# Online Goal Recognition using Path Signature and Dynamic Time Warping

arXiv: https://arxiv.org/abs/2605.07736 · HTML: https://mlsanity.com/paper/2605.07736/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 13 of its 36 references that are in the map.
- First posted on arXiv 8 May 2026.

## Builds on (most cited first, 13 of 13)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Curiosity-driven Exploration by Self-supervised Prediction](https://mlsanity.com/paper/1705.05363/) (2017)
- [A Primer on the Signature Method in Machine Learning](https://mlsanity.com/paper/1603.03788/) (2016)
- [Classical Planning in Deep Latent Space: Bridging the Subsymbolic-Symbolic Boundary](https://mlsanity.com/paper/1705.00154/) (2017)
- [Signatory: differentiable computations of the signature and logsignature transforms, on both CPU and GPU](https://mlsanity.com/paper/2001.00706/) (2020)
- [Landmark-Based Approaches for Goal Recognition as Planning](https://mlsanity.com/paper/1904.11739/) (2019)
- [Optimal Cost Design for Model Predictive Control](https://mlsanity.com/paper/2104.11353/) (2021)
- [Heuristic Online Goal Recognition in Continuous Domains](https://mlsanity.com/paper/1709.09839/) (2017)
- [Goal Recognition as Reinforcement Learning](https://mlsanity.com/paper/2202.06356/) (2022)
- [An LP-Based Approach for Goal Recognition as Planning](https://mlsanity.com/paper/1905.04210/) (2019)
- [Real-time goal recognition using approximations in Euclidean space](https://mlsanity.com/paper/2307.07876/) (2023)
- [Explorative Imitation Learning: A Path Signature Approach for Continuous Environments](https://mlsanity.com/paper/2407.04856/) (2024)
- [GRAIL: Goal Recognition Alignment through Imitation Learning](https://mlsanity.com/paper/2602.14252/) (2026)

## Abstract (arXiv)

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.