  Trajectory optimization (TO) is one of the most powerful tools for generating
feasible motions for humanoid robots. However, including uncertainties and
stochasticity in the TO problem to generate robust motions can easily lead to
an interactable problem. Furthermore, since the models used in the TO have
always some level of abstraction, it is hard to find a realistic set of
uncertainty in the space of abstract model. In this paper we aim at leveraging
a sample-efficient learning technique (Bayesian optimization) to robustify
trajectory optimization for humanoid locomotion. The main idea is to use
Bayesian optimization to find the optimal set of cost weights which compromises
performance with respect to robustness with a few realistic
simulation/experiment. The results show that the proposed approach is able to
generate robust motions for different set of disturbances and uncertainties.
