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.

翻译：运动轨迹聚类在人机交互中具有重要关联性，因为它能够预测人类运动、快速响应这些运动，并识别显式手势。此外，它还能实现记录运动数据的自动化分析。许多轨迹聚类算法基于逐点欧氏距离构建距离度量。然而，我们的研究表明，关注显著特征往往已足够。我们提出一种针对由状态和控制轨迹组成的运动规划的新型距离度量，该度量基于从其主要特征构建的压缩表示。该方法允许灵活选择与特定任务相关的特征类别。该距离度量用于凝聚层次聚类中。我们在Furuta摆、Manutec机械臂的运动规划测试集以及人体运动数据集的实际数据上，将我们的方法与广泛使用的动态时间规整算法进行了比较。所提方法在聚类方面表现出微弱优势，而在运行时间方面具有显著优势，尤其对于长轨迹而言。