  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.
