シミュレーションと実機実験を組み合わせた二足歩行安定化のベイズ最適化
Combining Simulations and Real-robot Experiments for Bayesian Optimization of Bipedal Gait Stabilization
シミュレーションと実機実験を併用し、エントロピーに基づくベイズ最適化で二足歩行の安定化パラメータを効率的に学習する手法を提案し、ヒューマノイドロボットで有効性を示した。
著者: Diego Rodriguez, André Brandenburger, Sven Behnke
分類: cs.RO
原文アブストラクト
Walking controllers often require parametrization which must be tuned according to some cost function. To estimate these parameters, simulations can be performed which are cheap but do not fully represent reality. Real-robot experiments, on the other hand, are more expensive and lead to hardware wear-off. In this paper, we propose an approach for combining simulations and real experiments to learn gait stabilization parameters. We use a Bayesian optimization method which selects the most informative points in parameter space to evaluate based on the entropy of the cost function to optimize. Experiments with the igus Humanoid Open Platform demonstrate the effectiveness of our approach.
