Exploring Uncertainty and Movement in Categorical Perception Using Robots

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9921)


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.


Uncertainty Active categorical perception Robotics 



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


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Copyright information

© Springer International Publishing AG 2016

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of VermontBurlingtonUSA

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