Learning visual stabilization reflexes in robots with moving eyes

This work addresses the problem of learning stabilization reflexes in robots with moving eyes. Most essential in achieving efficient visual stabilization is the exploitation/integration of different motion related sensory information. In our robot, self-motion is measured inertially with an artifici...

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Bibliographic Details
Published in:Neurocomputing (Amsterdam) 2002-10, Vol.48 (1), p.323-337
Main Authors: Panerai, F., Metta, G., Sandini, G.
Format: Article
Language:English
Subjects:
Artificial neural networks
Image stabilization
Inertial information
Oculo-motor reflexes
Optic flow
Robotics
Sensori-motor maps
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Summary:This work addresses the problem of learning stabilization reflexes in robots with moving eyes. Most essential in achieving efficient visual stabilization is the exploitation/integration of different motion related sensory information. In our robot, self-motion is measured inertially with an artificial vestibular system and visually using optic flow algorithms. The first sensory system provides short latency measurements of rotations and translations of the robot's head, the second, a delayed estimate of the motion across the image plane. A self-tuning neural network learns to combine these two measurements and generates oculo-motor compensatory behaviors that stabilize the visual scene. We describe the network architecture and the learning scheme. The stabilization performance is evaluated quantitatively using direct measurements on the image plane.
ISSN:0925-2312
1872-8286
DOI:10.1016/S0925-2312(01)00645-2