Speeding-Up the Learning of Saccade Control

  • Marco Antonelli
  • Angel J. Duran
  • Eris Chinellato
  • Angel P. Del Pobil
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8064)


A saccade is a ballistic eye movement that allows the visual system to bring the target in the center of the visual field. For artificial vision systems, as in humanoid robotics, performing such a movement requires to know the intrinsic parameters of the camera. Parameters can be encoded in a bio-inspired fashion by a non-parametric model, that is trained during the movement of the camera. In this work, we propose a novel algorithm to speed-up the learning of saccade control in a goal-directed manner. During training, the algorithm computes the covariance matrix of the transformation and uses it to choose the most informative visual feature to gaze next. Results on a simulated model and on a real setup show that the proposed technique allows for a very efficient learning of goal-oriented saccade control.


Ground Truth Root Mean Square Exploration Behavior Humanoid Robot Radial Basis Function Network 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Marco Antonelli
    • 1
  • Angel J. Duran
    • 1
  • Eris Chinellato
    • 2
  • Angel P. Del Pobil
    • 1
  1. 1.Robotic Intelligence LabUniversitat Jaume ISpain
  2. 2.Imperial College LondonUK

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