ガウス混合モデルによるロボット運動の幾何力学の学習
Learning the Geometric Mechanics of Robot Motion Using Gaussian Mixtures
ガウス混合モデルを用いて幾何力学の運動写像を学習し、従来法より高精度な予測と任意の運動データへの適用を可能にした。
著者: Ruizhen Hu, Shai Revzen
分類: cs.RO
原文アブストラクト
Data-driven models of robot motion constructed using principles from Geometric Mechanics have been shown to produce useful predictions of robot motion for a variety of robots. For robots with a useful number of DoF, these geometric mechanics models can only be constructed in the neighborhood of a gait. Here we show how Gaussian Mixture Models (GMM) can be used as a form of manifold learning that learns the structure of the Geometric Mechanics "motility map" and demonstrate: [i] a sizable improvement in prediction quality when compared to the previously published methods; [ii] a method that can be applied to any motion dataset and not only periodic gait data; [iii] a way to pre-process the data-set to facilitate extrapolation in places where the motility map is known to be linear. Our results can be applied anywhere a data-driven geometric motion model might be useful.
