mlsanityData to 7 Oct 2026

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

arXiv 2603.02783 ↗First posted 3 Mar 2026cs.RO cs.LG cs.MAcited by 0

On the map: Offline RL and imitation learning from data → Imitation learning methods

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.

Key facts. No citing papers in the map yet (data through 7 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.

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10 of 29 references.

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Same direction, newest first

  1. 2025R2BC: Multi-Agent Imitation Learning from Single-Agent Demonstrations20 Oct
  2. 2022imitation: Clean Imitation Learning Implementations22 Nov

Other references · 8

  1. 2017Proximal Policy Optimization Algorithmscited by 14,441
  2. 2016Generative Adversarial Imitation Learningcited by 1,576
  3. 2016Inverse Reinforcement Learning in Swarm Systemscited by 18
  4. 2016Turing learning: a metric-free approach to inferring behavior and its application to swarmscited by 4
  5. 2019Clone Swarms: Learning to Predict and Control Multi-Robot Systems by Imitationcited by 4
  6. 2023Show me what you want: Inverse reinforcement learning to automatically design robot swarms by demonstrationcited by 1
  7. 2024ROS2swarm - A ROS 2 Package for Swarm Robot Behaviorscited by 1
  8. 2024Learning to Imitate Spatial Organization in Multi-robot Systemscited by 1

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  1. 2020ADAIL: Adaptive Adversarial Imitation Learning
  2. 2024Deep Generative Models in Robotics: A Survey on Learning from Multimodal Demonstrations
  3. 2019Sample-efficient Adversarial Imitation Learning from Observation
  4. 2018Generative Adversarial Imitation from Observation
  5. 2022Versatile Skill Control via Self-supervised Adversarial Imitation of Unlabeled Mixed Motions
  6. 2018Multi-Agent Generative Adversarial Imitation Learning
  7. 2022Diverse Imitation Learning via Self-Organizing Generative Models
  8. 2025SD2AIL: Adversarial Imitation Learning from Synthetic Demonstrations via Diffusion Models