Generative adversarial imitation learning for robot swarms: Learning from human demonstrations and trained policies
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. Additional...
| Authors | Mattes Kraus, Jonas Kuckling |
|---|---|
| Primary category | cs.RO |
| Published | 3 March 2026 |
| Last updated at source | 3 March 2026 |
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