Trajectory representation by nonlinear scaling of dynamic movement primitives
Trajectory representation by nonlinear scaling of dynamic movement primitives
复制标题
通过动态运动基元的非线性缩放来表示轨迹
DOI：
10.1109/iros.2016.7759695
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发表时间：
2016
期刊：
影响因子：
--
通讯作者：
J. Morimoto
中科院分区：
文献类型：
--
作者：
A. Ude;Rok Vuga;B. Nemec;J. Morimoto
一个有效的机器人轨迹表示应该编码所需运动的所有相关方面。对于运动学表示，这意味着必须指定轨迹的空间路线及其速度曲线。动态运动基元（dynamic movement primitives，缩写为MRM）的概念提供了一种运动学表示，它完全指定了运动的这两个方面。然而，它们并没有在双线性表示中彼此分离。当在运动识别和技能学习算法内比较具有显著速度变化的运动时，这可能是有问题的。在这样的比较中，区分运动的空间和时间方面通常是重要的。在本文中，我们提出了一种基于动态运动基元的新表示，其中空间和时间方面得到了很好的分离。我们证明了所提出的表示方法对机器人技能和运动识别的统计学习的有效性，并将其性能与标准DMP进行了比较，其中运动的时间和空间方面交织在一起。
An effective robot trajectory representation should encode all relevant aspects of the desired motion. For kinematic representations, this means that both the spatial course of the trajectory and its speed profile must be specified. The concept of dynamic movement primitives (DMP) provides a kinematic representation that fully specifies these two aspects of motion. They are, however, not separated from each other within the DMP representation. This can be problematic when movements with significant speed variations are compared within movement recognition and skill learning algorithms. In such comparisons it is often important to distinguish between the spatial and temporal aspects of motion. In this paper we propose a new representation based on dynamic movement primitives, where spatial and temporal aspects are well separated. We demonstrate the effectiveness of the proposed representation for statistical learning of robot skills and movement recognition and compare the performance with standard DMPs, where temporal and spatial aspects of motion are intertwined.