Universal approximation using radial-basis-function networks
Neural Computation
Kalman Filtering and Neural Networks
Kalman Filtering and Neural Networks
Adaptive Behavior
Learning visuomotor transformations for gaze-control and grasping
Biological Cybernetics
Learning Saccadic Gaze Control via Motion Prediciton
CRV '07 Proceedings of the Fourth Canadian Conference on Computer and Robot Vision
Spatial transformations in the parietal cortex using basis functions
Journal of Cognitive Neuroscience
A developmental algorithm for ocular-motor coordination
Robotics and Autonomous Systems
Implicit Sensorimotor Mapping of the Peripersonal Space by Gazing and Reaching
IEEE Transactions on Autonomous Mental Development
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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.