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, n^o 1, pp. 69-73. [ DOI : 10.1016/j.robot.2012.05.008 ]
  
  https://hal.inria.fr/hal-00788440
• 2H. Caselles-Dupré, M. Garcia-Ortiz, D. Filliat.
  
  Symmetry-Based Disentangled Representation Learning requires Interaction with Environments, in: NeurIPS 2019, Vancouver, Canada, December 2019.
  
  https://hal.archives-ouvertes.fr/hal-02379399
• 3C. Colas, P. Fournier, O. Sigaud, M. Chetouani, P.-Y. Oudeyer.
  
  CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning, in: International Conference on Machine Learning, Long Beach, France, June 2019.
  
  https://hal.archives-ouvertes.fr/hal-01934921
• 4C. 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
• 5C. 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
• 6S. 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
• 7S. 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
• 8J. Gottlieb, P.-Y. Oudeyer.
  
  Towards a neuroscience of active sampling and curiosity, in: Nature Reviews Neuroscience, December 2018, vol. 19, n^o 12, pp. 758-770.
  
  https://hal.inria.fr/hal-01965608
• 9A. 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
• 10T. 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
• 11M. E. Meade, J. G. Meade, H. Sauzéon, M. A. Fernandes.
  
  Active Navigation in Virtual Environments Benefits Spatial Memory in Older Adults, in: Brain Sciences, 2019, vol. 9. [ DOI : 10.3390/brainsci9030047 ]
  
  https://hal.inria.fr/hal-02049031
• 12C. Moulin-Frier, J. Brochard, F. Stulp, P.-Y. Oudeyer.
  
  Emergent Jaw Predominance in Vocal Development through Stochastic Optimization, in: IEEE Transactions on Cognitive and Developmental Systems, 2017, n^o 99, pp. 1-12. [ DOI : 10.1109/TCDS.2017.2704912 ]
  
  https://hal.inria.fr/hal-01578075
• 13R. Portelas, C. Colas, K. Hofmann, P.-Y. Oudeyer.
  
  Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments, in: CoRL 2019 - Conference on Robot Learning, Osaka, Japan, October 2019, https://arxiv.org/abs/1910.07224.
  
  https://hal.archives-ouvertes.fr/hal-02370165
• 14A. 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
• 15C. Reinke, M. Etcheverry, P.-Y. Oudeyer.
  
  Intrinsically Motivated Discovery of Diverse Patterns in Self-Organizing Systems, in: International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, April 2020, Source code and videos athttps://automated-discovery.github.io/.
  
  https://hal.inria.fr/hal-02370003

Publications of the year

Doctoral Dissertations and Habilitation Theses

• 16C. Mazon.
  
  Digital technologies for the school inclusion of children with ASD in middle school : from individual to ecosystemic approaches in supporting the individuals and their caregivers, Université de Bordeaux, November 2019.
  
  https://hal.inria.fr/tel-02398226

Articles in International Peer-Reviewed Journals

• 17L. Caroux, C. Consel, M. Merciol, H. Sauzéon.
  
  Acceptability of notifications delivered to older adults by technology-based assisted living services, in: Universal Access in the Information Society, July 2019. [ DOI : 10.1007/s10209-019-00665-y ]
  
  https://hal.inria.fr/hal-02179319
• 18P.-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. [ DOI : 10.1016/j.compedu.2018.12.004 ]
  
  https://hal.archives-ouvertes.fr/hal-01954983
• 19C. 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. [ DOI : 10.1016/j.robot.2018.11.012 ]
  
  https://hal.archives-ouvertes.fr/hal-01959882
• 20L. Dupuy, B. N’Kaoua, P. Dehail, H. Sauzéon.
  
  Role of cognitive resources on everyday functioning among oldest-old physically frail, in: Aging Clinical and Experimental Research, October 2019. [ DOI : 10.1007/s40520-019-01384-3 ]
  
  https://hal.inria.fr/hal-02353741
• 21C. Fage, C. Consel, K. Etchegoyhen, A. Amestoy, M. Bouvard, C. Mazon, H. Sauzéon.
  
  An emotion regulation app for school inclusion of children with ASD: Design principles and evaluation, in: Computers and Education, April 2019, vol. 131, pp. 1-21. [ DOI : 10.1016/j.compedu.2018.12.003 ]
  
  https://hal.inria.fr/hal-02124850
• 22P. Fournier, C. Colas, M. Chetouani, O. Sigaud.
  
  CLIC: Curriculum Learning and Imitation for object Control in non-rewarding environments, in: IEEE Transactions on Cognitive and Developmental Systems, 2019, 1 p, forthcoming. [ DOI : 10.1109/TCDS.2019.2933371 ]
  
  https://hal.archives-ouvertes.fr/hal-02370859
• 23T. Lesort, V. Lomonaco, A. Stoian, D. Maltoni, D. Filliat, N. Díaz-Rodríguez.
  
  Continual Learning for Robotics: Definition, Framework, Learning Strategies, Opportunities and Challenges, in: Information Fusion, December 2019, https://arxiv.org/abs/1907.00182. [ DOI : 10.1016/j.inffus.2019.12.004 ]
  
  https://hal.archives-ouvertes.fr/hal-02381343
• 24C. Mazon, C. Fage, C. Consel, A. Amestoy, I. Hesling, M. Bouvard, K. Etchegoyhen, H. Sauzéon.
  
  Cognitive Mediators of School-Related Socio- Adaptive Behaviors in ASD and Intellectual Disability Pre-and Adolescents: A Pilot-Study in French Special Education Classrooms, in: Brain Sciences, 2019, vol. 9. [ DOI : 10.3390/brainsci9120334 ]
  
  https://hal.inria.fr/hal-02374929
• 25M. E. Meade, J. G. Meade, H. Sauzéon, M. A. Fernandes.
  
  Active Navigation in Virtual Environments Benefits Spatial Memory in Older Adults, in: Brain Sciences, 2019, vol. 9. [ DOI : 10.3390/brainsci9030047 ]
  
  https://hal.inria.fr/hal-02049031
• 26S. Mick, M. Lapeyre, P. Rouanet, C. Halgand, J. Benois-Pineau, F. Paclet, D. Cattaert, P.-Y. Oudeyer, A. De Rugy.
  
  Reachy, a 3D-Printed Human-Like Robotic Arm as a Testbed for Human-Robot Control Strategies, in: Frontiers in Neurorobotics, August 2019, vol. 13. [ DOI : 10.3389/fnbot.2019.00065 ]
  
  https://hal.archives-ouvertes.fr/hal-02326321

Articles in National Peer-Reviewed Journals

• 27C. Atlan, J.-P. Archambault, O. Banus, F. Bardeau, A. Blandeau, A. Cois, M. Courbin-Coulaud, G. Giraudon, S.-C. Lefèvre, V. Letard, B. Masse, F. Masseglia, B. Ninassi, S. De Quatrebarbes, M. Romero, D. Roy, T. Viéville.
  
  Apprentissage de la pensée informatique : de la formation des enseignant·e·s à la formation de tou·te·s les citoyen·ne·s, in: Revue de l'EPI (Enseignement Public et Informatique), June 2019, https://arxiv.org/abs/1906.00647.
  
  https://hal.inria.fr/hal-02145478

Invited Conferences

• 28H. Sauzéon.
  
  Assistances numériques pour la cognition quotidienne à tous les âges de la vie : Rôle de la motivation intrinsèque, in: Colloque - Augmentation de l'humain : vers des systèmes cognitivement augmentés (chaire industrielle « Systèmes Technologiques pour l'Augmentation de l'Humain »), Bordeaux, France, March 2019.
  
  https://hal.inria.fr/hal-02375475

International Conferences with Proceedings

• 29J. Ceha, N. Chhibber, J. Goh, C. Mcdonald, P.-Y. Oudeyer, D. Kulić, E. Law.
  
  Expression of Curiosity in Social Robots: Design, Perception, and Effects on Behaviour, in: CHI 2019 - The ACM CHI Conference on Human Factors in Computing Systems, Glasgow, United Kingdom, May 2019.
  
  https://hal.inria.fr/hal-02371252
• 30C. Reinke, M. Etcheverry, P.-Y. Oudeyer.
  
  Intrinsically Motivated Discovery of Diverse Patterns in Self-Organizing Systems, in: International Conference on Learning Representations (ICLR), Addis Ababa, Ethiopia, April 2020, https://arxiv.org/abs/1908.06663 - Source code and videos athttps://automated-discovery.github.io/.
  
  https://hal.inria.fr/hal-02370003

Conferences without Proceedings

• 31C. Atlan, J.-P. Archambault, O. Banus, F. Bardeau, A. Blandeau, A. Cois, M. Courbin-Coulaud, G. Giraudon, S.-C. Lefèvre, V. Letard, B. Masse, F. Masseglia, B. Ninassi, S. De Quatrebarbes, M. Romero, D. Roy, T. Viéville.
  
  Apprentissage de la pensée informatique : de la formation des enseignant·e·s à la formation de tou·te·s les citoyen·ne·s, in: EIAH'19 Wokshop - Apprentissage de la pensée informatique de la maternelle à l'Université : retours d'expériences et passage à l'échelle, Paris, France, June 2019.
  
  https://hal.inria.fr/hal-02145480
• 32H. Caselles-Dupré, M. Garcia-Ortiz, D. Filliat.
  
  Symmetry-Based Disentangled Representation Learning requires Interaction with Environments, in: NeurIPS 2019 6 Neural Information Processing Conference, Vancouver, Canada, December 2019, https://arxiv.org/abs/1904.00243.
  
  https://hal.archives-ouvertes.fr/hal-02379399
• 33C. Colas, P. Fournier, O. Sigaud, M. Chetouani, P.-Y. Oudeyer.
  
  CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning, in: ICML 2019 - Thirty-sixth International Conference on Machine Learning, Long Beach, United States, June 2019.
  
  https://hal.archives-ouvertes.fr/hal-01934921
• 34C. Colas, O. Sigaud, P.-Y. Oudeyer.
  
  A Hitchhiker's Guide to Statistical Comparisons of Reinforcement Learning Algorithms, in: ICLR Worskhop on Reproducibility, Nouvelle-Orléans, United States, May 2019, https://arxiv.org/abs/1904.06979.
  
  https://hal.archives-ouvertes.fr/hal-02369859
• 35N. Lair, C. Colas, R. Portelas, J.-M. Dussoux, P. Dominey, P.-Y. Oudeyer.
  
  Language Grounding through Social Interactions and Curiosity-Driven Multi-Goal Learning, in: NeurIPS Workshop on Visually Grounded Interaction and Language, Vancouver, Canada, December 2019, https://arxiv.org/abs/1911.03219.
  
  https://hal.archives-ouvertes.fr/hal-02369866
• 36T. Lesort, H. Caselles-Dupré, M. Garcia-Ortiz, J.-F. Goudou, D. Filliat.
  
  Generative Models from the perspective of Continual Learning, in: IJCNN - International Joint Conference on Neural Networks, Budapest, Hungary, July 2019.
  
  https://hal.archives-ouvertes.fr/hal-01951954
• 37T. Lesort, M. Seurin, X. Li, N. Díaz-Rodríguez, D. Filliat.
  
  Deep unsupervised state representation learning with robotic priors: a robustness analysis, in: IJCNN 2019 - International Joint Conference on Neural Networks, Budapest, Hungary, IEEE, July 2019, pp. 1-8. [ DOI : 10.1109/IJCNN.2019.8852042 ]
  
  https://hal.archives-ouvertes.fr/hal-02381375
• 38R. Portelas, C. Colas, K. Hofmann, P.-Y. Oudeyer.
  
  Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments, in: CoRL 2019 - Conference on Robot Learning, Osaka, Japan, October 2019, https://arxiv.org/abs/1910.07224.
  
  https://hal.archives-ouvertes.fr/hal-02370165
• 39A. Raffin, A. Hill, R. Traoré, T. Lesort, N. Díaz-Rodríguez, D. Filliat.
  
  Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics, in: SPiRL 2019 : Workshop on Structure and Priors in Reinforcement Learning at ICLR 2019, Nouvelle Orléans, United States, May 2019, https://arxiv.org/abs/1901.08651 - Github repo: https://github.com/araffin/srl-zoo Documentation: https://srl-zoo.readthedocs.io/en/latest/, As part of SRL-Toolbox: https://s-rl-toolbox.readthedocs.io/en/latest/. Accepted to the Workshop on Structure & Priors in Reinforcement Learning at ICLR 2019.
  
  https://hal.archives-ouvertes.fr/hal-02285831
• 40R. Traoré, H. Caselles-Dupré, T. Lesort, T. Sun, G. Cai, D. Filliat, N. Díaz-Rodríguez.
  
  DISCORL: Continual reinforcement learning via policy distillation : A preprint, in: NeurIPS workshop on Deep Reinforcement Learning, Vancouver, Canada, December 2019.
  
  https://hal.archives-ouvertes.fr/hal-02381494
• 41R. Traoré, H. Caselles-Dupré, T. Lesort, T. Sun, N. Díaz-Rodríguez, D. Filliat.
  
  Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer, in: ICML Workshop on “Multi-Task and Lifelong Reinforcement Learning”, Long Beach, United States, June 2019, https://arxiv.org/abs/1906.04452 - accepted to the Workshop on Multi-Task and Lifelong Reinforcement Learning, ICML 2019.
  
  https://hal.archives-ouvertes.fr/hal-02285839

Scientific Books (or Scientific Book chapters)

• 42P.-A. Cinquin, P. Guitton, H. Sauzéon.
  
  Accessibilité numérique des systèmes d'enseignement en ligne pour des personnes en situation de handicap d'origine cognitif, in: Handicaps et recherches - Regards pluridiciplinaires, E. Dugas (editor), Editions CNRS, 2019.
  
  https://hal.inria.fr/hal-02433430
• 43P. Karvinen, N. Díaz-Rodríguez, S. Grönroos, J. Lilius.
  
  RDF Stores for Enhanced Living Environments: An Overview, in: Enhanced Living Environments: Algorithms, Architectures, Platforms, and Systems, I. Ganchev, N. M. Garcia, C. Dobre, C. X. Mavromoustakis, R. Goleva (editors), Springer, January 2019, pp. 19-52. [ DOI : 10.1007/978-3-030-10752-9_2 ]
  
  https://hal.archives-ouvertes.fr/hal-02381354
• 44P.-Y. Oudeyer, G. Kachergis, W. Schueller.
  
  Computational and Robotic Models of Early Language Development: A Review, in: International Handbook of Language Acquisition, May 2019.
  
  https://hal.inria.fr/hal-02371233
• 45H. Sauzéon, L. Dupuy, C. Fage, C. Mazon.
  
  Assistances numériques pour la cognition quotidienne à tous les âges de la vie, in: Handicap et Recherches : Regards pluridisciplinaires, CNRS Editions, May 2019.
  
  https://hal.inria.fr/hal-02375456

Other Publications

• 46A. B. Arrieta, N. Díaz-Rodríguez, J. Del Ser, A. Bennetot, S. Tabik, A. Barbado, S. García, S. Gil-López, D. Molina, R. Benjamins, R. Chatila, F. Herrera.
  
  Explainable Artificial Intelligence (XAI): Concepts, Taxonomies, Opportunities and Challenges toward Responsible AI, November 2019, https://arxiv.org/abs/1910.10045 - 67 pages, 13 figures, under review in the Information Fusion journal. [ DOI : 10.10045 ]
  
  https://hal.archives-ouvertes.fr/hal-02381211
• 47A. Bennetot, J.-L. Laurent, R. Chatila, N. Díaz-Rodríguez.
  
  Towards Explainable Neural-Symbolic Visual Reasoning, November 2019, https://arxiv.org/abs/1909.09065 - Accepted at IJCAI19 Neural-Symbolic Learning and Reasoning Workshop (https://sites.google.com/view/nesy2019/home).
  
  https://hal.archives-ouvertes.fr/hal-02379596
• 48T. Gilliard, T. Desprez, P.-Y. Oudeyer.
  
  Conception and testing of modular robotic kits based on Poppy Ergo Jr for educational purposes, March 2019, Colloque des Jeunes Chercheurs en Sciences Cognitives (CJC2019), Poster.
  
  https://hal.inria.fr/hal-02154848

References in notes

• 49L. Steels, R. Brooks (editors)
  
  The Artificial Life Route to Artificial Intelligence: Building Embodied, Situated Agents, L. Erlbaum Associates Inc., Hillsdale, NJ, USA, 1995.
• 50B. A. Anderson, P. A. Laurent, S. Yantis.
  
  Value-driven attentional capture, in: Proceedings of the National Academy of Sciences, 2011, vol. 108, n^o 25, pp. 10367–10371.
• 51M. Andrychowicz, F. Wolski, A. Ray, J. Schneider, R. Fong, P. Welinder, B. McGrew, J. Tobin, O. P. Abbeel, W. Zaremba.
  
  Hindsight experience replay, in: Advances in Neural Information Processing Systems, 2017, pp. 5048–5058.
• 52B. Argall, S. Chernova, M. Veloso.
  
  A Survey of Robot Learning from Demonstration, in: Robotics and Autonomous Systems, 2009, vol. 57, n^o 5, pp. 469–483.
• 53M. 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.
• 54G. Baldassarre, M. Mirolli.
  
  Intrinsically Motivated Learning in Natural and Artificial Systems, Springer, 2013.
• 55A. 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, n^o 1, pp. 69-73. [ DOI : 10.1016/j.robot.2012.05.008 ]
  
  https://hal.inria.fr/hal-00788440
• 56A. Barto, M. Mirolli, G. Baldassarre.
  
  Novelty or surprise?, in: Frontiers in psychology, 2013, vol. 4.
• 57A. 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.
• 58A. Bennetot, J.-L. Laurent, R. Chatila, N. Díaz-Rodríguez.
  
  Towards Explainable Neural-Symbolic Visual Reasoning, November 2019, Accepted at IJCAI19 Neural-Symbolic Learning and Reasoning Workshop (https://sites.google.com/view/nesy2019/home).
  
  https://hal.archives-ouvertes.fr/hal-02379596
• 59D. Berlyne.
  
  Conflict, Arousal and Curiosity, McGraw-Hill, 1960.
• 60C. Breazeal.
  
  Designing sociable robots, The MIT Press, 2004.
• 61G. Brockman, V. Cheung, L. Pettersson, J. Schneider, J. Schulman, J. Tang, W. Zaremba.
  
  Openai gym, in: arXiv preprint arXiv:1606.01540, 2016.
• 62J. Brooke.
  
  SUS-A quick and dirty usability scale, in: Usability evaluation in industry, 1996, vol. 189, n^o 194, pp. 4–7.
• 63R. 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.
• 64H. 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
• 65H. Caselles-Dupré, M. Garcia-Ortiz, D. Filliat.
  
  Continual State Representation Learning for Reinforcement Learning using Generative Replay, in: Workshop on Continual Learning, NeurIPS 2018 (Neural Information Processing Systems), Montreal, Canada, December 2018.
  
  https://hal.archives-ouvertes.fr/hal-01951951
• 66B. W.-C. Chan.
  
  Lenia: Biology of Artificial Life, in: Complex Systems, 2019, vol. 28, n^o 3, pp. 251–-286.
• 67A. Clark.
  
  Mindware: An Introduction to the Philosophy of Cognitive Science, Oxford University Press, 2001.
• 68B. 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, n^o 2, pp. 20–48.
  
  https://hal.inria.fr/hal-00913669
• 69B. Clément.
  
  Adaptive Personalization of Pedagogical Sequences using Machine Learning, Université de Bordeaux, December 2018.
  
  https://hal.inria.fr/tel-01968241
• 70D. Cohn, Z. Ghahramani, M. Jordan.
  
  Active learning with statistical models, in: Journal of artificial intelligence research, 1996, vol. 4, pp. 129–145.
• 71C. 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
• 72W. Croft, D. Cruse.
  
  Cognitive Linguistics, Cambridge Textbooks in Linguistics, Cambridge University Press, 2004.
• 73M. Csikszenthmihalyi.
  
  Flow-the psychology of optimal experience, Harper Perennial, 1991.
• 74P. Dayan, W. Belleine.
  
  Reward, motivation and reinforcement learning, in: Neuron, 2002, vol. 36, pp. 285–298.
• 75E. Deci, R. Ryan.
  
  Intrinsic Motivation and Self-Determination in Human Behavior, Plenum Press, 1985.
• 76T. 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
• 77S. 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
• 78N. 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
• 79J. Elman.
  
  Learning and development in neural networks: The importance of starting small, in: Cognition, 1993, vol. 48, pp. 71–99.
• 80S. 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.
• 81S. 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.
• 82S. 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
• 83S. Forestier, Y. Mollard, P.-Y. Oudeyer.
  
  Intrinsically motivated goal exploration processes with automatic curriculum learning, in: arXiv preprint arXiv:1708.02190, 2017.
• 84S. Fujimoto, H. van Hoof, D. Meger.
  
  Addressing function approximation error in actor-critic methods, in: arXiv preprint arXiv:1802.09477, 2018.
• 85J. 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, n^o 11, pp. 585-93. [ DOI : 10.1016/j.tics.2013.09.001 ]
  
  https://hal.inria.fr/hal-00913646
• 86J. 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, n^o 11, pp. 585–593.
• 87T. 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
• 88T. Haarnoja, A. Zhou, P. Abbeel, S. Levine.
  
  Soft actor-critic: Off-policy maximum entropy deep reinforcement learning with a stochastic actor, in: arXiv preprint arXiv:1801.01290, 2018.
• 89S. Harnad.
  
  The symbol grounding problem, in: Physica D, 1990, vol. 40, pp. 335–346.
• 90M. Hasenjager, H. Ritter.
  
  Active learning in neural networks, Physica-Verlag GmbH, Heidelberg, Germany, Germany, 2002, pp. 137–169.
• 91J. Haugeland.
  
  Artificial Intelligence: the very idea, The MIT Press, Cambridge, MA, USA, 1985.
• 92A. Hill, A. Raffin, M. Ernestus, R. Traore, P. Dhariwal, C. Hesse, O. Klimov, A. Nichol, M. Plappert, A. Radford, J. Schulman, S. Sidor, Y. Wu.
  
  Stable Baselines, GitHub, 2018, https://github.com/hill-a/stable-baselines.
• 93J.-C. Horvitz.
  
  Mesolimbocortical and nigrostriatal dopamine responses to salient non-reward events, in: Neuroscience, 2000, vol. 96, n^o 4, pp. 651-656.
• 94X. 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.
• 95S. 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
• 96M. Johnson.
  
  Developmental Cognitive Neuroscience, 2nd, Blackwell publishing, 2005.
• 97P. Karvinen, N. Díaz-Rodríguez, S. Grönroos, J. Lilius.
  
  RDF Stores for Enhanced Living Environments: An Overview, in: Enhanced Living Environments: Algorithms, Architectures, Platforms, and Systems, I. Ganchev, N. M. Garcia, C. Dobre, C. X. Mavromoustakis, R. Goleva (editors), Springer, January 2019, pp. 19-52. [ DOI : 10.1007/978-3-030-10752-9_2 ]
  
  https://hal.archives-ouvertes.fr/hal-02381354
• 98C. Kidd, B. Hayden.
  
  The psychology and neuroscience of curiosity, in: Neuron (in press), 2015.
• 99W. 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.
• 100C. 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, n^o 5, pp. 239–252.
• 101A. 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
• 102T. Lesort, H. Caselles-Dupré, M. Garcia-Ortiz, J.-F. Goudou, D. Filliat.
  
  Generative Models from the perspective of Continual Learning, in: IJCNN - International Joint Conference on Neural Networks, Budapest, Hungary, July 2019.
  
  https://hal.archives-ouvertes.fr/hal-01951954
• 103T. 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
• 104T. Lesort, V. Lomonaco, A. STOIAN, D. Maltoni, D. Filliat, N. Díaz-Rodríguez.
  
  Continual Learning for Robotics: Definition, Framework, Learning Strategies, Opportunities and Challenges, November 2019, working paper or preprint.
  
  https://hal.archives-ouvertes.fr/hal-02381343
• 105T. Lesort, M. Seurin, X. Li, N. Díaz-Rodríguez, D. Filliat.
  
  Deep unsupervised state representation learning with robotic priors: a robustness analysis, in: IJCNN 2019 - International Joint Conference on Neural Networks, Budapest, Hungary, IEEE, July 2019, pp. 1-8. [ DOI : 10.1109/IJCNN.2019.8852042 ]
  
  https://hal.archives-ouvertes.fr/hal-02381375
• 106G. Loewenstein.
  
  The psychology of curiosity: A review and reinterpretation, in: Psychological bulletin, 1994, vol. 116, n^o 1, 75 p.
• 107M. 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
• 108M. 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
• 109M. Lungarella, G. Metta, R. Pfeifer, G. Sandini.
  
  Developmental Robotics: A Survey, in: Connection Science, 2003, vol. 15, n^o 4, pp. 151-190.
• 110N. 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
• 111D. 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.
• 112J. 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.
• 113M. 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
• 114C. 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, 2019, vol. 93, pp. 235–251.
• 115M. 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.
• 116D. 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.
• 117P. Miller.
  
  Theories of developmental psychology, 4th, New York: Worth, 2001.
• 118M. Mirolli, G. Baldassarre.
  
  Functions and mechanisms of intrinsic motivations, in: Intrinsically Motivated Learning in Natural and Artificial Systems, Springer, 2013, pp. 49–72.
• 119C. 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
• 120C. 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
• 121A. Nair, V. Pong, M. Dalal, S. Bahl, S. Lin, S. Levine.
  
  Visual Reinforcement Learning with Imagined Goals, in: CoRR, 2018, vol. abs/1807.04742.
  
  http://arxiv.org/abs/1807.04742
• 122S. 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
• 123S. 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
• 124S. M. Nguyen, P.-Y. Oudeyer.
  
  Socially Guided Intrinsic Motivation for Robot Learning of Motor Skills, in: Autonomous Robots, March 2014, vol. 36, n^o 3, pp. 273-294. [ DOI : 10.1007/s10514-013-9339-y ]
  
  https://hal.inria.fr/hal-00936938
• 125S. 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
• 126S. 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.
• 127P.-Y. Oudeyer, F. Kaplan, V. Hafner.
  
  Intrinsic Motivation for Autonomous Mental Development, in: IEEE Transactions on Evolutionary Computation, January 2007, vol. 11, n^o 2, pp. 265-286. [ DOI : 10.1109/TEVC.2006.890271 ]
  
  https://hal.inria.fr/hal-00793610
• 128P.-Y. Oudeyer, F. Kaplan, V. Hafner.
  
  Intrinsic Motivation Systems for Autonomous Mental Development, in: IEEE Transactions on Evolutionary Computation, 2007, vol. 11, n^o 1, pp. 265–286.
  
  http://www.pyoudeyer.com/ims.pdf
• 129P.-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.
• 130P.-Y. Oudeyer, F. Kaplan.
  
  What is intrinsic motivation? A typology of computational approaches, in: Frontiers in Neurorobotics, 2007, vol. 1, n^o 1.
• 131P.-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/
• 132P.-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/
• 133M. 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.
• 134L. 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.
• 135V. H. Pong, M. Dalal, S. Lin, A. Nair, S. Bahl, S. Levine.
  
  Skew-Fit: State-Covering Self-Supervised Reinforcement Learning, in: CoRR, 2019, vol. abs/1903.03698.
  
  http://arxiv.org/abs/1903.03698
• 136A. 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
• 137A. 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: NeurIPS 2018 Workshop on “Deep Reinforcement Learning”, Montreal, Canada, December 2018.
  
  https://hal.archives-ouvertes.fr/hal-01931713
• 138A. Raffin, A. Hill, R. Traoré, T. Lesort, N. Díaz-Rodríguez, D. Filliat.
  
  Decoupling feature extraction from policy learning: assessing benefits of state representation learning in goal based robotics, in: SPiRL 2019 : Workshop on Structure and Priors in Reinforcement Learning at ICLR 2019, Nouvelle Orléans, United States, May 2019, Github repo: https://github.com/araffin/srl-zoo Documentation: https://srl-zoo.readthedocs.io/en/latest/, As part of SRL-Toolbox: https://s-rl-toolbox.readthedocs.io/en/latest/. Accepted to the Workshop on Structure & Priors in Reinforcement Learning at ICLR 2019.
  
  https://hal.archives-ouvertes.fr/hal-02285831
• 139A. 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.
• 140E. 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, n^o 1, pp. 86–90.
• 141V. G. Santucci, G. Baldassarre, M. Mirolli.
  
  Which is the best intrinsic motivation signal for learning multiple skills?, in: Frontiers in neurorobotics, 2013, vol. 7.
• 142N. Savinov, A. Raichuk, R. Marinier, D. Vincent, M. Pollefeys, T. P. Lillicrap, S. Gelly.
  
  Episodic Curiosity through Reachability, in: CoRR, 2018, vol. abs/1810.02274.
  
  http://arxiv.org/abs/1810.02274
• 143T. 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/
• 144T. Schaul, D. Horgan, K. Gregor, D. Silver.
  
  Universal value function approximators, in: International Conference on Machine Learning, 2015, pp. 1312–1320.
• 145M. 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
• 146J. 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.
• 147W. Schultz, P. Dayan, P. Montague.
  
  A neural substrate of prediction and reward, in: Science, 1997, vol. 275, pp. 1593-1599.
• 148K. O. Stanley.
  
  Exploiting regularity without development, in: Proceedings of the AAAI Fall Symposium on Developmental Systems, AAAI Press Menlo Park, CA, 2006, 37 p.
• 149E. 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.
• 150E. Thelen, L. B. Smith.
  
  A dynamic systems approach to the development of cognition and action, MIT Press, Cambridge, MA, 1994.
• 151A. 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.
• 152R. Traoré, H. Caselles-Dupré, T. Lesort, T. Sun, G. Cai, D. Filliat, N. Díaz-Rodríguez.
  
  DISCORL: Continual reinforcement learning via policy distillation, in: NeurIPS workshop on Deep Reinforcement Learning, Vancouver, Canada, December 2019.
  
  https://hal.archives-ouvertes.fr/hal-02381494
• 153R. Traoré, H. Caselles-Dupré, T. Lesort, T. Sun, N. Díaz-Rodríguez, D. Filliat.
  
  Continual Reinforcement Learning deployed in Real-life using Policy Distillation and Sim2Real Transfer, in: ICML Workshop on “Multi-Task and Lifelong Reinforcement Learning”, Long Beach, United States, June 2019, accepted to the Workshop on Multi-Task and Lifelong Reinforcement Learning, ICML 2019.
  
  https://hal.archives-ouvertes.fr/hal-02285839
• 154A. Turing.
  
  Computing machinery and intelligence, in: Mind, 1950, vol. 59, pp. 433-460.
• 155M. 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.
• 156F. Varela, E. Thompson, E. Rosch.
  
  The embodied mind : Cognitive science and human experience, MIT Press, Cambridge, MA, 1991.
• 157S. Venkattaramanujam, E. Crawford, T. Doan, D. Precup.
  
  Self-supervised Learning of Distance Functions for Goal-Conditioned Reinforcement Learning, in: CoRR, 2019, vol. abs/1907.02998.
  
  http://arxiv.org/abs/1907.02998
• 158J. 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.
• 159R. C. Wilson, A. G. Collins.
  
  Ten simple rules for the computational modeling of behavioral data, in: eLife, 2019, vol. 8, e49547 p.