# Clone Swarms: Learning to Predict and Control Multi-Robot Systems by Imitation

arXiv: https://arxiv.org/abs/1912.02811 · HTML: https://mlsanity.com/paper/1912.02811/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 5 Dec 2019.

## Built on by (most cited first, 4 of 4)

- [Local Repair of Neural Networks Using Optimization](https://mlsanity.com/paper/2109.14041/) (2021)
- [Understandable Controller Extraction from Video Observations of Swarms](https://mlsanity.com/paper/2209.01118/) (2022)
- [R2BC: Multi-Agent Imitation Learning from Single-Agent Demonstrations](https://mlsanity.com/paper/2510.18085/) (2025)
- [Generative adversarial imitation learning for robot swarms: Learning from human demonstrations and trained policies](https://mlsanity.com/paper/2603.02783/) (2026)

## Abstract (arXiv)

In this paper, we propose SwarmNet -- a neural network architecture that can learn to predict and imitate the behavior of an observed swarm of agents in a centralized manner. Tested on artificially generated swarm motion data, the network achieves high levels of prediction accuracy and imitation authenticity. We compare our model to previous approaches for modelling interaction systems and show how modifying components of other models gradually approaches the performance of ours. Finally, we also discuss an extension of SwarmNet that can deal with nondeterministic, noisy, and uncertain environments, as often found in robotics applications.