FR

EN

Homepage Inria website
  • Inria login
  • The Inria's Research Teams produce an annual Activity Report presenting their activities and their results of the year. These reports include the team members, the scientific program, the software developed by the team and the new results of the year. The report also describes the grants, contracts and the activities of dissemination and teaching. Finally, the report gives the list of publications of the year.

  • Legal notice
  • Cookie management
  • Personal data
  • Cookies


Bibliography

Major publications by the team in recent years
  • 1A. Baranes, P.-Y. Oudeyer.

    Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in Robots, in: Robotics and Autonomous Systems, January 2013, vol. 61, no 1, pp. 69-73. [ DOI : 10.1016/j.robot.2012.05.008 ]

    https://hal.inria.fr/hal-00788440
  • 2B. Clément, D. Roy, P.-Y. Oudeyer, M. Lopes.

    Multi-Armed Bandits for Intelligent Tutoring Systems, in: Journal of Educational Data Mining (JEDM), June 2015, vol. 7, no 2, pp. 20–48.

    https://hal.inria.fr/hal-00913669
  • 3C. Craye, T. Lesort, D. Filliat, J.-F. Goudou.

    Exploring to learn visual saliency: The RL-IAC approach, in: Robotics and Autonomous Systems, February 2019, vol. 112, pp. 244-259.

    https://hal.archives-ouvertes.fr/hal-01959882
  • 4S. Forestier, P.-Y. Oudeyer.

    Modular Active Curiosity-Driven Discovery of Tool Use, in: IEEE/RSJ International Conference on Intelligent Robots and Systems, Daejeon, South Korea, Proceedings of the 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2016.

    https://hal.archives-ouvertes.fr/hal-01384566
  • 5J. Gottlieb, P.-Y. Oudeyer.

    Towards a neuroscience of active sampling and curiosity, in: Nature Reviews Neuroscience, December 2018, vol. 19, no 12, pp. 758-770.

    https://hal.inria.fr/hal-01965608
  • 6S. Ivaldi, S. M. Nguyen, N. Lyubova, A. Droniou, V. Padois, D. Filliat, P.-Y. Oudeyer, O. Sigaud.

    Object learning through active exploration, in: IEEE Transactions on Autonomous Mental Development, 2013, pp. 1-18. [ DOI : 10.1109/TAMD.2013.2280614 ]

    https://hal.archives-ouvertes.fr/hal-00919694
  • 7A. Laversanne-Finot, A. Péré, P.-Y. Oudeyer.

    Curiosity Driven Exploration of Learned Disentangled Goal Spaces, in: CoRL 2018 - Conference on Robot Learning, Zürich, Switzerland, October 2018.

    https://hal.inria.fr/hal-01891598
  • 8T. Lesort, N. Díaz-Rodríguez, J.-F. Goudou, D. Filliat.

    State Representation Learning for Control: An Overview, in: Neural Networks, December 2018, vol. 108, pp. 379-392. [ DOI : 10.1016/j.neunet.2018.07.006 ]

    https://hal.archives-ouvertes.fr/hal-01858558
  • 9O. Mangin, D. Filliat, L. ten Bosch, P.-Y. Oudeyer.

    MCA-NMF: Multimodal Concept Acquisition with Non-Negative Matrix Factorization, in: PLoS ONE, October 2015, vol. 10, no 10, e0140732 p. [ DOI : 10.1371/journal.pone.0140732.t005 ]

    https://hal.archives-ouvertes.fr/hal-01137529
  • 10C. Moulin-Frier, S. M. Nguyen, P.-Y. Oudeyer.

    Self-Organization of Early Vocal Development in Infants and Machines: The Role of Intrinsic Motivation, in: Frontiers in Psychology, 2013, vol. 4, no 1006. [ DOI : 10.3389/fpsyg.2013.01006 ]

    https://hal.inria.fr/hal-00927940
Publications of the year

Doctoral Dissertations and Habilitation Theses

  • 11B. Clément.

    Adaptive Personalization of Pedagogical Sequences using Machine Learning, Ecole Doctorale de Mathématiques et Informatique, Université de Bordeaux, December 2018.

    https://hal.inria.fr/tel-01968241
  • 12F. Golemo.

    How to Train Your Robot - New Environments for Robotic Training and New Methods for Transferring Policies from the Simulator to the Real Robot, Université de Bordeaux, December 2018.

    https://hal.inria.fr/tel-01974203
  • 13A. Matricon.

    Merging robotic skills into more general skills, Université de Bordeaux, June 2018.

    https://tel.archives-ouvertes.fr/tel-01895789
  • 14W. Schueller.

    Active Control of Complexity Growth in Language Games, Université de Bordeaux, December 2018.

    https://hal.inria.fr/tel-01966815

Articles in International Peer-Reviewed Journals

  • 15Y. Chen, J.-B. Bordes, D. Filliat.

    Comparison studies on active cross-situational object-word learning using Non-Negative Matrix Factorization and Latent Dirichlet Allocation, in: IEEE Transactions on Cognitive and Developmental Systems, 2018. [ DOI : 10.1109/TCDS.2017.2725304 ]

    https://hal.archives-ouvertes.fr/hal-01561168
  • 16P.-A. Cinquin, P. Guitton, H. Sauzéon.

    Online e-learning and cognitive disabilities: A systematic review, in: Computers and Education, March 2019, vol. 130, pp. 152-167.

    https://hal.archives-ouvertes.fr/hal-01954983
  • 17C. Craye, D. Filliat, J.-F. Goudou.

    BioVision: a Biomimetics Platform for Intrinsically Motivated Visual Saliency Learning, in: IEEE Transactions on Cognitive and Developmental Systems, 2018. [ DOI : 10.1109/TCDS.2018.2806227 ]

    https://hal.archives-ouvertes.fr/hal-01728340
  • 18C. Craye, T. Lesort, D. Filliat, J.-F. Goudou.

    Exploring to learn visual saliency: The RL-IAC approach, in: Robotics and Autonomous Systems, February 2019, vol. 112, pp. 244-259.

    https://hal.archives-ouvertes.fr/hal-01959882
  • 19A. Delmas, B. Clément, P.-Y. Oudeyer, H. Sauzéon.

    Fostering Health Education With a Serious Game in Children With Asthma: Pilot Studies for Assessing Learning Efficacy and Automatized Learning Personalization, in: Frontiers in Education , November 2018, vol. 3. [ DOI : 10.3389/feduc.2018.00099 ]

    https://hal.archives-ouvertes.fr/hal-01922316
  • 20S. Doncieux, D. Filliat, N. Díaz-Rodríguez, T. Hospedales, R. Duro, A. Coninx, D. M. Roijers, B. Girard, N. Perrin, O. Sigaud.

    Open-Ended Learning: A Conceptual Framework Based on Representational Redescription, in: Frontiers in Neurorobotics, 2018, vol. 12, 59 p. [ DOI : 10.3389/fnbot.2018.00059 ]

    https://hal.sorbonne-universite.fr/hal-01889947
  • 21C. Fage, C. Consel, E. Balland, K. Etchegoyhen, A. Amestoy, M. Bouvard, H. Sauzéon.

    Tablet Apps to Support First School Inclusion of Children With Autism Spectrum Disorders (ASD) in Mainstream Classrooms: A Pilot Study, in: Frontiers in Psychology, October 2018, vol. 9. [ DOI : 10.3389/fpsyg.2018.02020 ]

    https://hal.inria.fr/hal-01904791
  • 22J. Gottlieb, P.-Y. Oudeyer.

    Towards a neuroscience of active sampling and curiosity, in: Nature Reviews Neuroscience, December 2018, vol. 19, no 12, pp. 758-770.

    https://hal.inria.fr/hal-01965608
  • 23T. Lesort, N. Díaz-Rodríguez, J.-F. Goudou, D. Filliat.

    State Representation Learning for Control: An Overview, in: Neural Networks, December 2018, vol. 108, pp. 379-392. [ DOI : 10.1016/j.neunet.2018.07.006 ]

    https://hal.archives-ouvertes.fr/hal-01858558
  • 24C. Mazon, C. Fage, H. Sauzéon.

    Effectiveness and usability of technology-based interventions for children and adolescents with ASD: A systematic review of reliability, consistency, generalization and durability related to the effects of intervention, in: Computers in Human Behavior, December 2018.

    https://hal.inria.fr/hal-01950078

International Conferences with Proceedings

  • 25C. Colas, O. Sigaud, P.-Y. Oudeyer.

    GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms, in: International Conference on Machine Learning (ICML), Stockholm, Sweden, July 2018.

    https://hal.inria.fr/hal-01890151
  • 26T. Desprez, S. Noirpoudre, T. Segonds, D. Caselli, D. Roy, P.-Y. Oudeyer.

    Poppy Ergo Jr : un kit robotique au coeur du dispositif Poppy Éducation, in: Didapro 7 2018 - DidaSTIC Colloque de didactique de l’informatique, Lausanne, Switzerland, February 2018, pp. 1-6.

    https://hal.inria.fr/hal-01753111
  • 27F. Golemo, A. A. Taïga, P.-Y. Oudeyer, A. Courville.

    Sim-to-Real Transfer with Neural-Augmented Robot Simulation, in: Conference on Robot Learning (CoRL) 2018, Zurich, Switzerland, October 2018.

    https://hal.inria.fr/hal-01911978
  • 28A. Laversanne-Finot, A. Péré, P.-Y. Oudeyer.

    Curiosity Driven Exploration of Learned Disentangled Goal Spaces, in: CoRL 2018 - Conference on Robot Learning, Zürich, Switzerland, October 2018.

    https://hal.inria.fr/hal-01891598
  • 29C. Mazon, C. Fage, A. Amestoy, I. Hesling, M. Bouvard, K. Etchegoyhen, H. Sauzéon.

    Cognitive mediators of school-related socio-adaptive behaviors in children and adolescents with ASD: A pilot study, in: 4th International Congress of Clinical and Health Pscyhology on Children and Adolescents, Palma de Mallorca, Spain, Book of Abstracts of the 4th International Congress of Clinical and Health Pscyhology on Children and Adolescents, Aitana Investigacíon, November 2018.

    https://hal.inria.fr/hal-01939740
  • 30P. Papadakis, D. Filliat.

    Generic Object Discrimination for Mobile Assistive Robots using Projective Light Diffusion, in: WACV 2018 - IEEE Winter Conference on Applications of Computer Vision, Workshop CV-AAL - Computer Vision for Active and Assisted Living, Reno, United States, March 2018, pp. 1-9.

    https://hal.inria.fr/hal-01699842
  • 31W. Schueller, V. Loreto, P.-Y. Oudeyer.

    Complexity Reduction in the Negotiation of New Lexical Conventions, in: 40th Annual Conference of the Cognitive Science Society (CogSci 2018), Madison, WI, United States, July 2018.

    https://hal.inria.fr/hal-01891762

Conferences without Proceedings

  • 32H. Caselles-Dupré, L. Annabi, O. Hagen, M. Garcia-Ortiz, D. Filliat.

    Flatland: a Lightweight First-Person 2-D Environment for Reinforcement Learning, in: Workshop on Continual Unsupervised Sensorimotor Learning, ICDL-EpiRob 2018, Tokyo, Japan, September 2018.

    https://hal.archives-ouvertes.fr/hal-01951945
  • 33H. Caselles-Dupré, M. Garcia-Ortiz, D. Filliat.

    Continual State Representation Learning for Reinforcement Learning using Generative Replay, in: Workshop on Continual Learning, NeurIPS 2018 - Thirty-second Conference on Neural Information Processing Systems, Montréal, Canada, December 2018.

    https://hal.archives-ouvertes.fr/hal-01951399
  • 34N. Díaz-Rodríguez, V. Lomonaco, D. Filliat, D. Maltoni.

    Don't forget, there is more than forgetting: new metrics for Continual Learning, in: Workshop on Continual Learning, NeurIPS 2018 (Neural Information Processing Systems, Montreal, Canada, December 2018.

    https://hal.archives-ouvertes.fr/hal-01951488
  • 35I. Huitzil, U. Straccia, N. Díaz-Rodríguez, F. Bobillo.

    Datil: Learning Fuzzy Ontology Datatypes, in: IPMU 2018: 17th Information Processing and Management of Uncertainty in Knowledge-Based Systems Conference, Cádiz, Spain, June 2018.

    https://hal.archives-ouvertes.fr/hal-01951785
  • 36V. Lomonaco, A. Trotta, M. Ziosi, J. De Dios Yáñez Ávila, N. Díaz-Rodríguez.

    Intelligent Drone Swarm for Search and Rescue Operations at Sea, in: Workshop on AI for Good, NeurIPS 2018 (Neural Information Processing Systems), Montreal, Canada, December 2018.

    https://hal.archives-ouvertes.fr/hal-01951515
  • 37J. M. Mendes Filho, E. Lucet, D. Filliat.

    Experimental Validation of a Multirobot Distributed Receding Horizon Motion Planning Approach, in: ICARCV 2018 - 15th International Conference on Control, Automation, Robotics and Vision, Singapour, Singapore, November 2018.

    https://hal.archives-ouvertes.fr/hal-01935322
  • 38A. Péré, S. Forestier, O. Sigaud, P.-Y. Oudeyer.

    Unsupervised Learning of Goal Spaces for Intrinsically Motivated Goal Exploration, in: ICLR2018 - 6th International Conference on Learning Representations, Vancouver, Canada, April 2018.

    https://hal.archives-ouvertes.fr/hal-01891758
  • 39A. Raffin, A. Hill, R. Traoré, T. Lesort, N. Díaz-Rodríguez, D. Filliat.

    S-RL Toolbox: Environments, Datasets and Evaluation Metrics for State Representation Learning, in: NIPS 2018 Deep RL workshop, Montreal, Canada, December 2018.

    https://hal.archives-ouvertes.fr/hal-01931713

Scientific Books (or Scientific Book chapters)

  • 40K. Bollacker, N. Díaz-Rodríguez, X. Li.

    Extending Knowledge Graphs with Subjective Influence Networks for personalized fashion, in: Designing Cognitive Cities, September 2018.

    https://hal.archives-ouvertes.fr/hal-01952205
  • 41N. Díaz-Rodríguez, S. Grönroos, F. Wickström, J. Lilius, H. Eertink, A. Braun, P. Dillen, J. Crowley, J. Alexandersson.

    An Ontology for Wearables Data Interoperability and Ambient Assisted Living Application Development, in: Recent Developments and the New Direction in Soft-Computing Foundations and Applications, L. A. Zadeh, R. R. Yage, S. N. Shahbazova, M. Z. Reforma, V. Kreinovich (editors), Studies in Fuzziness and Soft Computing, Springer, 2018, vol. 361, pp. 559-568, Selected Paper from the 6th World Conference on Soft Computing, May 22-25, 2016, Berkeley, USA. [ DOI : 10.1007/978-3-319-75408-6_43 ]

    https://hal.archives-ouvertes.fr/hal-01951531

Other Publications

  • 42C. Colas, O. Sigaud, P.-Y. Oudeyer.

    How Many Random Seeds? Statistical Power Analysis in Deep Reinforcement Learning Experiments, October 2018, working paper or preprint.

    https://hal.inria.fr/hal-01890154
  • 43C. Colas, O. Sigaud, P.-Y. Oudeyer, P. Fournier.

    CURIOUS: Intrinsically Motivated Multi-Task Multi-Goal Reinforcement Learning, November 2018, working paper or preprint.

    https://hal.archives-ouvertes.fr/hal-01934921
  • 44T. Desprez, S. Noirpoudre, T. Segonds, D. Caselli, D. Roy, P.-Y. Oudeyer.

    Conception et évaluation de kits robotiques pédagogiques: Analyse écologique et expérimentale des utilisations de la robotique à l’école en termes de connaissances et de représentations , March 2018, Journée de l'EDMI, Poster.

    https://hal.archives-ouvertes.fr/hal-01780511
  • 45T. Desprez, S. Noirpoudre, T. Segonds, D. Caselli, D. Roy, P.-Y. Oudeyer.

    Design and Evaluation of Pedagogical Robotic Kits : Ecological and experimental analysis of the uses of robotics in schools in terms of knowledge and representation, January 2018, 1 p, Colloque e-Fran, Territoires éducatifs d'innovation numérique, Poster.

    https://hal.archives-ouvertes.fr/hal-01688310
  • 46C. Mazon, C. Fage, H. Sauzéon.

    Effectiveness and Usability of Technology-based Interventions with children and adolescents with ASD: a systematic review, July 2018, EuroScience Open Forum (ESOF 2018), Poster.

    https://hal.inria.fr/hal-01939765
References in notes
  • 47L. Steels, R. Brooks (editors)

    The Artificial Life Route to Artificial Intelligence: Building Embodied, Situated Agents, L. Erlbaum Associates Inc., Hillsdale, NJ, USA, 1995.
  • 48B. A. Anderson, P. A. Laurent, S. Yantis.

    Value-driven attentional capture, in: Proceedings of the National Academy of Sciences, 2011, vol. 108, no 25, pp. 10367–10371.
  • 49M. Andrychowicz, F. Wolski, A. Ray, J. Schneider, R. Fong, P. Welinder, B. McGrew, J. Tobin, P. Abbeel, W. Zaremba.

    Hindsight experience replay, in: Advances in Neural Information Processing Systems, 2017, pp. 5048–5058.
  • 50B. Argall, S. Chernova, M. Veloso.

    A Survey of Robot Learning from Demonstration, in: Robotics and Autonomous Systems, 2009, vol. 57, no 5, pp. 469–483.
  • 51M. Asada, S. Noda, S. Tawaratsumida, K. Hosoda.

    Purposive Behavior Acquisition On A Real Robot By Vision-Based Reinforcement Learning, in: Machine Learning, 1996, vol. 23, pp. 279-303.
  • 52G. Baldassarre, M. Mirolli.

    Intrinsically Motivated Learning in Natural and Artificial Systems, Springer, 2013.
  • 53A. Baranes, P.-Y. Oudeyer.

    Active Learning of Inverse Models with Intrinsically Motivated Goal Exploration in Robots, in: Robotics and Autonomous Systems, January 2013, vol. 61, no 1, pp. 69-73. [ DOI : 10.1016/j.robot.2012.05.008 ]

    https://hal.inria.fr/hal-00788440
  • 54A. Barto, M. Mirolli, G. Baldassarre.

    Novelty or surprise?, in: Frontiers in psychology, 2013, vol. 4.
  • 55A. Barto, S. Singh, N. Chentanez.

    Intrinsically Motivated Learning of Hierarchical Collections of Skills, in: Proceedings of the 3rd International Conference on Development and Learning (ICDL 2004), Salk Institute, San Diego, 2004.
  • 56D. Berlyne.

    Conflict, Arousal and Curiosity, McGraw-Hill, 1960.
  • 57C. Breazeal.

    Designing sociable robots, The MIT Press, 2004.
  • 58G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, W. Zaremba.

    Openai gym, in: arXiv preprint arXiv:1606.01540, 2016.
  • 59J. Brooke.

    SUS-A quick and dirty usability scale, in: Usability evaluation in industry, 1996, vol. 189, no 194, pp. 4–7.
  • 60R. Brooks, C. Breazeal, R. Irie, C. C. Kemp, B. Scassellati, M. Williamson.

    Alternative essences of intelligence, in: Proceedings of 15th National Conference on Artificial Intelligence (AAAI-98), AAAI Press, 1998, pp. 961–968.
  • 61K. P. Burnham, D. R. Anderson.

    Model selection and multimodel inference: A practical information-theoretic approach, Springer Science & Business Media, 2003.
  • 62A. Cangelosi, G. Metta, G. Sagerer, S. Nolfi, C. Nehaniv, K. Fischer, J. Tani, T. Belpaeme, G. Sandini, F. Nori.

    Integration of action and language knowledge: A roadmap for developmental robotics, in: Autonomous Mental Development, IEEE Transactions on, 2010, vol. 2, no 3, pp. 167–195.
  • 63D. Centola, A. Baronchelli.

    The spontaneous emergence of conventions: An experimental study of cultural evolution, in: Proceedings of the National Academy of Sciences, 2015, vol. 112, no 7, pp. 1989–1994.
  • 64A. Clark.

    Mindware: An Introduction to the Philosophy of Cognitive Science, Oxford University Press, 2001.
  • 65B. Clément, D. Roy, P.-Y. Oudeyer, M. Lopes.

    Multi-Armed Bandits for Intelligent Tutoring Systems, in: Journal of Educational Data Mining (JEDM), June 2015, vol. 7, no 2, pp. 20–48.

    https://hal.inria.fr/hal-00913669
  • 66D. Cohn, Z. Ghahramani, M. Jordan.

    Active learning with statistical models, in: Journal of artificial intelligence research, 1996, vol. 4, pp. 129–145.
  • 67M. Cornudella, P. Van Eecke, R. Van Trijp.

    How Intrinsic Motivation can Speed Up Language Emergence, in: Proceedings of the European Conference on Artificial Life, 2015, pp. 571–578.
  • 68W. Croft, D. Cruse.

    Cognitive Linguistics, Cambridge Textbooks in Linguistics, Cambridge University Press, 2004.
  • 69M. Csikszenthmihalyi.

    Flow-the psychology of optimal experience, Harper Perennial, 1991.
  • 70P. Dayan, W. Belleine.

    Reward, motivation and reinforcement learning, in: Neuron, 2002, vol. 36, pp. 285–298.
  • 71E. Deci, R. Ryan.

    Intrinsic Motivation and Self-Determination in Human Behavior, Plenum Press, 1985.
  • 72P. Dhariwal, C. Hesse, O. Klimov, A. Nichol, M. Plappert, A. Radford, J. Schulman, S. Sidor, Y. Wu, P. Zhokhov.

    OpenAI Baselines, GitHub, 2017.

    https://github.com/openai/baselines
  • 73J. Elman.

    Learning and development in neural networks: The importance of starting small, in: Cognition, 1993, vol. 48, pp. 71–99.
  • 74S. B. Flagel, H. Akil, T. E. Robinson.

    Individual differences in the attribution of incentive salience to reward-related cues: Implications for addiction, in: Neuropharmacology, 2009, vol. 56, pp. 139–148.
  • 75S. Forestier, Y. Mollard, D. Caselli, P.-Y. Oudeyer.

    Autonomous exploration, active learning and human guidance with open-source Poppy humanoid robot platform and Explauto library, in: The Thirtieth Annual Conference on Neural Information Processing Systems (NIPS 2016), 2016.
  • 76S. Forestier, Y. Mollard, P.-Y. Oudeyer.

    Intrinsically Motivated Goal Exploration Processes with Automatic Curriculum Learning, November 2017, working paper or preprint.

    https://hal.archives-ouvertes.fr/hal-01651233
  • 77S. Forestier, Y. Mollard, P.-Y. Oudeyer.

    Intrinsically motivated goal exploration processes with automatic curriculum learning, in: arXiv preprint arXiv:1708.02190, 2017.
  • 78S. Forestier, P.-Y. Oudeyer.

    A Unified Model of Speech and Tool Use Early Development, in: 39th Annual Conference of the Cognitive Science Society (CogSci 2017), London, United Kingdom, Proceedings of the 39th Annual Conference of the Cognitive Science Society, July 2017.

    https://hal.archives-ouvertes.fr/hal-01583301
  • 79M. C. Frank, N. D. Goodman.

    Predicting pragmatic reasoning in language games, in: Science, 2012, vol. 336, no 6084, pp. 998–998.
  • 80B. Galantucci, S. Garrod.

    Experimental semiotics: a review, in: Frontiers in human neuroscience, 2011, vol. 5, 11 p.
  • 81J. Gottlieb, P.-Y. Oudeyer, M. Lopes, A. Baranes.

    Information-seeking, curiosity, and attention: computational and neural mechanisms, in: Trends in Cognitive Sciences, November 2013, vol. 17, no 11, pp. 585-93. [ DOI : 10.1016/j.tics.2013.09.001 ]

    https://hal.inria.fr/hal-00913646
  • 82J. Gottlieb, P.-Y. Oudeyer, M. Lopes, A. Baranes.

    Information-seeking, curiosity, and attention: computational and neural mechanisms, in: Trends in cognitive sciences, 2013, vol. 17, no 11, pp. 585–593.
  • 83T. Guitard, D. Roy, P.-Y. Oudeyer, M. Chevalier.

    IniRobot, January 2016, Des activités robotiques pour l'initiation aux sciences du numérique.

    https://hal.inria.fr/hal-01412928
  • 84S. Harnad.

    The symbol grounding problem, in: Physica D, 1990, vol. 40, pp. 335–346.
  • 85M. Hasenjager, H. Ritter.

    Active learning in neural networks, Physica-Verlag GmbH, Heidelberg, Germany, Germany, 2002, pp. 137–169.
  • 86J. Haugeland.

    Artificial Intelligence: the very idea, The MIT Press, Cambridge, MA, USA, 1985.
  • 87P. Henderson, R. Islam, P. Bachman, J. Pineau, D. Precup, D. Meger.

    Deep reinforcement learning that matters, in: arXiv preprint arXiv:1709.06560, 2017.
  • 88J.-C. Horvitz.

    Mesolimbocortical and nigrostriatal dopamine responses to salient non-reward events, in: Neuroscience, 2000, vol. 96, no 4, pp. 651-656.
  • 89X. Huang, J. Weng.

    Novelty and reinforcement learning in the value system of developmental robots, in: Proceedings of the 2nd international workshop on Epigenetic Robotics : Modeling cognitive development in robotic systems, C. Prince, Y. Demiris, Y. Marom, H. Kozima, C. Balkenius (editors), Lund University Cognitive Studies 94, 2002, pp. 47–55.
  • 90S. Ivaldi, N. Lyubova, D. Gérardeaux-Viret, A. Droniou, S. Anzalone, M. Chetouani, D. Filliat, O. Sigaud.

    Perception and human interaction for developmental learning of objects and affordances, in: Proc. of the 12th IEEE-RAS International Conference on Humanoid Robots - HUMANOIDS, Japan, 2012, forthcoming.

    http://hal.inria.fr/hal-00755297
  • 91M. Johnson.

    Developmental Cognitive Neuroscience, 2nd, Blackwell publishing, 2005.
  • 92F. Kaplan, P.-Y. Oudeyer, B. Bergen.

    Computational models in the debate over language learnability, in: Infant and Child Development, 2008, vol. 17, no 1, pp. 55–80.
  • 93C. Kidd, B. Hayden.

    The psychology and neuroscience of curiosity, in: Neuron (in press), 2015.
  • 94S. Kirby, T. Griffiths, K. Smith.

    Iterated learning and the evolution of language, in: Current opinion in neurobiology, 2014, vol. 28, pp. 108–114.
  • 95W. B. Knox, P. Stone.

    Combining manual feedback with subsequent MDP reward signals for reinforcement learning, in: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS'10), 2010, pp. 5–12.
  • 96C. Lallemand, V. Koenig, G. Gronier, R. Martin.

    Création et validation d’une version française du questionnaire AttrakDiff pour l’évaluation de l’expérience utilisateur des systèmes interactifs, in: Revue Européenne de Psychologie Appliquée/European Review of Applied Psychology, 2015, vol. 65, no 5, pp. 239–252.
  • 97T. P. Lillicrap, J. J. Hunt, A. Pritzel, N. Heess, T. Erez, Y. Tassa, D. Silver, D. Wierstra.

    Continuous control with deep reinforcement learning, in: arXiv preprint arXiv:1509.02971, 2015.
  • 98G. Loewenstein.

    The psychology of curiosity: A review and reinterpretation, in: Psychological bulletin, 1994, vol. 116, no 1, 75 p.
  • 99M. Lopes, T. Cederborg, P.-Y. Oudeyer.

    Simultaneous Acquisition of Task and Feedback Models, in: Development and Learning (ICDL), 2011 IEEE International Conference on, Germany, 2011, pp. 1 - 7. [ DOI : 10.1109/DEVLRN.2011.6037359 ]

    http://hal.inria.fr/hal-00636166/en
  • 100M. Lopes, T. Lang, M. Toussaint, P.-Y. Oudeyer.

    Exploration in Model-based Reinforcement Learning by Empirically Estimating Learning Progress, in: Neural Information Processing Systems (NIPS), Lake Tahoe, United States, December 2012.

    http://hal.inria.fr/hal-00755248
  • 101M. Lopes, L. Montesano.

    Active Learning for Autonomous Intelligent Agents: Exploration, Curiosity, and Interaction, in: CoRR, 2014, vol. abs/1403.1, 40 p.
  • 102V. Loreto, A. Baronchelli, A. Mukherjee, A. Puglisi, F. Tria.

    Statistical physics of language dynamics, in: Journal of Statistical Mechanics: Theory and Experiment, 2011, vol. 2011, no 04, P04006 p.
  • 103M. Lungarella, G. Metta, R. Pfeifer, G. Sandini.

    Developmental Robotics: A Survey, in: Connection Science, 2003, vol. 15, no 4, pp. 151-190.
  • 104N. Lyubova, D. Filliat.

    Developmental Approach for Interactive Object Discovery, in: Neural Networks (IJCNN), The 2012 International Joint Conference on, Australia, June 2012, pp. 1-7. [ DOI : 10.1109/IJCNN.2012.6252606 ]

    https://hal.archives-ouvertes.fr/hal-00755298
  • 105D. J. Mankowitz, A. Žídek, A. Barreto, D. Horgan, M. Hessel, J. Quan, J. Oh, H. van Hasselt, D. Silver, T. Schaul.

    Unicorn: Continual Learning with a Universal, Off-policy Agent, in: arXiv preprint arXiv:1802.08294, 2018.
  • 106J. Marshall, D. Blank, L. Meeden.

    An Emergent Framework for Self-Motivation in Developmental Robotics, in: Proceedings of the 3rd International Conference on Development and Learning (ICDL 2004), Salk Institute, San Diego, 2004.
  • 107M. Mason, M. Lopes.

    Robot Self-Initiative and Personalization by Learning through Repeated Interactions, in: 6th ACM/IEEE International Conference on Human-Robot, Switzerland, 2011. [ DOI : 10.1145/1957656.1957814 ]

    http://hal.inria.fr/hal-00636164/en
  • 108M. McCloskey, N. J. Cohen.

    Catastrophic interference in connectionist networks: The sequential learning problem, in: Psychology of learning and motivation, Elsevier, 1989, vol. 24, pp. 109–165.
  • 109D. McFadden.

    Conditional logit analysis of qualitative choice behavior, in: Frintiers in Econometrics, New York, P. Zarembka (editor), Academic Press, 1973, chap. 4, pp. 105-142.
  • 110P. Miller.

    Theories of developmental psychology, 4th, New York: Worth, 2001.
  • 111M. Mirolli, G. Baldassarre.

    Functions and mechanisms of intrinsic motivations, in: Intrinsically Motivated Learning in Natural and Artificial Systems, Springer, 2013, pp. 49–72.
  • 112C. Moulin-Frier, S. M. Nguyen, P.-Y. Oudeyer.

    Self-Organization of Early Vocal Development in Infants and Machines: The Role of Intrinsic Motivation, in: Frontiers in Psychology, 2013, vol. 4, no 1006. [ DOI : 10.3389/fpsyg.2013.01006 ]

    https://hal.inria.fr/hal-00927940
  • 113C. Moulin-Frier, P.-Y. Oudeyer.

    Exploration strategies in developmental robotics: a unified probabilistic framework, in: ICDL-Epirob - International Conference on Development and Learning, Epirob, Osaka, Japan, August 2013.

    https://hal.inria.fr/hal-00860641
  • 114C. Moulin-Frier, P. Rouanet, P.-Y. Oudeyer.

    Explauto: an open-source Python library to study autonomous exploration in developmental robotics, in: ICDL-Epirob - International Conference on Development and Learning, Epirob, Genoa, Italy, October 2014.

    https://hal.inria.fr/hal-01061708
  • 115S. M. Nguyen, A. Baranes, P.-Y. Oudeyer.

    Bootstrapping Intrinsically Motivated Learning with Human Demonstrations, in: IEEE International Conference on Development and Learning, Frankfurt, Germany, 2011.

    http://hal.inria.fr/hal-00645986/en
  • 116S. M. Nguyen, A. Baranes, P.-Y. Oudeyer.

    Constraining the Size Growth of the Task Space with Socially Guided Intrinsic Motivation using Demonstrations., in: IJCAI Workshop on Agents Learning Interactively from Human Teachers (ALIHT), Barcelona, Spain, 2011.

    http://hal.inria.fr/hal-00645995/en
  • 117S. M. Nguyen, P.-Y. Oudeyer.

    Active Choice of Teachers, Learning Strategies and Goals for a Socially Guided Intrinsic Motivation Learner, in: Paladyn, September 2012, vol. 3, no 3, pp. 136-146. [ DOI : 10.2478/s13230-013-0110-z ]

    https://hal.inria.fr/hal-00936932
  • 118S. M. Nguyen, P.-Y. Oudeyer.

    Socially Guided Intrinsic Motivation for Robot Learning of Motor Skills, in: Autonomous Robots, March 2014, vol. 36, no 3, pp. 273-294. [ DOI : 10.1007/s10514-013-9339-y ]

    https://hal.inria.fr/hal-00936938
  • 119S. Noirpoudre, D. Roy, M. Demangeat, T. Desprez, T. Segonds, P. Rouanet, D. Caselli, N. Rabault, M. Lapeyre, P.-Y. Oudeyer.

    Livret pédagogique : Apprendre à programmer Poppy Ergo Jr en Snap!, June 2016, 50 p, Un livret composé d'activités pédagogiques pour apprendre les bases de la programmation (programmation séquentielles, boucles, conditions, variables etc.) et des idées de défis et de projets pour appliquer les connaissances.

    https://hal.inria.fr/hal-01384649
  • 120S. Noirpoudre, D. Roy, T. Desprez, T. Segonds, D. Caselli, P.-Y. Oudeyer.

    Poppy Education: un dispositif robotique open source pour l'enseignement de l'informatique et de la robotique, in: EIAH 2017-Environnements Informatiques pour l'Apprentissage Humain, 2017, 8 p.
  • 121P.-Y. Oudeyer, F. Delaunay.

    Developmental exploration in the cultural evolution of lexical conventions, in: 8th International Conference on Epigenetic Robotics: Modeling Cognitive Development in Robotic Systems, Brighton, United Kingdom, 2008.

    https://hal.inria.fr/inria-00420303
  • 122P.-Y. Oudeyer, F. Kaplan, V. Hafner.

    Intrinsic Motivation for Autonomous Mental Development, in: IEEE Transactions on Evolutionary Computation, January 2007, vol. 11, no 2, pp. 265-286. [ DOI : 10.1109/TEVC.2006.890271 ]

    https://hal.inria.fr/hal-00793610
  • 123P.-Y. Oudeyer, F. Kaplan, V. Hafner.

    Intrinsic Motivation Systems for Autonomous Mental Development, in: IEEE Transactions on Evolutionary Computation, 2007, vol. 11, no 1, pp. 265–286.

    http://www.pyoudeyer.com/ims.pdf
  • 124P.-Y. Oudeyer, F. Kaplan.

    Intelligent adaptive curiosity: a source of self-development, in: Proceedings of the 4th International Workshop on Epigenetic Robotics, L. Berthouze, H. Kozima, C. Prince, G. Sandini, G. Stojanov, G. Metta, C. Balkenius (editors), Lund University Cognitive Studies, 2004, vol. 117, pp. 127–130.
  • 125P.-Y. Oudeyer, F. Kaplan.

    What is intrinsic motivation? A typology of computational approaches, in: Frontiers in Neurorobotics, 2007, vol. 1, no 1.
  • 126P.-Y. Oudeyer.

    Sur les interactions entre la robotique et les sciences de l'esprit et du comportement, in: Informatique et Sciences Cognitives : influences ou confluences ?, C. Garbay, D. Kaiser (editors), Presses Universitaires de France, 2009.

    http://hal.inria.fr/inria-00420309/en/
  • 127P.-Y. Oudeyer.

    Developmental Learning of Sensorimotor Models for Control in Robotics, in: SIAM News, September 2014, vol. 47, no 7.

    https://hal.inria.fr/hal-01061633
  • 128P.-Y. Oudeyer, L. Smith.

    How Evolution May Work Through Curiosity-Driven Developmental Process, in: Topics in cognitive science, February 2016, vol. 8. [ DOI : 10.1111/tops.12196 ]

    https://hal.inria.fr/hal-01404334
  • 129P.-Y. Oudeyer.

    L'auto-organisation dans l'évolution de la parole, in: Parole et Musique: Aux origines du dialogue humain, Colloque annuel du Collège de France, S. Dehaene, C. Petit (editors), Odile Jacob, 2009, pp. 83-112.

    http://hal.inria.fr/inria-00446908/en/
  • 130M. Pelz, S. T. Piantadosi, C. Kidd.

    The dynamics of idealized attention in complex learning environments, in: IEEE International Conference on Development and Learning and on Epigenetic Robotics, 2015.
  • 131M. Plappert, R. Houthooft, P. Dhariwal, S. Sidor, R. Y. Chen, X. Chen, T. Asfour, P. Abbeel, M. Andrychowicz.

    Parameter space noise for exploration, in: arXiv preprint arXiv:1706.01905, 2017.
  • 132L. J. Points, J. W. Taylor, J. Grizou, K. Donkers, L. Cronin.

    Artificial intelligence exploration of unstable protocells leads to predictable properties and discovery of collective behavior, in: Proceedings of the National Academy of Sciences, 2018, 201711089 p.
  • 133A. Revel, J. Nadel.

    How to build an imitator?, in: Imitation and Social Learning in Robots, Humans and Animals: Behavioural, Social and Communicative Dimensions, K. Dautenhahn, C. Nehaniv (editors), Cambridge University Press, 2004.
  • 134E. F. Risko, N. C. Anderson, S. Lanthier, A. Kingstone.

    Curious eyes: Individual differences in personality predict eye movement behavior in scene-viewing, in: Cognition, 2012, vol. 122, no 1, pp. 86–90.
  • 135V. G. Santucci, G. Baldassarre, M. Mirolli.

    Which is the best intrinsic motivation signal for learning multiple skills?, in: Frontiers in neurorobotics, 2013, vol. 7.
  • 136T. Schatz, P.-Y. Oudeyer.

    Learning motor dependent Crutchfield's information distance to anticipate changes in the topology of sensory body maps, in: IEEE International Conference on Learning and Development, Chine Shangai, 2009.

    http://hal.inria.fr/inria-00420186/en/
  • 137T. Schaul, D. Horgan, K. Gregor, D. Silver.

    Universal value function approximators, in: International Conference on Machine Learning, 2015, pp. 1312–1320.
  • 138M. Schembri, M. Mirolli, G. Baldassarre.

    Evolving internal reinforcers for an intrinsically motivated reinforcement-learning robot, in: IEEE 6th International Conference on Development and Learning, 2007. ICDL 2007., July 2007, pp. 282-287.

    http://dx.doi.org/10.1109/DEVLRN.2007.4354052
  • 139J. Schmidhuber.

    Curious Model-Building Control Systems, in: Proceedings of the International Joint Conference on Neural Networks, Singapore, IEEE press, 1991, vol. 2, pp. 1458–1463.
  • 140W. Schueller, P.-Y. Oudeyer.

    Active Learning Strategies and Active Control of Complexity Growth in Naming Games, in: the 5th International Conference on Development and Learning and on Epigenetic Robotics, Providence, RI, United States, August 2015.

    https://hal.inria.fr/hal-01202654
  • 141W. Schueller, P.-Y. Oudeyer.

    Active Control Of Complexity Growth In Naming Games: Hearer's Choice, in: The Evolution of Language: Proceedings of the 11th International Conference (EVOLANGX11), 2016.
  • 142W. Schultz, P. Dayan, P. Montague.

    A neural substrate of prediction and reward, in: Science, 1997, vol. 275, pp. 1593-1599.
  • 143L. Steels, F. Kaplan, A. McIntyre, J. Van Looveren.

    Crucial factors in the origins of word-meaning, in: The transition to language, 2002, vol. 12, pp. 252–271.
  • 144L. Steels.

    Language games for autonomous robots, in: Intelligent Systems, IEEE, 2001, vol. 16, no 5, pp. 16–22.
  • 145L. Steels.

    The Autotelic Principle, in: Science, 2004, vol. 3139, pp. 1–16.
  • 146E. Sumner, E. DeAngelis, M. Hyatt, N. Goodman, C. Kidd.

    Toddlers Always Get the Last Word: Recency biases in early verbal behavior, in: Proceedings of the 37th Annual Meeting of the Cognitive Science Society, 2015.
  • 147E. Thelen, L. B. Smith.

    A dynamic systems approach to the development of cognition and action, MIT Press, Cambridge, MA, 1994.
  • 148A. L. Thomaz, C. Breazeal.

    Teachable robots: Understanding human teaching behavior to build more effective robot learners, in: Artificial Intelligence Journal, 2008, vol. 172, pp. 716-737.
  • 149A. Turing.

    Computing machinery and intelligence, in: Mind, 1950, vol. 59, pp. 433-460.
  • 150M. R. Uncapher, M. K. Thieu, A. D. Wagner.

    Media multitasking and memory: Differences in working memory and long-term memory, in: Psychonomic bulletin & review, 2015, pp. 1–8.
  • 151F. Varela, E. Thompson, E. Rosch.

    The embodied mind : Cognitive science and human experience, MIT Press, Cambridge, MA, 1991.
  • 152P. Wellens.

    Adaptive Strategies in the Emergence of Lexical Systems, Vrije Universiteit Brussel, Brussels, 2012.

    http://ai.vub.ac.be/publications/918
  • 153J. Weng, J. McClelland, A. Pentland, O. Sporns, I. Stockman, M. Sur, E. Thelen.

    Autonomous mental development by robots and animals, in: Science, 2001, vol. 291, pp. 599-600.