| Summary: | Controlling a humanoid robot with its typical many degrees of freedom is a complex task, and many methods have been proposed to solve the problem of humanoid locomotion. In this work, we generate a gait for a Hitec Robonova-I robot using a model-free approach, where fairly simple parameterized models, based on truncated Fourier series, are applied to generate joint angular trajectories. To find a parameter set that generates a fast and stable walk, optimization algorithms were used, specifically a genetic algorithm and particle swarm optimization. The optimization process was done in simulation first, and the learned walk was then adapted to the real robot. The simulated model of the Robonova-I was made using the USARSim simulator, and tests made to evaluate the resulting walks verified that the best walk obtained is faster than the ones publicly available for the Robonova-I. Later, to provide an additional validation, the same process was carried out for the simulated Nao from the RoboCup 3D Soccer Simulation League. Again, the resulting walk was fast and stable, overcoming the speed of the publicly available magma-AF base team.
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