W. Moore, and S. Schaal, Locally Weighted Learning, Artificial Intelligence Review, vol.11, issue.1, pp.11-7310, 1997.
DOI : 10.1007/978-94-017-2053-3_2

T. Munzer, F. Stulp, and O. Sigaud, Non-linear regression algorithms for motor skill acquisition: a comparison, p.14, 2014.
URL : https://hal.archives-ouvertes.fr/hal-01090848

R. Penrose, A generalized inverse for matrices, Mathematical Proceedings of the Cambridge Philosophical Society, vol.11, issue.03, pp.406-413, 1955.
DOI : 10.1093/qmath/2.1.189

R. H. Byrd, P. Lu, and J. Nocedal, A Limited Memory Algorithm for Bound Constrained Optimization, SIAM Journal on Scientific Computing, vol.16, issue.5, pp.1190-1208, 1995.
DOI : 10.1137/0916069

C. Zhu, R. H. Byrd, and J. Nocedal, Algorithm 778: L-BFGS-B: Fortran subroutines for large-scale bound-constrained optimization, ACM Transactions on Mathematical Software, vol.23, issue.4, pp.550-560, 1997.
DOI : 10.1145/279232.279236

A. Baranes and P. Oudeyer, Active learning of inverse models with intrinsically motivated goal exploration in robots, Robotics and Autonomous Systems, vol.61, issue.1, p.2012
DOI : 10.1016/j.robot.2012.05.008

URL : https://hal.archives-ouvertes.fr/hal-00788440

M. Rolf, Goal Babbling for an Efficient Bootstrapping of Inverse Models in High Dimensions, p.2012

M. Lopes, T. Lang, M. Toussaint, and P. Oudeyer, Exploration in model-based reinforcement learning by empirically estimating learning progress, Neural Information Processing System (NIPS), p.2012
URL : https://hal.archives-ouvertes.fr/hal-00755248

F. Benureau and P. Oudeyer, Autonomous reuse of motor exploration trajectories, 2013 IEEE Third Joint International Conference on Development and Learning and Epigenetic Robotics (ICDL)
DOI : 10.1109/DevLrn.2013.6652567

URL : https://hal.archives-ouvertes.fr/hal-00850759

F. Fernández and M. Veloso, Probabilistic policy reuse in a reinforcement learning agent, Proceedings of the fifth international joint conference on Autonomous agents and multiagent systems , AAMAS '06, pp.720-727, 2006.
DOI : 10.1145/1160633.1160762

M. E. Taylor, N. K. Jong, and P. Stone, Transferring Instances for Model-Based Reinforcement Learning, Machine Learning and Knowledge Discovery in Databases, pp.488-505, 2008.
DOI : 10.1007/978-3-540-87481-2_32

M. E. Taylor and P. Stone, Transfer learning for reinforcement learning domains: A survey, The Journal of Machine Learning Research, vol.10, pp.1633-1685, 2009.

S. J. Pan and Q. Yang, A Survey on Transfer Learning, IEEE Transactions on Knowledge and Data Engineering, vol.22, issue.10, pp.1345-1359
DOI : 10.1109/TKDE.2009.191

L. Torrey and J. Shavlik, Transfer Learning, Handbook of Research on Machine Learning Applications, 2009.
DOI : 10.4018/978-1-60566-766-9.ch011

F. Doshi-velez and G. D. Konidaris, Transfer Learning by Discovering Latent Task Parametrizations, the NIPS 2012 Workshop on Bayesian Nonparametric Models for Reliable Planning And Decision-Making Under Uncertainty, 2012.

M. G. Madden and T. Howley, Transfer of Experience Between Reinforcement Learning Environments with Progressive Difficulty, Artificial Intelligence Review, vol.21, issue.3/4, pp.3-4, 2004.
DOI : 10.1023/B:AIRE.0000036264.95672.64

S. Barrett, M. Taylor, and P. Stone, Transfer learning for reinforcement learning on a physical robot, The Ninth International Conference on Autonomous Agents and Multiagent Systems - Adaptive Learning Agents Workshop, 2010.

S. Thrun and T. Mitchell, Lifelong robot learning, Robotics and Autonomous Systems, vol.15, issue.1-2, 1995.
DOI : 10.1016/0921-8890(95)00004-Y

URL : http://citeseerx.ist.psu.edu/viewdoc/summary?doi=

D. L. Silver, Y. Qiang, and L. Lianghao, Lifelong Machine Learning Systems: Beyond Learning Algorithms, 2013 AAAI Spring Symposium Series, 2013.

J. Konczak, On the notion of motor primitives in humans and robots, 2005.

A. J. Ijspeert, J. Nakanishi, and S. Schaal, Movement imitation with nonlinear dynamical systems in humanoid robots, Proceedings 2002 IEEE International Conference on Robotics and Automation (Cat. No.02CH37292), p.13981403, 2002.
DOI : 10.1109/ROBOT.2002.1014739

A. J. Ijspeert, J. Nakanishi, H. Hoffmann, P. Pastor, and S. Schaal, Dynamical Movement Primitives: Learning Attractor Models for Motor Behaviors, Neural Computation, vol.2010, issue.11, p.328373, 2012.
DOI : 10.1109/AT-EQUAL.2009.32

T. Kulvicius, K. Ning, M. Tamosiunaite, and F. Worgotter, Joining Movement Sequences: Modified Dynamic Movement Primitives for Robotics Applications Exemplified on Handwriting, IEEE Transactions on Robotics, vol.28, issue.1, pp.145-157, 2012.
DOI : 10.1109/TRO.2011.2163863

F. Stulp, DmpBbo ? A C++ library for black-box optimization of dynamical movement primitives, p.2014