Speeding-Up the Learning of Saccade Control
Durán, Angel J.
Del Pobil, Angel P.
|Source||2nd International Conference on Biomimetic and Biohybrid Systems: Living Machines, LM 2013, London, United Kingdom, 29 July-2 August 2013, Code 98028. Lecture Notes in Computer Science: Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics , v. 8064 LNAI, 2013, p. 12-23|
|Summary||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. © 2013 Springer-Verlag Berlin Heidelberg.|
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