Skip to main content

Artificial Intelligence: The Point of View of Developmental Robotics

  • Chapter
  • First Online:

Part of the book series: Synthese Library ((SYLI,volume 376))

Abstract

We present here the research directions of the newly formed Artificial Intelligence Lab of Aldebaran Robotics. After a short historical review of AI, we introduce the field of developmental robotics, which stresses the importance of understanding the dynamical aspect of intelligence and the early developmental stages from sensorimotor categorization up to higher level socio-cognitive skills. Taking inspiration in particular from developmental psychology, the idea is to model the underlying mechanisms of gradual learning in the context of a progressively more complex interaction with the environment and with other agents. We review the different aspects of this approach that are explored in the lab, with a focus on language acquisition and symbol grounding.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   249.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Astington, J. W., & Baird, J. A. (2005). Why language matters for theory of mind. Oxford: Oxford University Press.

    Google Scholar 

  • Baranes, A., & Oudeyer, P. Y. (2010). Maturationally-constrained competence-based intrinsically motivated learning. In IEEE 9th International Conference on Development and Learning (ICDL), 2010, Ann Arbor (pp. 197–203). IEEE.

    Google Scholar 

  • Barnes, 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 

  • Baron-Cohen, S. (1997). Mindblindness: An essay on autism and theory of mind. Cambridge: MIT.

    Google Scholar 

  • Barsalou, L. (2008a). Grounded cognition. Annual Review of Psychology, 59, 617–645.

    Google Scholar 

  • Barsalou, L. (2008b). Grounding symbolic operations in the brain’s modal systems. In Embodied grounding: Social, cognitive, affective, and neuroscientific approaches. Cambridge/New York: Cambridge University Press.

    Google Scholar 

  • Bates, E., & Goodman, J. (1999). On the emergence of grammar from the lexicon. The emergence of language (pp. 29–79). Mahwah: Lawrence Erlbaum Associates.

    Google Scholar 

  • Behnke, S. (2003). Hierarchical neural networks for image interpretation (Vol. 2766). Berlin/New York: Springer.

    Google Scholar 

  • Bengio, Y. (2009). Learning deep architectures for ai. Foundations and Trends®; in Machine Learning, 2(1), 1–127.

    Google Scholar 

  • Bergen, B., & Chang, N. (2005). Embodied construction grammar in simulation-based language understanding. In Construction grammars: Cognitive grounding and theoretical extensions (pp. 147–190). Amsterdam/Philadelphia: John Benjamins.

    Google Scholar 

  • Brooks, R. (1990). Elephants don’t play chess. Robotics and Autonomous Systems, 6(1), 3–15.

    Google Scholar 

  • Bybee, J. (2006). From usage to grammar: The mind’s response to repetition. Language, 82, 711–733.

    Google Scholar 

  • Cangelosi, A., & Parisi, D. (2002). Simulating the evolution of language. London: Springer

    Google Scholar 

  • Cangelosi, A., Metta, G., Sagerer, G., Nolfi, S., Nehaniv, C., Fischer, K., Tani, J., Belpaeme, T., Sandini, G., & Nori, F., et al. (2010). Integration of action and language knowledge: A roadmap for developmental robotics. IEEE Transactions on Autonomous Mental Development, 2(3), 167–195.

    Google Scholar 

  • Chialvo, D. R. (2010). Emergent complex neural dynamics. Nature Physics, 6(10), 744–750.

    Google Scholar 

  • Fitzpatrick, P., Metta, G., Fitzpatrick, P., & Metta, G. (2003). Grounding vision through experimental manipulation. Philosophical Transactions of the Royal Society of London Series A: Mathematical, Physical and Engineering Sciences, 361(1811), 2165–2185.

    Google Scholar 

  • Glenberg, A. M., & Robertson, D. A. (2000). Symbol grounding and meaning: A comparison of high-dimensional and embodied theories of meaning. Journal of Memory and Language, 43(3), 379–401.

    Google Scholar 

  • Goldberg, A. (1995). Constructions: A construction grammar approach to argument structure. Chicago: University of Chicago Press.

    Google Scholar 

  • Grizou, J., Lopes, M., & Oudeyer, P. (2012). Robot learning simultaneously a task and how to interpret teaching signals. In IEEE-RAS International Conference on Humanoid Robots, Madrid.

    Google Scholar 

  • Harnad, S. (1990). The symbol grounding problem. Physica D: Nonlinear Phenomena, 42(1), 335–346.

    Google Scholar 

  • Hawkins, J., & Blakeslee, S. (2005). On intelligence. New York: St. Martin’s Griffin.

    Google Scholar 

  • Hinton, G. (2007). Learning multiple layers of representation. Trends in Cognitive Sciences, 11(10), 428–434.

    Google Scholar 

  • Hirsh-Pasek, K., & Golinkoff, R. M. (1999). The origins of grammar: Evidence from early language comprehension. Cambridge: MIT.

    Google Scholar 

  • Jensen, H. J. (1998). Self-organized criticality: Emergent complex behavior in physical and biological systems (Vol. 10). Cambridge/New York: Cambridge university press.

    Google Scholar 

  • Law, J., Lee, M., Hülse, M., & Tomassetti, A. (2011). The infant development timeline and its application to robot shaping. Adaptive Behavior, 19(5), 335–358.

    Google Scholar 

  • Le, Q. V., Monga, R., Devin, M., Corrado, G., Chen, K., Ranzato, M., Dean, J., & Ng, A. Y. (2011, preprint). Building high-level features using large scale unsupervised learning. arXiv:11126209.

    Google Scholar 

  • Lee, M. H., Meng, Q., & Chao, F. (2007). Staged competence learning in developmental robotics. Adaptive Behavior, 15(3), 241–255.

    Google Scholar 

  • Lungarella, M., Metta, G., Pfeifer, R., & Sandini, G. (2003). Developmental robotics: A survey. Connection Science, 15(4), 151–190.

    Google Scholar 

  • Mangin, O., & Oudeyer, P. (2012). Learning to recognize parallel combinations of human motion primitives with linguistic descriptions using non-negative matrix factorization. In RSJ International Conference on Intelligent Robots and Systems, Vilamoura-Algarve.

    Google Scholar 

  • Minsky, M. (1961). Steps toward artificial intelligence. Proceedings of the IRE, 49(1), 8–30.

    Google Scholar 

  • Newell, A., Shaw, J., & Simon, H. A. (1988). Chess-playing programs and the problem of complexity. In Computer games I (pp. 89–115). New York: Springer.

    Google Scholar 

  • Newell, A., & Simon, H. A. (1961). Computer simulation of human thinking. Santa Monica: Rand Corporation.

    Google Scholar 

  • Nishimoto, R., Namikawa, J., & Tani, J. (2008). Learning multiple goal-directed actions through self-organization of a dynamic neural network model: A humanoid robot experiment. Adaptive Behavior, 16(2–3), 166–181.

    Google Scholar 

  • Orabona, F., Metta, G., & Sandini, G. (2005). Object-based visual attention: A model for a behaving robot. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition-Workshops, 2005. CVPR Workshops, San Diego (pp. 89–89). IEEE.

    Google Scholar 

  • Orabona, F., Metta, G., & Sandini, G. (2007). A proto-object based visual attention model. In Attention in cognitive systems: Theories and systems from an interdisciplinary viewpoint (pp. 198–215). Berlin/Heidelberg: Springer.

    Google Scholar 

  • Oudeyer, P. Y., & Kaplan, F. (2006). Discovering communication. Connection Science, 18(2), 189–206.

    Google Scholar 

  • Oudeyer, P., Kaplan, F., Hafner, V., & Whyte, A. (2005). The playground experiment: Task-independent development of a curious robot. In Proceedings of the AAAI Spring Symposium on Developmental Robotics, Stanford (pp. 42–47).

    Google Scholar 

  • Rakison, D., & Oakes, L. (2003). Early category and concept development: Making sense of the blooming, buzzing confusion. Oxford/New York: Oxford University Press.

    Google Scholar 

  • Scassellati, B. (1999). Imitation and mechanisms of joint attention: A developmental structure for building social skills on a humanoid robot. In Computation for metaphors, analogy, and agents (pp. 176–195). Berlin/New York: Springer.

    Google Scholar 

  • Schmidhuber, J. (2006). Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts. Connection Science, 18(2), 173–187.

    Google Scholar 

  • Smith, L., & Gasser, M. (2005). The development of embodied cognition: Six lessons from babies. Artificial Life, 11(1–2), 13–29.

    Google Scholar 

  • Steels, L. (2001). Language games for autonomous robots. Intelligent Systems, IEEE, 16(5), 16–22.

    Google Scholar 

  • Steels, L. (2005). The emergence and evolution of linguistic structure: From lexical to grammatical communication systems. Connection Science, 17(3–4), 213–230.

    Google Scholar 

  • Steels, L. (2011). Design patterns in fluid construction grammar (Vol. 11). Amsterdam/Philadelphia: John Benjamins.

    Google Scholar 

  • Steels, L. (2012a). Experiments in cultural language evolution (Vol. 3). Amsterdam/Philadelphia: John Benjamins.

    Google Scholar 

  • Steels, L. (2012b). Interactions between cultural, social and biological explanations for language evolution. Physics of Life Reviews, 9(1), 5–8.

    Google Scholar 

  • Steels, L., & Hild, M. (2012). Language grounding in robots. New York: Springer.

    Google Scholar 

  • Thelen, E., & Smith, L. B. (1996). A dynamic systems approach to the development of cognition and action. Cambridge: MIT.

    Google Scholar 

  • Thrun, S. (1994). A lifelong learning perspective for mobile robot control. In Proceedings of the IEEE/RSJ/GI International Conference on Intelligent Robots and Systems’94.’Advanced Robotic Systems and the Real World’, IROS’94 (Vol. 1, pp. 23–30). Munich: IEEE.

    Google Scholar 

  • Thrun, S. (1995). Exploration in active learning. In M. Arbib (Ed.), Handbook of brain. Science and neural networks (pp. 381–384). Cambridge, MA: MIT Press.

    Google Scholar 

  • Tomasello, M. (1999). The cultural origins of human cognition. Cambridge: Harvard University Press.

    Google Scholar 

  • Tomasello, M. (2005). Constructing a language: A usage-based theory of language acquisition. Cambridge: Harvard University Press.

    Google Scholar 

  • Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460.

    Google Scholar 

  • Weng, J., McClelland, J., Pentland, A., Sporns, O., Stockman, I., Sur, M., & Thelen, E. (2001). Autonomous mental development by robots and animals. Science, 291(5504), 599–600.

    Google Scholar 

  • Wiskott, L., & Sejnowski, T. J. (2002). Slow feature analysis: Unsupervised learning of invariances. Neural Computation, 14(4), 715–770.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jean-Christophe Baillie .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2016 Springer International Publishing Switzerland

About this chapter

Cite this chapter

Baillie, JC. (2016). Artificial Intelligence: The Point of View of Developmental Robotics. In: Müller, V.C. (eds) Fundamental Issues of Artificial Intelligence. Synthese Library, vol 376. Springer, Cham. https://doi.org/10.1007/978-3-319-26485-1_24

Download citation

Publish with us

Policies and ethics