Bayesian Optimization for Learning Gaits under Uncertainty An experimental comparison on a dynamic bipedal walker

Designing gaits and corresponding control policies is a key challenge in robot locomotion. Even with a viable controller parameterization, finding nearoptimal parameters can be daunting. Typically, this kind of parameter optimization requires specific expert knowledge and extensive robot experiments. Automatic black-box gait optimization methods greatly reduce the need for human expertise and time-consuming design processes. Many different approaches for automatic gait optimization have been suggested to date, such as grid search and evolutionary algorithms. In this article, we thoroughly discuss multiple of these optimization methods in the context of automatic gait optimization. Moreover, we extensively evaluate Bayesian optimization, a model-based approach to blackbox optimization under uncertainty, on both simulated problems and real robots. This evaluation demonstrates that Bayesian optimization is particularly suited for robotic applications, where it is crucial to find a good set of gait parameters in a small number of experiments.