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Artificial Intelligence: The Point of View of Developmental Robotics

  • Jean-Christophe BaillieEmail author
Chapter
Part of the Synthese Library book series (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.

Keywords

Aldebaran Robotics Developmental robotics Learning Symbol grounding 

References

  1. Astington, J. W., & Baird, J. A. (2005). Why language matters for theory of mind. Oxford: Oxford University Press.CrossRefGoogle Scholar
  2. 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
  3. 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.CrossRefGoogle Scholar
  4. Baron-Cohen, S. (1997). Mindblindness: An essay on autism and theory of mind. Cambridge: MIT.Google Scholar
  5. Barsalou, L. (2008a). Grounded cognition. Annual Review of Psychology, 59, 617–645.CrossRefGoogle Scholar
  6. 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
  7. 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
  8. Behnke, S. (2003). Hierarchical neural networks for image interpretation (Vol. 2766). Berlin/New York: Springer.Google Scholar
  9. Bengio, Y. (2009). Learning deep architectures for ai. Foundations and Trends®; in Machine Learning, 2(1), 1–127.CrossRefGoogle Scholar
  10. 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.CrossRefGoogle Scholar
  11. Brooks, R. (1990). Elephants don’t play chess. Robotics and Autonomous Systems, 6(1), 3–15.CrossRefGoogle Scholar
  12. Bybee, J. (2006). From usage to grammar: The mind’s response to repetition. Language, 82, 711–733.CrossRefGoogle Scholar
  13. Cangelosi, A., & Parisi, D. (2002). Simulating the evolution of language. London: SpringerCrossRefGoogle Scholar
  14. 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
  15. Chialvo, D. R. (2010). Emergent complex neural dynamics. Nature Physics, 6(10), 744–750.CrossRefGoogle Scholar
  16. 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.CrossRefGoogle Scholar
  17. 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.CrossRefGoogle Scholar
  18. Goldberg, A. (1995). Constructions: A construction grammar approach to argument structure. Chicago: University of Chicago Press.Google Scholar
  19. 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
  20. Harnad, S. (1990). The symbol grounding problem. Physica D: Nonlinear Phenomena, 42(1), 335–346.CrossRefGoogle Scholar
  21. Hawkins, J., & Blakeslee, S. (2005). On intelligence. New York: St. Martin’s Griffin.Google Scholar
  22. Hinton, G. (2007). Learning multiple layers of representation. Trends in Cognitive Sciences, 11(10), 428–434.CrossRefGoogle Scholar
  23. Hirsh-Pasek, K., & Golinkoff, R. M. (1999). The origins of grammar: Evidence from early language comprehension. Cambridge: MIT.Google Scholar
  24. Jensen, H. J. (1998). Self-organized criticality: Emergent complex behavior in physical and biological systems (Vol. 10). Cambridge/New York: Cambridge university press.CrossRefGoogle Scholar
  25. 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.CrossRefGoogle Scholar
  26. 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
  27. Lee, M. H., Meng, Q., & Chao, F. (2007). Staged competence learning in developmental robotics. Adaptive Behavior, 15(3), 241–255.CrossRefGoogle Scholar
  28. Lungarella, M., Metta, G., Pfeifer, R., & Sandini, G. (2003). Developmental robotics: A survey. Connection Science, 15(4), 151–190.CrossRefGoogle Scholar
  29. 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
  30. Minsky, M. (1961). Steps toward artificial intelligence. Proceedings of the IRE, 49(1), 8–30.CrossRefGoogle Scholar
  31. 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.CrossRefGoogle Scholar
  32. Newell, A., & Simon, H. A. (1961). Computer simulation of human thinking. Santa Monica: Rand Corporation.Google Scholar
  33. 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.CrossRefGoogle Scholar
  34. 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
  35. 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
  36. Oudeyer, P. Y., & Kaplan, F. (2006). Discovering communication. Connection Science, 18(2), 189–206.CrossRefGoogle Scholar
  37. 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
  38. 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
  39. 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.CrossRefGoogle Scholar
  40. Schmidhuber, J. (2006). Developmental robotics, optimal artificial curiosity, creativity, music, and the fine arts. Connection Science, 18(2), 173–187.CrossRefGoogle Scholar
  41. Smith, L., & Gasser, M. (2005). The development of embodied cognition: Six lessons from babies. Artificial Life, 11(1–2), 13–29.CrossRefGoogle Scholar
  42. Steels, L. (2001). Language games for autonomous robots. Intelligent Systems, IEEE, 16(5), 16–22.Google Scholar
  43. Steels, L. (2005). The emergence and evolution of linguistic structure: From lexical to grammatical communication systems. Connection Science, 17(3–4), 213–230.CrossRefGoogle Scholar
  44. Steels, L. (2011). Design patterns in fluid construction grammar (Vol. 11). Amsterdam/Philadelphia: John Benjamins.Google Scholar
  45. Steels, L. (2012a). Experiments in cultural language evolution (Vol. 3). Amsterdam/Philadelphia: John Benjamins.Google Scholar
  46. Steels, L. (2012b). Interactions between cultural, social and biological explanations for language evolution. Physics of Life Reviews, 9(1), 5–8.CrossRefGoogle Scholar
  47. Steels, L., & Hild, M. (2012). Language grounding in robots. New York: Springer.CrossRefGoogle Scholar
  48. Thelen, E., & Smith, L. B. (1996). A dynamic systems approach to the development of cognition and action. Cambridge: MIT.Google Scholar
  49. 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
  50. 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
  51. Tomasello, M. (1999). The cultural origins of human cognition. Cambridge: Harvard University Press.Google Scholar
  52. Tomasello, M. (2005). Constructing a language: A usage-based theory of language acquisition. Cambridge: Harvard University Press.Google Scholar
  53. Turing, A. M. (1950). Computing machinery and intelligence. Mind, 59(236), 433–460.CrossRefGoogle Scholar
  54. 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.CrossRefGoogle Scholar
  55. Wiskott, L., & Sejnowski, T. J. (2002). Slow feature analysis: Unsupervised learning of invariances. Neural Computation, 14(4), 715–770.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Director Aldebaran Robotics AI Lab/A-LabsParisFrance

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