サンプル効率の良い学習によるヒューマノイド歩行のロバスト軌道最適化
Trajectory Optimization for Robust Humanoid Locomotion with Sample-Efficient Learning
ベイズ最適化を用いて軌道最適化のコスト重みを調整し、少ない試行でヒューマノイドのロバストな歩行軌道を生成する手法を提案した。
著者: Majid Khadiv, Mohammad Hasan Yeganegi, S. Ali A. Moosavian, Jia-Jie Zhu, Ludovic Righetti
分類: cs.RO
原文アブストラクト
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.
