Using Artificial Neural Networks to Construct a Meta-Model for the Evolution of Gait Patterns
نویسندگان
چکیده
In this paper we suggest a novel method to approximate the fitness function of a genetic programming approach in order to develop fast and stable gait patterns for a quadruped robot. Therefore, gait patterns are classified by so called Signal Space Detectors. We show how a Signal Space Detector can extract information about the reliability of a classification. Finally, we demonstrate how this information can be used to replace conventional time-consuming fitness modules like real-world tournaments or offline simulations.
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