论文标题
几何感力的动态运动原语
Geometry-aware Dynamic Movement Primitives
论文作者
论文摘要
在许多机器人控制问题中，诸如刚度和阻尼矩阵和可操作性椭圆形的因素自然表示为对称阳性定位（SPD）矩阵，这些矩阵捕获了这些因素的特定几何特征。但是，典型的学习技能模型（例如动态运动原始）（DMP）不能直接用以SPD矩阵表示的数量来直接使用，因为它们仅限于欧几里得空间中的数据。 在本文中，我们提出了一个新颖且数学上的原则性框架，该框架使用Riemannian指标重新制定DMP，以便所得的配方可以在SPD歧管中使用SPD数据来运行。对该方法的评估表明，DMP的有益特性，例如操作过程中目标的变化也适用于拟议的配方。
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.
