Towards Learning Strategies and Exploration Patterns for Feature Perception

Daniel Lewkowicz, Alexandros Giagkos, Patricia Shaw, Suresh Kumar, Mark Lee, Qiang Shen

Research output: Contribution to conferencePaperpeer-review

Abstract

During infancy, infants spend a lot of time visually exploring the scene around them. Over the first year of life, the level of detail that can be perceived visually increases significantly. In this study, the ability to perceive areas of interest w.r.t. human developmental change in vision, specifically acuity and field of view over the first year of life, is investigated. Two scenarios, namely learning through a series of developmental changes and learning without any constraints, shed light on how a humanoid robot scaffolds learning of interesting areas in the scene through different emergent exploratory behaviours. Divergence/convergence in features is reported, demonstrating a potential to be used at a higher level of understanding. Staged strategies with early sensory constraints and exploratory behaviour based on “similarity searches” improve the quality of acquired features and may be used as a mechanism for better on-line learning of objects knowledge.
Original languageEnglish
Publication statusPublished - 19 Sept 2016
EventICDL-EpiRob 2016, 6th Joint IEEE International Conference on Developmental Learning and Epigenetic Robotics - ETIS, Cergy-Pontoise, Paris, France
Duration: 19 Sept 201622 Sept 2016
Conference number: 6
http://icdl-epirob.org

Conference

ConferenceICDL-EpiRob 2016, 6th Joint IEEE International Conference on Developmental Learning and Epigenetic Robotics
Abbreviated titleICDL-EpiRob 2016
Country/TerritoryFrance
CityParis
Period19 Sept 201622 Sept 2016
Internet address

Keywords

  • developmental learning
  • Feature perception
  • Robotics

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