Advertisement

Exploring Uncertainty and Movement in Categorical Perception Using Robots

  • Nathaniel Powell
  • Josh Bongard
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9921)

Abstract

Cognitive agents are able to perform categorical perception through physical interaction (active categorical perception; ACP), or passively at a distance (distal categorical perception; DCP). It is possible that the former scaffolds the learning of the latter. However, it is unclear whether ACP indeed scaffolds DCP in humans and animals, nor how a robot could be trained to likewise learn DCP from ACP. Here we demonstrate a method for doing so which involves uncertainty: robots are trained to perform ACP when uncertain and DCP when certain. We found evidence in these trials that suggests such scaffolding may be occurring: Early during training, robots moved objects to reduce uncertainty as to their class (ACP), but later in training, robots exhibited less action and less class uncertainty (DCP). Furthermore, we demonstrate that robots trained in such a manner are more competent at categorizing novel objects than robots trained to categorize in other ways.

Keywords

Uncertainty Active categorical perception Robotics 

Notes

Acknowledgments

This work was supported by the National Science Foundation under projects PECASE-0953837 and INSPIRE-1344227.

References

  1. 1.
    Beer, R.D.: The dynamics of active categorical perception in an evolved model agent. Adapt. Behav. 11(4), 209–243 (2003)CrossRefGoogle Scholar
  2. 2.
    Bongard, J.: The utility of evolving simulated robot morphology increases with task complexity for object manipulation. Artif. Life 16(3), 201–223 (2010)CrossRefGoogle Scholar
  3. 3.
    Brooks, R.A.: Elephants don’t play Chess. Robot. Auton. Syst. 6(1), 3–15 (1990)CrossRefGoogle Scholar
  4. 4.
    Hauser, H., Ijspeert, A.J., Füchslin, R.M., Pfeifer, R., Maass, W.: Towards a theoretical foundation for morphological computation with compliant bodies. Biol. Cybern. 105(5–6), 355–370 (2011)MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Lungarella, M., Sporns, O.: Information self-structuring: key principle for learning and development. In: Proceedings of the International Conference on Development and Learning, pp. 25–30. IEEE (2005)Google Scholar
  6. 6.
    Paul, C.: Morphological computation: a basis for the analysis of morphology and control requirements. Robot. Auton. Syst. 54(8), 619–630 (2006)CrossRefGoogle Scholar
  7. 7.
    Pfeifer, R., Bongard, J.: How the Body Shapes the Way We Think: A New View of Intelligence. MIT Press, Cambridge (2006)Google Scholar
  8. 8.
    Pfeifer, R., Iida, F., Lungarella, M.: Cognition from the bottom up: on biological inspiration, body morphology, and soft materials. Trends Cogn. Sci. 18(8), 404–413 (2014)CrossRefGoogle Scholar
  9. 9.
    Schmidt, M., Lipson, H.: Age-fitness pareto optimization. In: Riolo, R., McConaghy, T., Vladislavleva, E. (eds.) Genetic Programming Theory and Practice VIII, vol. 8, pp. 129–146. Springer, New York (2011)CrossRefGoogle Scholar
  10. 10.
    Seung, H.S., Opper, M., Sompolinsky, H.: Query by committee. In: Proceedings of the Fifth Annual Workshop on Computational Learning Theory, pp. 287–294. ACM (1992)Google Scholar
  11. 11.
    Tuci, E., Massera, G., Nolfi, S.: Active categorical perception of object shapes in a simulated anthropomorphic robotic arm. IEEE Trans. Evol. Comput. 14(6), 885–899 (2010)CrossRefGoogle Scholar
  12. 12.
    Zieba, K., Bongard, J.: An embodied approach for evolving robust visual classifiers. In: Proceedings of the Genetic and Evolutionary Computation Conerence, pp. 201–208. ACM (2015)Google Scholar

Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of VermontBurlingtonUSA

Personalised recommendations