# Exploring Behavior Discovery Methods for Heterogeneous Swarms of Limited-Capability Robots

arXiv: https://arxiv.org/abs/2310.16941 · HTML: https://mlsanity.com/paper/2310.16941/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 25 Oct 2023.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Unsupervised Behaviour Discovery with Quality-Diversity Optimisation](https://mlsanity.com/paper/2106.05648/) (2021)
- [Transforming Exploratory Creativity with DeLeNoX](https://mlsanity.com/paper/2103.11715/) (2021)
- [Leveraging Human Feedback to Evolve and Discover Novel Emergent Behaviors in Robot Swarms](https://mlsanity.com/paper/2305.16148/) (2023)
- [Generic Behaviour Similarity Measures for Evolutionary Swarm Robotics](https://mlsanity.com/paper/1304.3393/) (2013)
- [Systematic Derivation of Behaviour Characterisations in Evolutionary Robotics](https://mlsanity.com/paper/1407.0577/) (2014)

## Built on by (most cited first, 1 of 1)

- [Discovery and Deployment of Emergent Robot Swarm Behaviors via Representation Learning and Real2Sim2Real Transfer](https://mlsanity.com/paper/2502.15937/) (2025)

## Abstract (arXiv)

We study the problem of determining the emergent behaviors that are possible given a functionally heterogeneous swarm of robots with limited capabilities. Prior work has considered behavior search for homogeneous swarms and proposed the use of novelty search over either a hand-specified or learned behavior space followed by clustering to return a taxonomy of emergent behaviors to the user. In this paper, we seek to better understand the role of novelty search and the efficacy of using clustering to discover novel emergent behaviors. Through a large set of experiments and ablations, we analyze the effect of representations, evolutionary search, and various clustering methods in the search for novel behaviors in a heterogeneous swarm. Our results indicate that prior methods fail to discover many interesting behaviors and that an iterative human-in-the-loop discovery process discovers more behaviors than random search, swarm chemistry, and automated behavior discovery. The combined discoveries of our experiments uncover 23 emergent behaviors, 18 of which are novel discoveries. To the best of our knowledge, these are the first known emergent behaviors for heterogeneous swarms of computation-free agents. Videos, code, and appendix are available at the project website: https://sites.google.com/view/heterogeneous-bd-methods