# Environment induced emergence of collective behaviour in evolving swarms with limited sensing

arXiv: https://arxiv.org/abs/2203.11585 · HTML: https://mlsanity.com/paper/2203.11585/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 2 of its 41 references that are in the map.
- First posted on arXiv 22 Mar 2022.

## Builds on (most cited first, 2 of 2)

- [Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learning](https://mlsanity.com/paper/2108.10470/) (2021)
- [Gait-learning with morphologically evolving robots generated by L-system](https://mlsanity.com/paper/2107.08249/) (2021)

## Built on by (most cited first, 2 of 2)

- [Emergence of specialized Collective Behaviors in Evolving Heterogeneous Swarms](https://mlsanity.com/paper/2402.04763/) (2024)
- [Emergent Heterogeneous Swarm Control Through Hebbian Learning](https://mlsanity.com/paper/2507.11566/) (2025)

## Abstract (arXiv)

Designing controllers for robot swarms is challenging, because human developers have typically no good understanding of the link between the details of a controller that governs individual robots and the swarm behavior that is an indirect result of the interactions between swarm members and the environment. In this paper we investigate whether an evolutionary approach can mitigate this problem. We consider a very challenging task where robots with limited sensing and communication abilities must follow the gradient of an environmental feature and use Differential Evolution to evolve a neural network controller for simulated robots. We conduct a systematic study to measure the flexibility and scalability of the method by varying the size of the arena and number of robots in the swarm. The experiments confirm the feasibility of our approach, the evolved robot controllers induced swarm behavior that solved the task. We found that solutions evolved under the harshest conditions (where the environmental clues were the weakest) were the most flexible and that there is a sweet spot regarding the swarm size. Furthermore, we observed collective motion of the swarm, showcasing truly emergent behavior that was not represented in- and selected for during evolution.