A Curious Vision System for Autonomous and Cumulative Object Learning

  • Pramod ChandrashekhariahEmail author
  • Gabriele Spina
  • Jochen Triesch
Conference paper
Part of the Communications in Computer and Information Science book series (CCIS, volume 458)


We introduce a fully autonomous active vision system that explores its environment and learns visual representations of objects in the scene. The system design is motivated by the fact that infants learn internal representations of the world without much human assistance. Inspired by this, we build a curiosity driven system that is drawn towards locations in the scene that provide the highest potential for learning. In particular, the attention on a stimulus in the scene is related to the improvement in its internal model. This makes the system learn dynamic changes of object appearance in a cumulative fashion. We also introduce a self-correction mechanism in the system that rectifies situations where several distinct models have been learned for the same object or a single model has been learned for adjacent objects. We demonstrate through experiments that the curiosity-driven learning leads to a higher learning speed and improved accuracy.


Active vision Unsupervised learning Autonomous vision system Vision for robotics Humanoid robot Icub Object recognition Visual attention Stereo vision Intrinsic motivation 



This work was supported by the BMBF Project “Bernstein Fokus: Neurotechnologie Frankfurt, FKZ 01GQ0840” and by the “IM-CLeVeR - Intrinsically Motivated Cumulative Learning Versatile Robots” project, FP7-ICT-IP-231722. We thank Richard Veale, Indiana University for providing the code on saliency.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Pramod Chandrashekhariah
    • 1
    Email author
  • Gabriele Spina
    • 1
  • Jochen Triesch
    • 1
  1. 1.Frankfurt Institute for Advanced Studies (FIAS)Johann Wolfgang Goethe UniversityFrankfurt am MainGermany

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