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Major publications by the team in recent years
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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
Doctoral Dissertations and Habilitation Theses
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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
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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
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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
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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)
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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
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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
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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 -
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