# Learning NEAT Emergent Behaviors in Robot Swarms

arXiv: https://arxiv.org/abs/2309.14663 · HTML: https://mlsanity.com/paper/2309.14663/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 28 references that are in the map.
- First posted on arXiv 26 Sep 2023.

## Builds on (most cited first, 4 of 4)

- [Robot Operating System 2: Design, Architecture, and Uses In The Wild](https://mlsanity.com/paper/2211.07752/) (2022)
- [Learning Robot Swarm Tactics over Complex Adversarial Environments](https://mlsanity.com/paper/2109.05663/) (2021)
- [Local Communication Protocols for Learning Complex Swarm Behaviors with Deep Reinforcement Learning](https://mlsanity.com/paper/1709.07224/) (2017)
- [Securing emergent behaviour in swarm robotics](https://mlsanity.com/paper/2102.03148/) (2021)

## Abstract (arXiv)

When researching robot swarms, many studies observe complex group behavior emerging from the individual agents' simple local actions. However, the task of learning an individual policy to produce a desired group behavior remains a challenging problem. We present a method of training distributed robotic swarm algorithms to produce emergent behavior. Inspired by the biological evolution of emergent behavior in animals, we use an evolutionary algorithm to train a population of individual behaviors to produce a desired group behavior. We perform experiments using simulations of the Georgia Tech Miniature Autonomous Blimps (GT-MABs) aerial robotics platforms conducted in the CoppeliaSim simulator. Additionally, we test on simulations of Anki Vector robots to display our algorithm's effectiveness on various modes of actuation. We evaluate our algorithm on various tasks where a somewhat complex group behavior is required for success. These tasks include an Area Coverage task and a Wall Climb task. We compare behaviors evolved using our algorithm against designed policies, which we create in order to exhibit the emergent behaviors we desire.