Bayesian optimization for learning gaits under uncertainty

Abstract

Designing gaits and corresponding control policies is a key challenge in robot locomotion. Even with a viable controller parametrization, finding near-optimal 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… (More)
DOI: 10.1007/s10472-015-9463-9

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