Building Representations of Proto-Objects with Exploration of the Effect on Fixation Times

Type Paper
Original languageEnglish
Number of pages8
Publication statusAccepted/In press - 18 Sep 2017
EventICDL-EpiRob 2017, 7th Joint IEEE International Conference on Developmental Learning and Epigenetic Robotics - Lisbon, Portugal
Duration: 18 Sep 201722 Sep 2017
Conference number: 7


ConferenceICDL-EpiRob 2017, 7th Joint IEEE International Conference on Developmental Learning and Epigenetic Robotics
Abbreviated titleICDL-EpiRob 2017
Period18 Sep 201722 Sep 2017
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During development, infants rapidly build models of the world around them, segmenting the visual scene into clusters of features that can be indexed as proto-objects. These proto- objects form the foundation of more specialised object perception later on, but also act as a means for generalising, comparing and recognising similar objects. This paper takes inspiration from psychological studies to present an approach for building representations of proto-objects that can be learned on-line on a robotic platform and used for object recognition. In particular, from our previous studies of infant visual development, we first identify four types of features; brightness, motion, colour and edges, and then apply heuristics to cluster them into proto-object representations. When correlations of the observed features are made, pairs of features are used to construct graphs that encapsulate information of the observed phenomena. By a three- phase experiment we demonstrate the robot’s ability of effectively learn proto-object representations and then, by utilising the graphs, to recognise what is presented to it and report on the impact uncertainties in object recognition have on fixation times.


  • Developmental learning, Proto-objects, Robotics


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