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Journal of Robotics
This paper presents an efficient technique for a self-learning dynamic walk for a quadrupedal robot. The cost function for such a task is typically complicated, and the number of parameters to be optimized is high. Therefore, a simple technique for optimization is of importance. We apply a genetic algorithm (GA) which uses real experimental data rather than simulations to evaluate the fitness of a tested gait. The algorithm actively optimizes 12 of the robot's dynamic walking parameters. Thesedoi:10.1155/2020/8051510 fatcat:h53pnf34qnbdzkmyywe4t3i4ty