J. Weng, J. Mcclelland, A. Pentland, O. Sporns, I. Stockman et al., ARTIFICIAL INTELLIGENCE: Autonomous Mental Development by Robots and Animals, Science, vol.291, issue.5504, pp.599-600, 2001.
DOI : 10.1126/science.291.5504.599

M. Lungarella, G. Metta, R. Pfeifer, and G. Sandini, Developmental robotics: a survey, Connection Science, vol.1, issue.4, pp.151-190, 2003.
DOI : 10.2307/1131322

M. Asada, S. Noda, S. Tawaratsumida, and K. Hosoda, Purposive behavior acquisition on a real robot by vision-based reinforcement learning, Machine Learning, pp.279-303, 1996.

J. Elman, Learning and development in neural networks: the importance of starting small, Cognition, vol.48, issue.1, pp.71-99, 1993.
DOI : 10.1016/0010-0277(93)90058-4

R. White, Motivation reconsidered: The concept of competence., Psychological Review, vol.66, issue.5, pp.297-333, 1959.
DOI : 10.1037/h0040934

E. Deci and R. Ryan, Intrinsic Motivation and Self-Determination in Human Behavior, 1985.
DOI : 10.1007/978-1-4899-2271-7

M. Csikszenthmihalyi, Flow-the psychology of optimal experience, 1991.

W. Schultz, P. Dayan, and P. Montague, A Neural Substrate of Prediction and Reward, Science, vol.275, issue.5306, pp.1593-1599, 1997.
DOI : 10.1126/science.275.5306.1593

P. Dayan and W. Belleine, Reward, Motivation, and Reinforcement Learning, Neuron, vol.36, issue.2, pp.285-298, 2002.
DOI : 10.1016/S0896-6273(02)00963-7

URL : http://doi.org/10.1016/s0896-6273(02)00963-7

S. Kakade and P. Dayan, Dopamine: generalization and bonuses, Neural Networks, vol.15, issue.4-6, pp.549-559, 2002.
DOI : 10.1016/S0893-6080(02)00048-5

J. Horvitz, Mesolimbocortical and nigrostriatal dopamine responses to salient non-reward events, Neuroscience, vol.96, issue.4, pp.651-656, 2000.
DOI : 10.1016/S0306-4522(00)00019-1

M. Csikszentmihalyi, Creativity-flow and the psychology of discovery and invention. Harper perennial, 1996.

J. Schmidhuber, Curious model-building control systems, [Proceedings] 1991 IEEE International Joint Conference on Neural Networks, pp.1458-1463, 1991.
DOI : 10.1109/IJCNN.1991.170605

S. Thrun, Exploration in active learning, Handbook of Brain Science and Neural Networks, M. Arbib, 1995.

J. Herrmann, K. Pawelzik, and T. Geisel, Learning predicitve representations, Neurocomputing, pp.32-33, 2000.

J. Weng, A theory for mentally developing robots, Second International Conference on Development and Learning, 2002.

X. Huang and J. Weng, Novelty and reinforcement learning in the value system of developmental robots, Proceedings of the 2nd international workshop on Epigenetic Robotics : Modeling cognitive development in robotic systems Lund University Cognitive Studies 94, pp.47-55, 2002.

F. Kaplan and P. Oudeyer, Motivational principles for visual knowhow development Modeling cognitive development in robotic systems, Proceedings of the 3rd international workshop on Epigenetic Robotics Lund University Cognitive Studies 101, pp.73-80, 2003.

J. Marshall, D. Blank, and L. Meeden, An emergent framework for selfmotivation in developmental robotics, Proceedings of the 3rd International Conference on Development and Learning Salk Institute, 2004.

A. Barto, S. Singh, and N. Chentanez, Intrinsically motivated learning of hierarchical collections of skills, Proceedings of the 3rd International Conference on Development and Learning Salk Institute, 2004.

V. Fedorov, Theory of Optimal Experiment, 1972.

D. Cohn, Z. Ghahramani, and M. Jordan, Active learning with statistical models, Journal of artificial intelligence research, vol.4, pp.129-145, 1996.

M. Hasenjager and H. Ritter, Active learning in neural networks, ser. Physica-Verlag Studies In Fuzziness And Soft Computing Series, pp.137-169, 2002.

J. Denzler and C. Brown, Information theoretic sensor data selection for active object recognition and state estimation, IEEE Transactions on Pattern Analysis and Machine Intelligence, vol.24, issue.2, pp.145-157, 2001.
DOI : 10.1109/34.982896

M. Plutowsky and H. White, Selecting concise training sets from clean data, IEEE Transactions on Neural Networks, vol.4, issue.2, pp.305-318, 1993.
DOI : 10.1109/72.207618

T. Watkin and A. Rau, Selecting examples for perceptrons, Journal of Physics A: Mathematical and General, vol.25, issue.1, pp.113-121, 1992.
DOI : 10.1088/0305-4470/25/1/016

D. Mackay, Information-Based Objective Functions for Active Data Selection, Neural Computation, vol.4, issue.4, pp.590-604, 1992.
DOI : 10.1088/0266-5611/1/3/006

M. Belue, K. Bauer, and D. Ruck, Selecting Optimal Experiments for Multiple Output Multilayer Perceptrons, Neural Computation, vol.9, issue.1, pp.161-183, 1997.
DOI : 10.1088/0305-4470/25/1/016

G. Paas and J. Kindermann, Bayesian query construction for neural network models, Advances in Neural Processing Systems, pp.443-450, 1995.

K. O. Hasenjager and H. Ritter, Active Learning in Self-Organizing Maps, pp.57-70, 1999.
DOI : 10.1016/B978-044450270-4/50005-X

D. Cohn, L. Atlas, and R. Ladner, Improving generalization with active learning, Machine Learning, pp.201-221, 1994.
DOI : 10.1007/BF00993277

J. Poland and A. Zell, Different criteria for active learning in neural networks: A comparative study, Proceedings of the 10th European Symposium on Artificial Neural Networks, M. Verleysen, pp.119-124, 2002.

J. Weng, DEVELOPMENTAL ROBOTICS: THEORY AND EXPERIMENTS, International Journal of Humanoid Robotics, vol.01, issue.02, pp.199-236, 2004.
DOI : 10.1142/S0219843604000149

N. Roy and A. Mccallum, Towards optimal active learning through sampling estimation of error reduction, Proc. 18th Intl Conf. Machine Learning, 2001.

R. Collobert and S. Bengio, Svmtorch: Support vector machines for largescale regression problems, Journal of Machine Learning Research, vol.1, pp.143-160, 2001.

R. Sutton and A. Barto, Reinforcement Learning: An Introduction, IEEE Transactions on Neural Networks, vol.9, issue.5, 1998.
DOI : 10.1109/TNN.1998.712192

C. Walkins and P. Dayan, Q-learning, Machine learning, vol.8, pp.279-292, 1992.

K. Kaneko and I. Tsuda, Complex systems : chaos and beyond, 2000.
DOI : 10.1007/978-3-642-56861-9

O. Sporns and T. Pegors, Information-theoretical aspects of embodied artificial intelligence, " in Embodied artificial intelligence, ser. LNAI 3139, pp.74-85, 2003.

J. Piaget, The origins of intelligence in children, 1952.
DOI : 10.1037/11494-000

O. Michel, Webots: Professional mobile robot simulation, International Journal of Advanced Robotic Systems, vol.1, issue.1, pp.39-42, 2004.
DOI : 10.5772/5618

URL : http://doi.org/10.5772/5618

J. Rekimoto and Y. Ayatsuka, CyberCode, Proceedings of DARE 2000 on Designing augmented reality environments , DARE '00, pp.1-10, 2000.
DOI : 10.1145/354666.354667

S. Schaal, C. Atkeson, and S. Vijayakumar, Scalable techniques from nonparameteric statistics for real-time robot learning, Applied Intelligence, vol.17, issue.1, pp.49-60, 2002.
DOI : 10.1023/A:1015727715131

E. Thelen and L. B. Smith, A dynamic systems approach to the development of cognition and action, 1994.

R. D. Beer, The Dynamics of Active Categorical Perception in an Evolved Model Agent, Adaptive Behavior, vol.11, issue.4, pp.209-243, 2003.
DOI : 10.1177/1059712303114001

S. Nolfi and J. Tani, Extracting regularities in space and time through a cascade of prediction networks, Connection Science, vol.11, issue.2, pp.129-152, 1999.

M. Arbib, The handbook of brain theory and neural networks, 2003.

M. Minsky, A framework for representing knowledge, " in The psychology of computer vision, P. Wiston, pp.211-277, 1975.

DOI : 10.1016/B978-1-4832-1446-7.50019-4

URL : https://hal.archives-ouvertes.fr/hal-00692030

G. L. Drescher, Made-up minds, 1991.

R. Sutton, D. Precup, and S. Singh, Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning, Artificial Intelligence, vol.112, issue.1-2, pp.181-211, 1999.
DOI : 10.1016/S0004-3702(99)00052-1

K. Doya, K. Samejima, K. Katagiri, and M. Kawato, Multiple Model-Based Reinforcement Learning, Neural Computation, vol.3, issue.6, pp.1347-1369, 2002.
DOI : 10.1016/S1364-6613(98)01221-2

J. Tani and S. Nolfi, Learning to perceive the world as articulated: an approach for hierarchical learning in sensory-motor systems, Neural Networks, vol.12, issue.7-8, pp.1131-1141, 1999.
DOI : 10.1016/S0893-6080(99)00060-X

M. Tomasello, M. Carptenter, J. Call, T. Behne, and H. Moll, Understanding and sharing intentions: The origins of cultural cognition, Behavioral and Brain Sciences, vol.28, issue.05, 2004.
DOI : 10.1017/S0140525X05000129

F. Dignum and R. Conte, Intentional agents and goal formation, LNCS 1365: Proceedings of the 4th International Workshop on Intelligent Agents IV, Agent Theories, Architectures, and Languages, pp.231-243, 1997.
DOI : 10.1007/BFb0026762

F. Kaplan and V. Hafner, The challenges of joint attention, Interaction Studies, vol.7, issue.2, pp.128-134, 2006.

A. Robins, Transfer in Cognition, Connection Science, vol.8, issue.2, pp.185-204, 1996.
DOI : 10.1080/095400996116875

G. Lakoff and M. Johnson, Philosophy in the flesh: the embodied mind and its challenge to Western thought. Basic Books, 1998.

D. Gentner, K. Holyoak, and N. Kokinov, The analogical mind: perspectives from cognitive science, p.78, 2001.

L. Pratt and B. Jennings, A Survey of Connectionist Network Reuse Through Transfer, Connection Science, vol.8, issue.2, pp.163-184, 1996.
DOI : 10.1007/978-1-4615-5529-2_2

J. Tani, M. Ito, and Y. Sugita, Self-organization of distributedly represented multiple behavior schemata in a mirror system: reviews of robot experiments using RNNPB, Neural Networks, vol.17, issue.8-9, pp.1273-1289, 2004.
DOI : 10.1016/j.neunet.2004.05.007

F. Kaplan and P. Oudeyer, The progress-drive hypothesis: an interpretation of early imitation, " in Models and mechanisms of imitation and social learning: Behavioural, social and communication dimensions

L. Vygotsky, Mind in society, 1978.

L. Steels, The Autotelic Principle, Embodied Artificial Intelligence
DOI : 10.1007/978-3-540-27833-7_17

A. Meltzoff and A. Gopnick, The role of imitation in understanding persons and developing a theory of mind, Understanding other minds, pp.335-366, 1993.

C. Moore and V. Corkum, Social Understanding at the End of the First Year of Life, Developmental Review, vol.14, issue.4, pp.349-372, 1994.
DOI : 10.1006/drev.1994.1014

P. Rochat, Ego function of early imitation, The Imitative Mind : Development , Evolution and Brain
DOI : 10.1017/CBO9780511489969.006

J. Baldwin, Mental development in the child and the race, 1925.

H. Schaffer, Early interactive development in studies of mother-infant interaction, Proceedings of Loch Lomonds Symposium, pp.3-18, 1977.

J. Piaget, Play, dreams and imitation in childhood, 1962.

J. Gibson, The ecological approach to visual perception, 1986.

. Baillie, URBI: towards a universal robotic low-level programming language, 2005 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2005.
DOI : 10.1109/IROS.2005.1545467