# Discovery and Deployment of Emergent Robot Swarm Behaviors via Representation Learning and Real2Sim2Real Transfer

arXiv: https://arxiv.org/abs/2502.15937 · HTML: https://mlsanity.com/paper/2502.15937/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Builds on 13 of its 47 references that are in the map.
- First posted on arXiv 21 Feb 2025.

## Builds on (most cited first, 13 of 13)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Unsupervised Data Augmentation for Consistency Training](https://mlsanity.com/paper/1904.12848/) (2019)
- [Learning Representations by Maximizing Mutual Information Across Views](https://mlsanity.com/paper/1906.00910/) (2019)
- [Large Batch Training of Convolutional Networks](https://mlsanity.com/paper/1708.03888/) (2017)
- [Evolution of Swarm Robotics Systems with Novelty Search](https://mlsanity.com/paper/1304.3362/) (2013)
- [Evolution of Collective Behaviors for a Real Swarm of Aquatic Surface Robots](https://mlsanity.com/paper/1511.03154/) (2015)
- [HeRo 2.0: A Low-Cost Robot for Swarm Robotics Research](https://mlsanity.com/paper/2202.12391/) (2022)
- [Leveraging Human Feedback to Evolve and Discover Novel Emergent Behaviors in Robot Swarms](https://mlsanity.com/paper/2305.16148/) (2023)
- [Flocking-Segregative Swarming Behaviors using Gibbs Random Fields](https://mlsanity.com/paper/2104.10814/) (2021)
- [Chemistry-Inspired Pattern Formation with Robotic Swarms](https://mlsanity.com/paper/2206.03388/) (2022)
- [Zespol: A Lightweight Environment for Training Swarming Agents](https://mlsanity.com/paper/2306.17744/) (2023)
- [Exploring Behavior Discovery Methods for Heterogeneous Swarms of Limited-Capability Robots](https://mlsanity.com/paper/2310.16941/) (2023)

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

- [R2BC: Multi-Agent Imitation Learning from Single-Agent Demonstrations](https://mlsanity.com/paper/2510.18085/) (2025)
- [SyncSBC: Decentralized Swarm Behavior Prediction for Synchronized Autonomous Control](https://mlsanity.com/paper/2608.06587/) (2026)

## Abstract (arXiv)

Given a swarm of limited-capability robots, we seek to automatically discover the set of possible emergent behaviors. Prior approaches to behavior discovery rely on human feedback or hand-crafted behavior metrics to represent and evolve behaviors and only discover behaviors in simulation, without testing or considering the deployment of these new behaviors on real robot swarms. In this work, we present Real2Sim2Real Behavior Discovery via Self-Supervised Representation Learning, which combines representation learning and novelty search to discover possible emergent behaviors automatically in simulation and enable direct controller transfer to real robots. First, we evaluate our method in simulation and show that our proposed self-supervised representation learning approach outperforms previous hand-crafted metrics by more accurately representing the space of possible emergent behaviors. Then, we address the reality gap by incorporating recent work in sim2real transfer for swarms into our lightweight simulator design, enabling direct robot deployment of all behaviors discovered in simulation on an open-source and low-cost robot platform.