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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Beer, R.D.: The dynamics of active categorical perception in an evolved model agent. Adapt. Behav. 11(4), 209–243 (2003)
Bongard, J.: The utility of evolving simulated robot morphology increases with task complexity for object manipulation. Artif. Life 16(3), 201–223 (2010)
Brooks, R.A.: Elephants don’t play Chess. Robot. Auton. Syst. 6(1), 3–15 (1990)
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)
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)
Paul, C.: Morphological computation: a basis for the analysis of morphology and control requirements. Robot. Auton. Syst. 54(8), 619–630 (2006)
Pfeifer, R., Bongard, J.: How the Body Shapes the Way We Think: A New View of Intelligence. MIT Press, Cambridge (2006)
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)
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)
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)
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)
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)
Acknowledgments
This work was supported by the National Science Foundation under projects PECASE-0953837 and INSPIRE-1344227.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this paper
Cite this paper
Powell, N., Bongard, J. (2016). Exploring Uncertainty and Movement in Categorical Perception Using Robots. In: Handl, J., Hart, E., Lewis, P., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds) Parallel Problem Solving from Nature – PPSN XIV. PPSN 2016. Lecture Notes in Computer Science(), vol 9921. Springer, Cham. https://doi.org/10.1007/978-3-319-45823-6_58
Download citation
DOI: https://doi.org/10.1007/978-3-319-45823-6_58
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-45822-9
Online ISBN: 978-3-319-45823-6
eBook Packages: Computer ScienceComputer Science (R0)