Embodied Simulation Based on Autobiographical Memory

  • Gregoire Pointeau
  • Maxime Petit
  • Peter Ford Dominey
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8064)

Abstract

The ability to generate and exploit internal models of the body, the environment, and their interaction is crucial for survival. Referred to as a forward model, this simulation capability plays an important role in motor control. In this context, the motor command is sent to the forward model in parallel with its actual execution. The results of the actual and simulated execution are then compared, and the consequent error signal is used to correct the movement. Here we demonstrate how the iCub robot can (a) accumulate experience in the generation of action within its Autobiographical memory (ABM), (b) consolidate this experience encoded in the ABM memory to populate a semantic memory whose content can then be used to (c) simulate the results of actions. This simulation can be used as a traditional forward model in the control sense, but it can also be used in more extended time as a mental simulation or mental image that can contribute to higher cognitive function such as planning future actions, or even imagining the mental state of another agent. We present the results of the use of such a mental imagery capability in a forward modeling for motor control task, and a classical mentalizing task. Part of the novelty of this research is that the information that is used to allow the simulation of action is purely acquired from experience. In this sense we can say that the simulation capability is embodied in the sensorimotor experience of the iCub robot.

Keywords

Humanoid robot perception action mental simulation mental imagery forward model 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Vernon, D., Metta, G., Sandini, G.: A survey of artificial cognitive systems: Implications for the autonomous development of mental capabilities in computational agents. IEEE Transactions on Evolutionary Computation 11, 151–180 (2007)CrossRefGoogle Scholar
  2. 2.
    Bubic, A., Von Cramon, D.Y., Schubotz, R.I.: Prediction, cognition and the brain. Frontiers in Human Neuroscience 4 (March 22, 2010)Google Scholar
  3. 3.
    Friston, K.: A theory of cortical responses. Philos. Trans. R. Soc. Lond. B Biol. Sci. 360, 815–836 (2005)CrossRefGoogle Scholar
  4. 4.
    Wolpert, D.M., Ghahramani, Z., Jordan, M.I.: An internal model for sensorimotor integration (1995)Google Scholar
  5. 5.
    Frith, C.D., Frith, U.: Interacting minds–a biological basis. Science 286, 1692–1695 (1999)CrossRefGoogle Scholar
  6. 6.
    Singer, T.: The neuronal basis and ontogeny of empathy and mind reading: review of literature and implications for future research. Neuroscience and Biobehavioral Reviews 30, 855–863 (2006)CrossRefGoogle Scholar
  7. 7.
    Petit, M., Lallee, S., Boucher, J.-D., Pointeau, G., Cheminade, P., Ognibene, D., Chinellato, E., Pattacini, U., Demiris, Y., Metta, G., Dominey, P.F.: The Coordinating Role of Language in Real-Time Multi-Modal Learning of Cooperative Tasks. IEEE Transactions on Autonomous Mental Development (2012)Google Scholar
  8. 8.
    Pointeau, G., Petit, M., Dominey, P.F.: Robot Learning Rules of Games by Extraction of Intrinsic Properties. In: The Sixth International Conference on Advances in Computer-Human Interactions, ACHI 2013, pp. 109–116 (2013)Google Scholar
  9. 9.
    Shirai, Y., Inoue, H.: Guiding a robot by visual feedback in assembling tasks. Pattern Recognition 5, 99–108 (1973)CrossRefGoogle Scholar
  10. 10.
    Onishi, K.H., Baillargeon, R.: Do 15-month-old infants understand false beliefs? Science 308, 255–258 (2005)CrossRefGoogle Scholar
  11. 11.
    Dearden, A., Demiris, Y.: Learning forward models for robots. In: International Joint Conference on Artificial Intelligence, p. 1440 (2005)Google Scholar
  12. 12.
    Metta, G., Fitzpatrick, P.: Early integration of vision and manipulation. Adaptive Behavior 11, 109–128 (2003)CrossRefGoogle Scholar
  13. 13.
    Metzinger, T.: Empirical perspectives from the self-model theory of subjectivity: a brief summary with examples. In: Banerjee, R., Chakrabarti, B. (eds.) Models of Brain and Mind: Physical, Computational and Psychological Approaches, vol. 168, p. 215 (2008)Google Scholar
  14. 14.
    Neisser, U.: The roots of self-knowledge: perceiving self, it, and thou. Ann. N. Y. Acad. Sci. 818, 18–33 (1997)CrossRefGoogle Scholar
  15. 15.
    Metzinger, T.: Being no one: The self-model theory of subjectivity. Bradford Books (2004)Google Scholar
  16. 16.
    Barsalou, L.W.: Simulation, situated conceptualization, and prediction. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 364, 1281–1289 (2009)CrossRefGoogle Scholar
  17. 17.
    Madden, C., Hoen, M., Dominey, P.F.: A cognitive neuroscience perspective on embodied language for human-robot cooperation. Brain Lang. 112, 180–188 (2010)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Gregoire Pointeau
    • 1
  • Maxime Petit
    • 1
  • Peter Ford Dominey
    • 1
  1. 1.Robot Cognition LaboratoryINSERM U846BronFrance

Personalised recommendations