# Generative adversarial imitation learning for robot swarms: Learning from human demonstrations and trained policies

arXiv: https://arxiv.org/abs/2603.02783 · HTML: https://mlsanity.com/paper/2603.02783/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Imitation learning methods, one of 13 directions in Offline RL and imitation learning from data.
- Builds on 10 of its 29 references that are in the map.
- First posted on arXiv 3 Mar 2026.

## Placed in

- Offline RL and imitation learning from data / [Imitation learning methods](https://mlsanity.com/offlinerl/imitation-learning-methods/)

## Builds on (most cited first, 10 of 10)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Generative Adversarial Imitation Learning](https://mlsanity.com/paper/1606.03476/) (2016)
- [imitation: Clean Imitation Learning Implementations](https://mlsanity.com/paper/2211.11972/) (2022)
- [Inverse Reinforcement Learning in Swarm Systems](https://mlsanity.com/paper/1602.05450/) (2016)
- [Turing learning: a metric-free approach to inferring behavior and its application to swarms](https://mlsanity.com/paper/1603.04904/) (2016)
- [Clone Swarms: Learning to Predict and Control Multi-Robot Systems by Imitation](https://mlsanity.com/paper/1912.02811/) (2019)
- [Show me what you want: Inverse reinforcement learning to automatically design robot swarms by demonstration](https://mlsanity.com/paper/2301.06864/) (2023)
- [ROS2swarm - A ROS 2 Package for Swarm Robot Behaviors](https://mlsanity.com/paper/2405.02438/) (2024)
- [Learning to Imitate Spatial Organization in Multi-robot Systems](https://mlsanity.com/paper/2407.11592/) (2024)
- [R2BC: Multi-Agent Imitation Learning from Single-Agent Demonstrations](https://mlsanity.com/paper/2510.18085/) (2025)

## Abstract (arXiv)

In imitation learning, robots are supposed to learn from demonstrations of the desired behavior. Most of the work in imitation learning for swarm robotics provides the demonstrations as rollouts of an existing policy. In this work, we provide a framework based on generative adversarial imitation learning that aims to learn collective behaviors from human demonstrations. Our framework is evaluated across six different missions, learning both from manual demonstrations and demonstrations derived from a PPO-trained policy. Results show that the imitation learning process is able to learn qualitatively meaningful behaviors that perform similarly well as the provided demonstrations. Additionally, we deploy the learned policies on a swarm of TurtleBot 4 robots in real-robot experiments. The exhibited behaviors preserved their visually recognizable character and their performance is comparable to the one achieved in simulation.