幾何学を考慮した動的運動プリミティブ
Geometry-aware Dynamic Movement Primitives
SPD行列データをリーマン多様体上で扱えるようDMPを再定式化し、剛性や可操作性などの幾何学的特性を学習・制御できるようにした。
著者: Fares J. Abu-Dakka, Ville Kyrki
分類: cs.RO
原文アブストラクト
In many robot control problems, factors such as stiffness and damping matrices and manipulability ellipsoids are naturally represented as symmetric positive definite (SPD) matrices, which capture the specific geometric characteristics of those factors. Typical learned skill models such as dynamic movement primitives (DMPs) can not, however, be directly employed with quantities expressed as SPD matrices as they are limited to data in Euclidean space. In this paper, we propose a novel and mathematically principled framework that uses Riemannian metrics to reformulate DMPs such that the resulting formulation can operate with SPD data in the SPD manifold. Evaluation of the approach demonstrates that beneficial properties of DMPs such as change of the goal during operation apply also to the proposed formulation.
