In the model proposed in this work, the character has 3 DOFs (3 pairs of muscles) for each leg, and the dimensions of the neuron and muscle parameters are 3 and 8 respectively for each neuron-muscle pair. The symmetry between left and right legs first reduces the dimensions of motor and muscle parameters to half. Together with the synaptic weights in the central neural system, the total dimension of parameters is 806. This far exceeds the dimensions of parameter space in existing works11,12,28 and the capability of the algorithm as declared by the author29. To optimize in the parameter space with such complexity, we resolve this challenge with a two-stage strategy with the algorithm of Covariance Matrix Adaptation (CMA). First, we optimize the input currents Is to the motor neurons and muscle parameters for the standard case of walking straight forward. Next, we optimize the offset currents from the central neurons, which are added to the standard case Is and generate the versatile motor skills including walking on curve trajectories and uneven terrains.