Cobotic applications require a good knowledge on human behaviour in order to be cleverly, securely and fluidly performed. For example, to make a human and a humanoid robot carry and move a table together, a model of human walking trajectories is essential to make the robot follow or even anticipate the human movements. This paper aims to study the Center of Mass (CoM) path during gait and generate human-like trajectories thanks to an optimal control scheme. It also proposes a metric which allows to assess this model compared to the human behaviour. CoM trajectories during gait of 10 healthy subjects were recorded and analysed as part of this study. Inverse optimal control was used to find the optimal cost function which best fits the model to the measurements. Then, the measurements and the generated data were compared in order to assess the performance of the presented model. Even if the experiments show a great variability in human behaviours, the model presented in this study gives an accurate approximation of the average human walking trajectories. Furthermore, this model gives an approximation of human locomotion good enough to improve cobotic tasks allowing a humanoid robot to anticipate human behaviour.