Online Goal Recognition using Path Signature and Dynamic Time Warping

TL;DR

This paper presents a novel online goal recognition method that uses path signatures to encode trajectories efficiently, improving accuracy and efficiency over existing approaches.

Contribution

It introduces the use of path signatures from rough path theory for online goal recognition, a novel application in this domain.

Findings

Outperforms state-of-the-art methods in predictive accuracy

Enhances online planning efficiency

Remains competitive in offline scenarios

Abstract

Online goal recognition in continuous domains poses two central challenges: efficiently encoding large trajectories and effectively comparing them. Recent work addresses these challenges by using custom state-space representations and metrics to compare observations against hypotheses. However, these approaches often overlook well-established encoding techniques used in other domains that offer substantial advantages. This paper introduces a novel method for online goal recognition that leverages path signatures, a compact, expressive representation of rough path theory that efficiently captures key semantic features of trajectories, enabling more meaningful comparisons between them. Experiments show that our method consistently outperforms the state of the art in predictive accuracy and online planning efficiency, while remaining competitive offline.