# Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots

arXiv: https://arxiv.org/abs/1511.03154 · HTML: https://mlsanity.com/paper/1511.03154/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Nov 2015.

## Built on by (most cited first, 5 of 5)

- [Federated Reinforcement Learning for Collective Navigation of Robotic Swarms](https://mlsanity.com/paper/2202.01141/) (2022)
- [Discovery and Deployment of Emergent Robot Swarm Behaviors via Representation Learning and Real2Sim2Real Transfer](https://mlsanity.com/paper/2502.15937/) (2025)
- [Show me what you want: Inverse reinforcement learning to automatically design robot swarms by demonstration](https://mlsanity.com/paper/2301.06864/) (2023)
- [A model-based framework for learning transparent swarm behaviors](https://mlsanity.com/paper/2103.05343/) (2021)
- [SyncSBC: Decentralized Swarm Behavior Prediction for Synchronized Autonomous Control](https://mlsanity.com/paper/2608.06587/) (2026)

## Abstract (arXiv)

Swarm robotics is a promising approach for the coordination of large numbers of robots. While previous studies have shown that evolutionary robotics techniques can be applied to obtain robust and efficient self-organized behaviors for robot swarms, most studies have been conducted in simulation, and the few that have been conducted on real robots have been confined to laboratory environments. In this paper, we demonstrate for the first time a swarm robotics system with evolved control successfully operating in a real and uncontrolled environment. We evolve neural network-based controllers in simulation for canonical swarm robotics tasks, namely homing, dispersion, clustering, and monitoring. We then assess the performance of the controllers on a real swarm of up to ten aquatic surface robots. Our results show that the evolved controllers transfer successfully to real robots and achieve a performance similar to the performance obtained in simulation. We validate that the evolved controllers display key properties of swarm intelligence-based control, namely scalability, flexibility, and robustness on the real swarm. We conclude with a proof-of-concept experiment in which the swarm performs a complete environmental monitoring task by combining multiple evolved controllers.