在自主赛车中达到极限:最佳控制与强化学习相比
Yunlong Song1, Angel Romero1, Matthias Müller2
1University of Zurich, Zurich, Switzerland.
Science robotics
|September 13, 2023
概括
强化学习 (RL) 控制器在自主无人机比赛中通过优化更好的目标,而不仅仅是更好的优化,优化了最佳控制 (OC). 这使敏捷的机器人能够以强大的控制响应实现超人性能.
科学领域:
- 机器人技术 机器人技术 机器人技术
- 控制系统 控制系统
- 机器学习 机器学习
背景情况:
- 为敏捷的移动机器人设计控制系统是机器人技术的一个关键挑战.
- 自主无人机赛车为机器人控制系统提供了苛刻的测试平台.
- 传统的最佳控制 (OC) 方法在复杂,动态的环境中面临着局限性.
研究的目的:
- 系统地研究敏捷移动机器人的控制系统的设计.
- 为了比较强化学习 (RL) 和最佳控制 (OC) 在自主无人机比赛中的性能.
- 识别导致RL与OC相比取得成功的基本因素.
主要方法:
- 一个神经网络控制器使用强化学习 (RL) 进行了训练.
- 将RL控制器的性能与最佳控制 (OC) 方法进行了比较.
- 在RL框架内使用域随机化来处理模型不确定性.
主要成果:
- 在自主无人机比赛中,RL训练的控制器显著超过了最佳控制 (OC) 方法.
- RL的优势来自于优化一个更合适的目标,而不是优化的优化效率.
- RL控制器实现了超过12g的峰值加速度和108公里/小时的峰值速度,证明了超人性能.
结论:
- 强化学习 (RL) 为机器人控制提供了一种优越的方法,与敏捷系统的最佳控制 (OC) 相比.
- 对于先进的机器人行为来说,RL直接优化任务级目标和处理模型不确定性的能力至关重要.
- 这项研究标志着敏捷机器人技术的一个里程碑,突出了RL在实现前所未有的性能和强大的控制方面的潜力.
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