Intrinsic Motivation Systems for Autonomous Mental Development
Open accessAbstract
Exploratory activities seem to be intrinsically rewarding for children and crucial for their cognitive development. Can a machine be endowed with such an intrinsic motivation system? This is the question we study in this paper, presenting a number of computational systems that try to capture this drive towards novel or curious situations. After discussing related research coming from developmental psychology, neuroscience, developmental robotics, and active learning, this paper presents the mechanism of Intelligent Adaptive Curiosity, an intrinsic motivation system which pushes a robot towards situations in which it maximizes its learning progress. This drive makes the robot focus on situations which are neither too predictable nor too unpredictable, thus permitting autonomous mental development. The complexity of the robot's activities autonomously increases and complex developmental sequences self-organize without being constructed in a supervised manner. Two experiments are presented illustrating the stage-like organization emerging with this mechanism. In one of them, a physical robot is placed on a baby play mat with objects that it can learn to manipulate. Experimental results show that the robot first spends time in situations which are easy to learn, then shifts its attention progressively to situations of increasing difficulty, avoiding situations in which nothing can be learned. Finally, these various results are discussed in relation to more complex forms of behavioral organization and data coming from developmental psychology.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Intrinsic motivation develops internal reward signals that drive exploration and skill acquisition without external supervision, often based on learning progress, rewarding improved ability to predict or control aspects of the environment.
“Intrinsic motivation develops internal reward signals that drive exploration and skill acquisition without external supervision, often based on learning progress, rewarding improved ability to predict or control aspects of the environment (Singh et al., 2004; Oudeyer et al., 2007; ¸ Sim¸ sek and Barto, 2006).”
From Toward Enactive Artificial Intelligence (2026) · cites this paper as Oudeyer et al., 2007
The brain generates intrinsic rewards when it senses learning progress, measured by tracking prediction errors over time.
“One solution, emerging from the field of developmental robotics, is that the brain generates intrinsic rewards when it senses learning progress, which it measures by tracking its prediction errors over time (Oudeyer et al., 2007).”
From Attention, learning, and the value of information (2012) · cites this paper as Oudeyer et al., 2007
To generate adaptive exploration, the brain must distinguish between at least two types of uncertainty: reducible and irreducible.
“It has been previously noted that to generate adaptive exploration the brain must distinguish between at least two types of uncertainty (Oudeyer et al., 2007; Payzan-LeNestour and Bossaerts, 2011; Yu and Dayan, 2005).”
From Attention, learning, and the value of information (2012) · cites this paper as Oudeyer et al., 2007
The intrinsic reward mechanism may motivate learning in the absence of external reward and has been effective in producing curiosity-like behaviors in robots.
“This mechanism may motivate learning even in the absence of an external reward, and has been very effective in producing curiosity-like behaviors - whereby robots remain spontaneously interested in activities of intermediate complexity where they improve their predictions but disengage from random (unlearnable) or from overlearned and “boring” tasks.”
From Attention, learning, and the value of information (2012) · cites this paper as Oudeyer et al., 2007
Cites
(1978) · Medical Entomology and Zoology · Cited by 53,488 · see citing works
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. G. (1998) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.1998.712192 · Cited by 27,225 · see citing works
Intrinsic Motivation and Self-Determination in Human Behavior
Deci, E. L., & Ryan, R. M. (1985) · doi:10.1007/978-1-4899-2271-7 · Cited by 26,772 · see citing works
Reinforcement Learning: An Introduction
Sutton, R. S., & Barto, A. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2004.842673 · Cited by 25,263 · see citing works
The Ecological Approach to Visual Perception
Bornstein, M. H., & Gibson, J. J. (1980) · The Journal of Aesthetics and Art Criticism · doi:10.2307/429816 · Cited by 22,935 · see citing works
Intrinsic Motivation and Self-Determination in Human Behavior
Miller, K. A. J., Deci, E. L., & Ryan, R. M. (1988) · Contemporary Sociology A Journal of Reviews · doi:10.2307/2070638 · Cited by 15,361 · see citing works
Flow: the psychology of optimal experience
(1990) · Choice Reviews Online · doi:10.5860/choice.28-0597 · Cited by 12,652 · see citing works
The Ecological Approach to Visual Perception: Classic Edition
Gibson, J. J. (2014) · doi:10.4324/9781315740218 · Cited by 12,132 · see citing works
The origins of intelligence in children.
Piaget, J. (1952) · doi:10.1037/11494-000 · Cited by 10,687 · see citing works
Philosophy in the flesh: the embodied mind and its challenge to Western thought
VEREZA, S. C. (1999) · Choice Reviews Online · doi:10.5860/choice.37-0239 · Cited by 9,867 · see citing works
Flow: The Psychology of Optimal Experience
Buchanan, R., & Csíkszentmihályi, M. (1991) · Design Issues · doi:10.2307/1511458 · Cited by 9,437 · see citing works
A neural substrate of prediction and reward
Schultz, W., Dayan, P., & Montague, P. R. (1997) · Science · doi:10.1126/science.275.5306.1593 · Cited by 9,388 · see citing works
Watkins, C. J. C. H., & Dayan, P. (1992) · Machine Learning · doi:10.1007/bf00992698 · Cited by 9,083 · see citing works
Conflict, arousal, and curiosity.
Berlyne, D. E. (1960) · McGraw-Hill Book Company · doi:10.1037/11164-000 · Cited by 6,018 · see citing works
Motivation reconsidered: The concept of competence.
White, R. W. (1959) · Psychological Review · doi:10.1037/h0040934 · Cited by 5,681 · see citing works
Play, Dreams And Imitation In Childhood
Piaget, J. (2013) · Routledge eBooks · doi:10.4324/9781315009698 · Cited by 5,416 · see citing works
Understanding and sharing intentions: The origins of cultural cognition
Tomasello, M., Carpenter, M., Call, J., Behne, T., & Moll, H. (2005) · Behavioral and Brain Sciences · doi:10.1017/s0140525x05000129 · Cited by 4,882 · see citing works
Motivation reconsidered: The concept of competence.
White, R. W. (1963) · Scott, Foresman eBooks · doi:10.1037/14156-005 · Cited by 4,835 · see citing works
A FRAMEWORK FOR REPRESENTING KNOWLEDGE
Minsky, M. (1988) · Elsevier eBooks · doi:10.1016/b978-1-4832-1446-7.50018-2 · Cited by 4,542 · see citing works
The Handbook of brain theory and neural networks
(1996) · Choice Reviews Online · doi:10.5860/choice.33-4484 · Cited by 3,913 · see citing works
A dynamic systems approach to the development of cognition and action
Thelen, E., & Smith, L. B. (1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90021-8 · Cited by 3,454 · see citing works
Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
Sutton, R. S., Precup, D., & Singh, S. (1999) · Artificial Intelligence · doi:10.1016/s0004-3702(99)00052-1 · Cited by 3,119 · see citing works
Kronenfeld, D. B. (1978) · Language · doi:10.2307/412850 · Cited by 3,094 · see citing works
The psychology of computer vision
Rosenfeld, A. (1976) · Artificial Intelligence · doi:10.1016/0004-3702(76)90008-4 · Cited by 3,015 · see citing works
Scripts, Plans, Goals, and Understanding: An Inquiry into Human Knowledge Structures
Hunt, E. B., Schank, R. C., & Abelson, R. P. (1979) · The American Journal of Psychology · doi:10.2307/1421499 · Cited by 2,967 · see citing works
Active Learning with Statistical Models
Cohn, D. A., Zoubin Ghahramani, A., & Jordan, M. I. (1996) · Journal of Artificial Intelligence Research · doi:10.1613/jair.295 · Cited by 2,054 · see citing works
Learning and development in neural networks: the importance of starting small
Elman, J. L. (1993) · Cognition · doi:10.1016/0010-0277(93)90058-4 · Cited by 1,988 · see citing works
Improving Generalization with Active Learning
Cohn, D. A., Atlas, L., & Ladner, R. E. (1994) · Machine Learning · doi:10.1023/a:1022673506211 · Cited by 1,597 · see citing works
The Handbook of brain theory and neural networks
(2003) · Choice Reviews Online · doi:10.5860/choice.41-0300 · Cited by 1,540 · see citing works
Cyberbotics Ltd. Webots <sup>™</sup> : Professional Mobile Robot Simulation
Michel, O. (2004) · International Journal of Advanced Robotic Systems · doi:10.5772/5618 · Cited by 1,261 · see citing works
Information-based objective functions for active data selection
MacKay, D. J. C. (1992) · Neural Computation · doi:10.1162/neco.1992.4.4.590 · Cited by 1,221 · see citing works
(2001) · The MIT Press eBooks · doi:10.7551/mitpress/1251.001.0001 · Cited by 1,162 · see citing works
Mesolimbocortical and nigrostriatal dopamine responses to salient non-reward events
Horvitz, J. C. (2000) · Neuroscience · doi:10.1016/s0306-4522(00)00019-1 · Cited by 967 · see citing works
Reward, Motivation, and Reinforcement Learning
Dayan, P., & Balleine, B. W. (2002) · Neuron · doi:10.1016/s0896-6273(02)00963-7 · Cited by 855 · see citing works
Toward Optimal Active Learning through Sampling Estimation of Error Reduction
Roy, N., & McCallum, A. (2001) · Cited by 819 · see citing works
Curious model-building control systems
Schmidhuber, J. (1991) · [Proceedings] 1991 IEEE International Joint Conference on Neural Networks · doi:10.1109/ijcnn.1991.170605 · Cited by 733 · see citing works
Autonomous Mental Development by Robots and Animals
Weng, J., McClelland, J. L., Pentland, A., Sporns, O., Stockman, I. J., Sur, M., & Thelen, E. (2001) · Science · doi:10.1126/science.291.5504.599 · Cited by 655 · see citing works
Developmental robotics: a survey
Lungarella, M., Giorgio Metta, B., Pfeifer, R., & Sandini, G. (2003) · Connection Science · doi:10.1080/09540090310001655110 · Cited by 626 · see citing works
The Imitative Mind: Development, Evolution and Brain Bases
Meltzoff, A. N., & Prinz, W. (2002) · Cited by 540 · see citing works
The Dynamics of Active Categorical Perception in an Evolved Model Agent
Beer, R. D. (2003) · Adaptive Behavior · doi:10.1177/1059712303114001 · Cited by 505 · see citing works
Dopamine: generalization and bonuses
Kakade, S. M., & Dayan, P. (2002) · Neural Networks · doi:10.1016/s0893-6080(02)00048-5 · Cited by 477 · see citing works
Multiple model-based reinforcement learning
Doya, K., Samejima, K., Katagiri, K.-I., & Kawato, M. (2002) · Neural Computation · doi:10.1162/089976602753712972 · Cited by 476 · see citing works
Rekimoto, J., & Ayatsuka, Y. (2000) · doi:10.1145/354666.354667 · Cited by 430 · see citing works
(2013) · Oxford University Press eBooks · doi:10.1093/acprof:oso/9780199692972.001.0001 · Cited by 409 · see citing works
The role of imitation in understanding persons and developing a theory of mind
Meltzoff, A. N. (1993) · Medical Entomology and Zoology · Cited by 381 · see citing works
Intrinsically Motivated Learning of Hierarchical Collections of Skills
Barto, A. G., Singh, S. P., & Chentanez, N. (2004) · Cited by 376 · see citing works
Social Understanding at the End of the First Year of Life
Moore, C. L., & Corkum, V. (1994) · Developmental Review · doi:10.1006/drev.1994.1014 · Cited by 356 · see citing works
Mental Development in the Child and the Race
Bolton, T. L., & Baldwin, J. M. (1895) · The American Journal of Psychology · doi:10.2307/1412056 · Cited by 328 · see citing works
Complex Systems: Chaos and Beyond
Kaneko, K., & Tsuda, I. (2001) · doi:10.1007/978-3-642-56861-9 · Cited by 275 · see citing works
Purposive behavior acquisition for a real robot by vision-based reinforcement learning
Asada, M., Noda, S., Tawaratsumida, S., & Hosoda, K. (1996) · Machine Learning · doi:10.1007/bf00117447 · Cited by 274 · see citing works
Tani, J., & Nolfi, S. (1999) · Neural Networks · doi:10.1016/s0893-6080(99)00060-x · Cited by 259 · see citing works
Information theoretic sensor data selection for active object recognition and state estimation
Denzler, J., & Brown, C. M. (2002) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.982896 · Cited by 257 · see citing works
Scalable Techniques from Nonparametric Statistics for Real Time Robot Learning
Schaal, S., Atkeson, C. G., & Vijayakumar, S. (2002) · Applied Intelligence · doi:10.1023/a:1015727715131 · Cited by 236 · see citing works
The challenges of joint attention
Kaplan, F., & Hafner, V. V. (2006) · Interaction Studies Social Behaviour and Communication in Biological and Artificial Systems · doi:10.1075/is.7.2.04kap · Cited by 227 · see citing works
Self-citationTani, J., Ito, M., & Sugita, Y. (2004) · Neural Networks · doi:10.1016/j.neunet.2004.05.007 · Cited by 208 · see citing works
Mental Development in the Child and the Race.
Tracy, F., & Baldwin, J. M. (1895) · The Philosophical Review · doi:10.2307/2175958 · Cited by 201 · see citing works
DEVELOPMENTAL ROBOTICS: THEORY AND EXPERIMENTS
Weng, J. (2004) · International Journal of Humanoid Robotics · doi:10.1142/s0219843604000149 · Cited by 170 · see citing works
Webots: Professional Mobile Robot Simulation
Michel, O. (2004) · Infoscience (Ecole Polytechnique Fédérale de Lausanne) · Cited by 167 · see citing works
The learning machine: Learning
CERN (2006) · PsycEXTRA Dataset · doi:10.1037/e595122010-001 · Cited by 166 · see citing works
Embodied Artificial Intelligence
Fumiya Iida, A. (2004) · Lecture notes in computer science · doi:10.1007/b99075 · Cited by 159 · see citing works
Selecting concise training sets from clean data
Plutowski, M., & White, H. (1993) · IEEE Transactions on Neural Networks · doi:10.1109/72.207618 · Cited by 141 · see citing works
Novelty and Reinforcement Learning in the Value System of Developmental Robots
Huang, X., & Weng, J. J. (2002) · CogPrints (University of Southampton) · Cited by 118 · see citing works
URBI: towards a universal robotic low-level programming language
Baillie, J.-C. (2005) · IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) · doi:10.1109/iros.2005.1545467 · Cited by 111 · see citing works
Steels, L. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27833-7_17 · Cited by 82 · see citing works
Exploration in active learning
Thrun, S. (1998) · Cited by 78 · see citing works
Nolfi, S., & Tani, J. (1999) · Connection Science · doi:10.1080/095400999116313 · Cited by 64 · see citing works
Motivational principles for visual know-how development
Kaplan, F., & Oudeyer, P. (2003) · CogPrints (University of Southampton) · Cited by 63 · see citing works
Self-citationAn Emergent Framework For Self-Motivation In Developmental Robotics
Marshall, J. B., Blank, D., & Meeden, L. (2004) · Scholarship, Research, and Creative Work at Bryn Mawr College (Bryn Mawr College) · Cited by 57 · see citing works
A Survey of Transfer Between Connectionist Networks
Pratt, L., & Jennings, B. (1996) · Connection Science · doi:10.1080/095400996116866 · Cited by 55 · see citing works
Intentional agents and goal formation
Dignum, F. P. M., & Conte, R. (1998) · Lecture notes in computer science · doi:10.1007/bfb0026762 · Cited by 55 · see citing works
A theory for mentally developing robots
Weng, J. (2003) · IEEE International Conference on Development and Learning (ICDL) · doi:10.1109/devlrn.2002.1011821 · Cited by 53 · see citing works
Active Learning in Neural Networks
Hasenjäger, M., & Ritter, H. J. (2002) · Studies in fuzziness and soft computing · doi:10.1007/978-3-7908-1803-1_5 · Cited by 49 · see citing works
The progress drive hypothesis: an interpretation of early imitation
Kaplan, F., & Oudeyer, P. (2007) · Cambridge University Press eBooks · doi:10.1017/cbo9780511489808.024 · Cited by 45 · see citing works
Self-citationEgo function of early imitation
Rochat, P. (2002) · Cambridge University Press eBooks · doi:10.1017/cbo9780511489969.006 · Cited by 41 · see citing works
Selecting examples for perceptrons
Watkin, T. L. H., & Rau, A. (1992) · Journal of Physics A Mathematical and General · doi:10.1088/0305-4470/25/1/016 · Cited by 32 · see citing works
A Survey of Connectionist Network Reuse Through Transfer
Pratt, L., & Jennings, B. (1996) · Springer eBooks · doi:10.1007/978-1-4615-5529-2_2 · Cited by 28 · see citing works
Robins, A. V. (1996) · Connection Science · doi:10.1080/095400996116875 · Cited by 27 · see citing works
Bayesian Query Construction for Neural Network Models
Paaß, G., & Kindermann, J. (1994) · Publikationsdatenbank der Fraunhofer-Gesellschaft (Fraunhofer-Gesellschaft) · Cited by 22 · see citing works
Learning predictive representations
Herrmann, J. M., Pawelzik, K. R., & Geisel, T. (2000) · Neurocomputing · doi:10.1016/s0925-2312(00)00245-9 · Cited by 20 · see citing works
Robins, A. V. (1996) · Springer eBooks · doi:10.1007/978-1-4615-5529-2_3 · Cited by 20 · see citing works
Active Learning in Self-Organizing Maps
Hasenjäger, M., Ritter, H., & Obermayer, K. (1999) · Elsevier eBooks · doi:10.1016/b978-044450270-4/50005-x · Cited by 18 · see citing works
Information-Theoretical Aspects of Embodied Artificial Intelligence
Sporns, O., & Pegors, T. K. (2004) · Lecture notes in computer science · doi:10.1007/978-3-540-27833-7_5 · Cited by 16 · see citing works
Selecting Optimal Experiments for Multiple Output Multilayer Perceptrons
Belue, L. M., Bauer, K. W., & Ruck, D. W. (1997) · Neural Computation · doi:10.1162/neco.1997.9.1.161 · Cited by 12 · see citing works
Different criteria for active learning in neural networks: a comparative study.
Poland, J., & Zell, A. (2002) · Cited by 8 · see citing works
van der Maas, H. L. J. (1995) · Cognitive Development · doi:10.1016/0885-2014(95)90030-6 · Cited by 4 · see citing works
Flagged first, then by citations
Cited by
Sorted by citations
Continual lifelong learning with neural networks: A review
Parisi, G. I., Kemker, R., Part, J. L., Kanan, C., & Wermter, S. (2019) · Neural Networks · doi:10.1016/j.neunet.2019.01.012 · Cited by 3,117 · see citing works
Curiosity-Driven Exploration by Self-Supervised Prediction
Pathak, D., Agrawal, P., Efros, A. A., & Darrell, T. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2017.70 · Cited by 1,815 · see citing works
Precise minds in uncertain worlds: Predictive coding in autism.
Van de Cruys, S., Evers, K., Van der Hallen, R., Van Eylen, L., Boets, B., de-Wit, L., & Wagemans, J. (2014) · Psychological Review · doi:10.1037/a0037665 · Cited by 885 · see citing works
What is intrinsic motivation? A typology of computational approaches
Oudeyer, P.-Y. (2007) · Frontiers in Neurorobotics · doi:10.3389/neuro.12.006.2007 · Cited by 811 · see citing works
Information-seeking, curiosity, and attention: computational and neural mechanisms
Gottlieb, J., Oudeyer, P.-Y., Lopes, M., & Baranes, A. (2013) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2013.09.001 · Cited by 784 · see citing works
Self-citationFormal theory of creativity, fun, and intrinsic motivation (1990–2010)
Schmidhuber, J. (2010) · IEEE Transactions on Autonomous Mental Development · doi:10.1109/tamd.2010.2056368 · Cited by 747 · see citing works
Hierarchically organized behavior and its neural foundations: A reinforcement learning perspective
Botvinick, M. M., Niv, Y., & Barto, A. G. (2009) · Cognition · doi:10.1016/j.cognition.2008.08.011 · Cited by 646 · see citing works
Exploration by Random Network Distillation
Burda, Y., Edwards, H., Storkey, A., & Klimov, O. (2018) · arXiv · doi:10.48550/arxiv.1810.12894 · Cited by 527 · see citing works
Towards a neuroscience of active sampling and curiosity
Gottlieb, J., & Oudeyer, P.-Y. (2018) · Nature Reviews Neuroscience · doi:10.1038/s41583-018-0078-0 · Cited by 494 · see citing works
Self-citationMachado, M. C., Bellemare, M. G., Talvitie, E., Veness, J., Hausknecht, M., & Bowling, M. (2018) · Journal of Artificial Intelligence Research · doi:10.1613/jair.5699 · Cited by 359 · see citing works
Deep reinforcement learning and its neuroscientific implications
Botvinick, M., Wang, J. X., Dabney, W., Miller, K. J., & Kurth-Nelson, Z. (2020) · Neuron · doi:10.1016/j.neuron.2020.06.014 · Cited by 269 · see citing works
Attention, learning, and the value of information
Gottlieb, J. (2012) · Neuron · doi:10.1016/j.neuron.2012.09.034 · Cited by 241 · see citing works
Emotion in reinforcement learning agents and robots: a survey
Moerland, T. M., Broekens, J., & Jonker, C. M. (2018) · Machine Learning · doi:10.1007/s10994-017-5666-0 · Cited by 205 · see citing works
The feeling of grip: novelty, error dynamics, and the predictive brain
Kiverstein, J., Miller, M., & Rietveld, E. (2019) · Synthese · doi:10.1007/s11229-017-1583-9 · Cited by 143 · see citing works
Planning to Explore via Self-Supervised World Models
Sekar, R., Rybkin, O., Daniilidis, K., Abbeel, P., Hafner, D., & Pathak, D. (2020) · arXiv · doi:10.48550/arxiv.2005.05960 · Cited by 129 · see citing works
GPT-3-Driven Pedagogical Agents to Train Children’s Curious Question-Asking Skills
Abdelghani, R., Wang, Y.-H., Yuan, X., Wang, T., Lucas, P., Sauzéon, H., & Oudeyer, P.-Y. (2024) · International Journal of Artificial Intelligence in Education · doi:10.1007/s40593-023-00340-7 · Cited by 129 · see citing works
Self-citationPlay, Curiosity, and Cognition
Chu, J., & Schulz, L. E. (2020) · Annual Review of Developmental Psychology · doi:10.1146/annurev-devpsych-070120-014806 · Cited by 93 · see citing works
Clune, J. (2019) · arXiv · doi:10.48550/arxiv.1905.10985 · Cited by 82 · see citing works
A curious formulation robot enables the discovery of a novel protocell behavior
Grizou, J., Points, L. J., Sharma, A., & Cronin, L. (2020) · Science Advances · doi:10.1126/sciadv.aay4237 · Cited by 77 · see citing works
RLHF Deciphered: A Critical Analysis of Reinforcement Learning from Human Feedback for LLMs
Chaudhari, S., Aggarwal, P., Murahari, V., Rajpurohit, T., Kalyan, A., Narasimhan, K., Deshpande, A., & Castro da Silva, B. (2026) · ACM Computing Surveys · doi:10.1145/3743127 · Cited by 92 · see citing works
Intrinsic motivations and open-ended development in animals, humans, and robots: an overview
Baldassarre, G., Stafford, T., Mirolli, M., Redgrave, P., Ryan, R. M., & Barto, A. (2014) · Frontiers in Psychology · doi:10.3389/fpsyg.2014.00985 · Cited by 73 · see citing works
Balancing new against old information: the role of puzzlement surprise in learning
Faraji, M., Preuschoff, K., & Gerstner, W. (2018) · Neural Computation · doi:10.1162/neco_a_01025 · Cited by 68 · see citing works
Lessons from infant learning for unsupervised machine learning
Zaadnoordijk, L., Besold, T. R., & Cusack, R. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00488-2 · Cited by 65 · see citing works
Teacher algorithms for curriculum learning of Deep RL in continuously parameterized environments
Portelas, R., Colas, C., Hofmann, K., & Oudeyer, P.-Y. (2019) · arXiv · doi:10.48550/arxiv.1910.07224 · Cited by 59 · see citing works
Self-citationSibley, M. H., Graziano, P. A., Ortiz, M., Rodriguez, L., & Coxe, S. (2019) · Journal of School Psychology · doi:10.1016/j.jsp.2019.10.005 · Cited by 57 · see citing works
An Information-Theoretic Perspective on Intrinsic Motivation in Reinforcement Learning: A Survey
Aubret, A., Matignon, L., & Hassas, S. (2023) · Entropy · doi:10.3390/e25020327 · Cited by 52 · see citing works
Learning tactile skills through curious exploration
Pape, L., Oddo, C. M., Controzzi, M., Cipriani, C., Förster, A., Carrozza, M. C., & Schmidhuber, J. (2012) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2012.00006 · Cited by 51 · see citing works
Predictive processing in cognitive robotics: A review
Ciria, A., Schillaci, G., Pezzulo, G., Hafner, V. V., & Lara, B. (2021) · Neural Computation · doi:10.1162/neco_a_01383 · Cited by 51 · see citing works
Self-citationCuriosity: primate neural circuits for novelty and information seeking
Monosov, I. E. (2024) · Nature Reviews Neuroscience · doi:10.1038/s41583-023-00784-9 · Cited by 44 · see citing works
Butz, M. V. (2021) · KI - Künstliche Intelligenz · doi:10.1007/s13218-021-00705-x · Cited by 42 · see citing works
Conversational agents for fostering curiosity-driven learning in children
Abdelghani, R., Oudeyer, P.-Y., Law, E., de Vulpillières, C., & Sauzéon, H. (2022) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2022.102887 · Cited by 39 · see citing works
Self-citationLanguage and culture internalization for human-like autotelic AI
Colas, C., Karch, T., Moulin-Frier, C., & Oudeyer, P.-Y. (2022) · Nature Machine Intelligence · doi:10.1038/s42256-022-00591-4 · Cited by 34 · see citing works
Self-citationDarwin Gödel Machine: Open-Ended Evolution of Self-Improving Agents
Zhang, J., Hu, S., Lu, C., Lange, R., & Clune, J. (2025) · SuperIntelligence - Robotics - Safety & Alignment · doi:10.70777/si.v2i3.15063 · Cited by 34 · see citing works
General intelligence requires rethinking exploration
Jiang, M., Rocktäschel, T., & Grefenstette, E. (2022) · arXiv · doi:10.48550/arxiv.2211.07819 · Cited by 17
Not playing by the rules: Exploratory play, rational action, and efficient search
Chu, J., & Schulz, L. (2020) · doi:10.31234/osf.io/9yra2 · Cited by 22 · see citing works
FOCUS: object-centric world models for robotic manipulation
Ferraro, S., Mazzaglia, P., Verbelen, T., & Dhoedt, B. (2025) · Frontiers in Neurorobotics · doi:10.3389/fnbot.2025.1585386 · Cited by 9 · see citing works
Darwin Godel Machine: Open-Ended Evolution of Self-Improving Agents
Zhang, J., Hu, S., Lu, C., Lange, R., & Clune, J. (2025) · arXiv · doi:10.48550/arxiv.2505.22954 · Cited by 5 · see citing works
Craftax: A Lightning-Fast Benchmark for Open-Ended Reinforcement Learning
Matthews, M., Beukman, M., Ellis, B., Samvelyan, M., Jackson, M., Coward, S., & Foerster, J. (2024) · arXiv · doi:10.48550/arxiv.2402.16801 · Cited by 4 · see citing works
When Can AI Models Explain Learning? Validity Criteria for AI as Cognitive Models in Education
Wang, P., Viberg, O., Law, E. L.-C., & Greiff, S. (2026) · Educational Psychology Review · doi:10.1007/s10648-026-10202-y · Cited by 3 · see citing works
Toward Enactive Artificial Intelligence
Rafiee, B., & Sutton, R. (2026) · arXiv · doi:10.48550/arxiv.2605.24238
- Authors
- Oudeyer, P.-Y., Kaplan, F., & Hafner, V. V.
- Affiliations
- École Nationale Supérieure de Techniques Avancées (FR)
- Sony Computer Science Laboratories (JP)
- Venue
- IEEE Transactions on Evolutionary Computation
- Published
- 2007-04
- Volume
- 11
- Issue
- 2
- Pages
- 265-286
- ISSN
- 1089-778X (ISSN-L)
- OpenAlex
- W2101524054
- Cited by
- 1,126
- References
- 85
- Citations by year
- 2026: 37 · 2025: 35 · 2024: 41 · 2023: 52 · 2022: 69 · 2021: 74 · 2020: 100 · 2019: 89 · 2018: 73 · 2017: 62 · 2016: 68 · 2015: 44 · 2014: 62 · 2013: 57 · 2012: 58
- FWCI
- 55.18
- Type
- journal article
- Language
- en
- Open access
- green
- inria.hal.science
- Locations
- IEEE Transactions on Evolutionary Computation (publishedVersion)
- CogPrints (University of Southampton) (submittedVersion)
- HAL (submittedVersion, other-oa)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Reinforcement Learning in Robotics
- Physical Sciences › Computer Science › Artificial Intelligence › Psychiatry, Mental Health, Neuroscience
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- intrinsic motivation, curiosity, autonomous mental development, developmental robotics, active learning (machine learning), self-organization, developmental stages, robot learning, exploration, developmental psychology
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
