意味的特徴に基づく距離尺度による動作軌道のクラスタリング
Clustering of Motion Trajectories by a Distance Measure Based on Semantic Features
動作計画の主要な特徴を圧縮表現し、それに基づく新しい距離尺度で階層的クラスタリングを行う手法を提案し、DTWより高速で同等以上の性能を示した。
著者: Christoph Zelch, Jan Peters, Oskar von Stryk
分類: cs.RO
原文アブストラクト
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.
