Publication
Clustering of Motion Trajectories by a Distance Measure Based on Semantic Features
Christoph Zelch; Jan Peters; Oskar von Stryk
In: 22nd IEEE-RAS International Conference on Humanoid Robots, Humanoids 2023, Austin, TX, USA, December 12-14, 2023. IEEE-RAS International Conference on Humanoid Robots (Humanoids), Pages 1-8, IEEE, 2023.
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