论文标题
基于模型的元强化学习，用于悬挂有效载荷的飞行
Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads
论文作者
论文摘要
对于自动驾驶汽车，运输悬挂的有效载荷对机器人的动态造成了重大且无法预测的变化，这对于自动驾驶汽车来说是挑战。这些变化会导致次优的飞行性能甚至灾难性的失败。尽管自适应控制和基于学习的方法原则上可以适应这些混合机器人付费系统的变化，但是快速飞行飞行器对具有先验性未知物理属性的有效载荷的快速适应性仍然是一个开放的问题。我们提出了一种元学习方法，该方法在连接后飞行数据的几秒钟内“学习如何学习”模型。我们的实验表明，我们的在线适应方法在一系列具有挑战性的有效载荷运输任务上优于非自适应方法。视频和其他补充材料可在我们的网站上找到：https：//sites.google.com/view/meta-rl-for-flight
Transporting suspended payloads is challenging for autonomous aerial vehicles because the payload can cause significant and unpredictable changes to the robot's dynamics. These changes can lead to suboptimal flight performance or even catastrophic failure. Although adaptive control and learning-based methods can in principle adapt to changes in these hybrid robot-payload systems, rapid mid-flight adaptation to payloads that have a priori unknown physical properties remains an open problem. We propose a meta-learning approach that "learns how to learn" models of altered dynamics within seconds of post-connection flight data. Our experiments demonstrate that our online adaptation approach outperforms non-adaptive methods on a series of challenging suspended payload transportation tasks. Videos and other supplemental material are available on our website: https://sites.google.com/view/meta-rl-for-flight
