生成对抗模仿学习在机器人群体中的应用：从人类示范和训练策略中学习
Generative adversarial imitation learning for robot swarms: Learning from human demonstrations and trained policies
AI总结：
本文提出基于生成对抗模仿学习的框架，从人类示范和训练策略中学习机器人群体的集体行为，并在真实机器人上验证了其有效性。
摘要：
在模仿学习中，机器人应从期望行为的示范中学习。大多数 swarm robotics 的模仿学习工作将示范作为现有策略的 rollout 提供。在本工作中，我们提供了一个基于生成对抗模仿学习的框架，旨在从人类示范中学习集体行为。我们的框架在六个不同的任务中进行评估，从手动示范和基于 PPO 训练策略的示范中学习。结果表明，模仿学习过程能够学习到具有定性意义的行为，其表现与提供的示范相当。此外，我们将在 TurtleBot 4 机器人群体上部署所学策略进行真实机器人实验。所展示的行为保留了其视觉可识别的特征，其性能与仿真中的性能相当。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
