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.
面向机器人集群的生成对抗模仿学习：从人类演示与训练策略中学习 / Generative adversarial imitation learning for robot swarms: Learning from human demonstrations and trained policies
这篇论文提出了一个基于生成对抗模仿学习的框架，让机器人集群能够通过观察人类演示或已有策略的演示来学习集体行为，并在仿真和真实机器人实验中成功复现了与演示性能相当、视觉上可识别的群体行为。
源自 arXiv: 2603.02783
