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Autonomous development and learning in artificial intelligence and robotics: Scaling up deep learning to human-like learning

Published online by Cambridge University Press:  10 November 2017

Pierre-Yves Oudeyer
Pierre-Yves Oudeyer*
Affiliation:
Inria and Ensta Paris-Tech, 33405 Talence, France. pierre-yves.oudeyer@inria.frhttp://www.pyoudeyer.com
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Abstract

Autonomous lifelong development and learning are fundamental capabilities of humans, differentiating them from current deep learning systems. However, other branches of artificial intelligence have designed crucial ingredients towards autonomous learning: curiosity and intrinsic motivation, social learning and natural interaction with peers, and embodiment. These mechanisms guide exploration and autonomous choice of goals, and integrating them with deep learning opens stimulating perspectives.

Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 40 , 2017 , e275
Copyright
Copyright © Cambridge University Press 2017 

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References

Asada, M., Hosoda, K., Kuniyoshi, Y., Ishiguro, H., Inui, T., Yoshikawa, Y. & Yoshida, C. (2009) Cognitive developmental robotics: A survey. IEEE Transactions on Autonomous Mental Development 1(1):12–34.Google Scholar
Baldassarre, G. & Mirolli, M., eds. (2013) Intrinsically motivated learning in natural and artificial systems. Springer.Google Scholar
Baranes, A. & Oudeyer, P.-Y. (2013) Active learning of inverse models with intrinsically motivated goal exploration in robots. Robotics and Autonomous Systems 61(1):49–73.Google Scholar
Baranes, A. F., Oudeyer, P. Y. & Gottlieb, J. (2014) The effects of task difficulty, novelty and the size of the search space on intrinsically motivated exploration. Frontiers in Neurosciences 8:1–9.Google Scholar
Barto, A. (2013) Intrinsic motivation and reinforcement learning. In: Intrinsically motivated learning in natural and artificial systems, ed. Baldassarre, G. & Mirolli, M., pp. 17–47. Springer.Google Scholar
Bellemare, M., Srinivasan, S., Ostrovski, G., Schaul, T., Saxton, D. & Munos, R. (2016) Unifying count-based exploration and intrinsic motivation. Presented at the 2016 Neural Information Processing Systems conference, Barcelona, Spain, December 5–10, 2016. In: Advances in neural information processing systems 29 (NIPS 2016), ed. Lee, D. D., Sugiyama, M., Luxburg, U. V., Guyon, I. & Garnett, R., pp. 1471–79. Neural Information Processing Systems.Google Scholar
Cangelosi, A. & Schlesinger, M. (2015) Developmental robotics: From babies to robots. MIT Press.Google Scholar
Chernova, S. & Thomaz, A. L. (2014) Robot learning from human teachers. Synthesis lectures on artificial intelligence and machine learning. Morgan & Claypool.Google Scholar
Collins, S., Ruina, A., Tedrake, R. & Wisse, M. (2005) Efficient bipedal robots based on passive-dynamic walkers. Science 307(5712):1082–85.Google Scholar
Flash, T., Hochner, B. (2005) Motor primitives in vertebrates and invertebrates. Current Opinion in Neurobiology 15(6):660–66.Google Scholar
Forestier, S. & Oudeyer, P.-Y. (2016) Curiosity-driven development of tool use precursors: A computational model. In: Proceedings of the 38th Annual Conference of the Cognitive Science Society, Philadelphia, PA, ed. Papafragou, A., Grodner, D., Mirman, D. & Trueswell, J. C., pp. 1859–1864. Cognitive Science Society.Google Scholar
Gottlieb, J., Oudeyer, P-Y., Lopes, M. & Baranes, A. (2013) Information seeking, curiosity and attention:Computational and neural mechanisms. Trends in Cognitive Science 17(11):585–96.Google Scholar
Jaderberg, M., Mnih, V., Czarnecki, W. M., Schaul, T., Leibo, J. Z., Silver, D. & Kavukcuoglu, K. (2016) Reinforcement learning with unsupervised auxiliary tasks. Presented at the 5th International Conference on Learning Representations, Palais des Congrès Neptune, Toulon, France, April 24–26, 2017. arXiv preprint 1611.05397. Available at: https://arxiv.org/abs/1611.05397.Google Scholar
Kidd, C., Piantadosi, S. T. & Aslin, R. N. (2012) The Goldilocks effect: Human infants allocate attention to visual sequences that are neither too simple nor too complex. PLoS One 7(5):e36399.CrossRefGoogle ScholarPubMed
Kulkarni, T. D., Narasimhan, K. R., Saeedi, A. & Tenenbaum, J. B. (2016) Hierarchical deep reinforcement learning: Integrating temporal abstraction and intrinsic motivation. arXiv preprint 1604.06057. Available at: https://arxiv.org/abs/1604.06057.Google Scholar
Lopes, M. & Oudeyer, P.-Y. (2012) The strategic student approach for life-long exploration and learning. In: IEEE International Conference on Development and Learning and Epigenetic Robotics (ICDL), San Diego, CA, November 7–9, 2012, pp. 1–8. IEEE.Google Scholar
Moulin-Frier, C., Nguyen, M. & Oudeyer, P.-Y. (2014) Self-organization of early vocal development in infants and machines: The role of intrinsic motivation. Frontiers in Psychology 4:1006. Available at: http://dx.doi.org/10.3389/fpsyg.2013.01006.Google Scholar
Nguyen, M. & Oudeyer, P.-Y. (2013) Active choice of teachers, learning strategies and goals for a socially guided intrinsic motivation learner, Paladyn Journal of Behavioural Robotics 3(3):136–46.Google Scholar
Nguyen-Tuong, D. & Peters, J. (2011) Model learning for robot control: A survey. Cognitive Processing 12(4):319–40.Google Scholar
Oudeyer, P.-Y. (2016) What do we learn about development from baby robots? WIREs Cognitive Science 8(1–2):e1395. Available at: http://www.pyoudeyer.com/oudeyerWiley16.pdf. doi: 10.1002/wcs.1395.Google Scholar
Oudeyer, P.-Y., Baranes, A. & Kaplan, F. (2013) Intrinsically motivated learning of real-world sensorimotor skills with developmental constraints. In: Intrinsically motivated learning in natural and artificial systems, ed. Baldassarre, G. & Mirolli, M., pp. 303–65. Springer.Google Scholar
Oudeyer, P.-Y., Kaplan, F. & Hafner, V. (2007) Intrinsic motivation systems for autonomous mental development. IEEE Transactions on Evolutionary Computation 11(2):265–86.Google Scholar
Oudeyer, P-Y. & Smith, L. (2016) How evolution may work through curiosity-driven developmental process. Topics in Cognitive Science 8(2):492–502.Google Scholar
Pfeifer, R., Lungarella, M. & Iida, F. (2007) Self-organization, embodiment, and biologically inspired robotics. Science 318(5853):1088–93.Google Scholar
Schmidhuber, J. (1991) Curious model-building control systems. Proceedings of the IEEE International Joint Conference on Neural Networks 2:1458–63.Google Scholar
Vollmer, A-L., Mühlig, M., Steil, J. J., Pitsch, K., Fritsch, J., Rohlfing, K. & Wrede, B. (2014) Robots show us how to teach them: Feedback from robots shapes tutoring behavior during action learning, PLoS One 9(3):e91349.Google Scholar
Yamada, Y., Mori, H. & Kuniyoshi, Y. (2010) A fetus and infant developmental scenario: Self-organization of goal-directed behaviors based on sensory constraints. In: Proceedings of the 10th International Conference on Epigenetic Robotics, Őrenäs Slott, Sweden, pp. 145–52. Lund University Cognitive Studies.Google Scholar