Abstract
Clustering of motion trajectories is highly relevant for human-robot interactions as it allows the anticipation of human motions, fast reaction to those, as well as the recognition of explicit gestures. Further, it allows automated analysis of recorded motion data. Many clustering algorithms for trajectories build upon distance metrics that are based on pointwise Euclidean distances. However, our work indicates that focusing on salient characteristics is often sufficient. We present a novel distance measure for motion plans consisting of state and control trajectories that is based on a compressed representation built from their main features. This approach allows a flexible choice of feature classes relevant to the respective task. The distance measure is used in agglomerative hierarchical clustering. We compare our method with the widely used dynamic time warping algorithm on test sets of motion plans for the Furuta pendulum and the Manutec robot arm and on real-world data from a human motion dataset. The proposed method demonstrates slight advantages in clustering and strong advantages in runtime, especially for long trajectories.
Abstract (translated)
运动轨迹聚类对于人机交互具有高度相关性，因为它允许预测人类动作、快速反应以及识别明确的手势。此外，它允许对记录的运动数据进行自动分析。许多轨迹聚类算法基于基于点间欧氏距离的距离度量。然而，我们的工作表明，关注显著特征通常是足够的。我们提出了一种基于主要特征的压缩表示的运动计划距离度量，为人机交互任务提供了一种灵活的特征类选择方式。该方法用于聚类分层。我们将我们的方法与用于 furua 摆和 manutec 机器人手臂的运动计划广泛使用的动态时间扭曲算法以及来自人类运动数据集的真实世界数据进行比较。与聚类算法相比，我们的方法在聚类方面略微优势，在运行时具有明显优势，特别是对于长轨迹。
URL
https://arxiv.org/abs/2404.17269
