Abstract— The generation of optimal solutions for robotic bipedal walking using whole-body dynamics is well known to have a big computational cost, preventing online trajectory generation for optimal control methods that satisfy Pontryagin's Principle and its Conditions of Optimality. However, bipedal walking has fundamental kinematic and dynamic characteristics that shape different solutions for different parameters in similar curves. Such characteristics were previously defined in biomechanical literature as movement primitives. Recently, studies generated parametrized optimal solutions by performing regressions from training data into movement primitives using Machine Learning. The learned solutions were very close to the actual optimal solution. This study evaluates the precision of such strategy by optimizing the gait of a 6 degrees of freedom planar robot using different Cost Functions, in order to understand if the precision of Machine Learning in recreating optimal solutions is impacted by what is being optimized.

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