# How Swarms Differ: Challenges in Collective Behaviour Comparison

arXiv: https://arxiv.org/abs/2602.13016 · HTML: https://mlsanity.com/paper/2602.13016/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data.
- First posted on arXiv 13 Feb 2026.

## Placed in

- Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/)

## Abstract (arXiv)

Collective behaviours often need to be expressed through numerical features, e.g., for classification or imitation learning. This problem is often addressed by proposing an ad-hoc feature set for a particular swarm behaviour context, usually without further consideration of the solution's resilience outside of the conceived context. Yet, the development of automatic methods to design swarm behaviours is dependent on the ability to measure quantitatively the similarity of swarm behaviours. Hence, we investigate the impact of feature sets for collective behaviours. We select swarm feature sets and similarity measures from prior swarm robotics works, which mainly considered a narrow behavioural context and assess their robustness. We demonstrate that the interplay of feature set and similarity measure makes some combinations more suitable to distinguish groups of similar behaviours. We also propose a self-organised map-based approach to identify regions of the feature space where behaviours cannot be easily distinguished.