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反事实奖励促进集体运输,使用个人控制的群体微机器人
Veit-Lorenz Heuthe1,2, Emanuele Panizon3,4, Hongri Gu1
1Department of Physics, University of Konstanz, Universitaetsstrasse 10, Konstanz, 78464, Germany.
Science robotics
|December 18, 2024
概括
研究人员使用多剂增强学习来训练微机器人群. 这些微机器人群可以集体运输货物,为先进的应用程序展示强大和多功能集体行为.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 人工智能的人工智能
- 集体行为 集体行为
背景情况:
- 群体机器人可以通过集体行动实现复杂的任务,模仿像殖民地这样的自然系统.
- 控制大量单独操作的微机器人,由于规模,噪音和机器人间的相互作用,因此存在重大挑战.
研究的目的:
- 开发一个有效的控制策略,用于微机器人群使用多代理强化学习 (MARL).
- 为了使微机器人群能够以精确和适应性的方式集体运输货物.
主要方法:
- 实施了MARL,为快速和公正的个人微机器人控制政策培训提供反事实奖励.
- 利用激光点来精确控制多达200个微机器人的个体运动.
主要成果:
- 成功训练了微型机器人群,共同将大型货物运输到指定的位置和方向.
- 证明对群体大小的变化,故障的单位和环境噪音的稳定性.
- 展示了机器人群体对多个对象的同时操纵.
结论:
- 马尔提供了一个有效的框架,用于在微机器人群中开发复杂的集体行为.
- 这种方法为自动装配,药物输送和lab-on-a-chip系统中的应用提供了一个灵活和多功能平台.
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