吊り下げペイロードを運ぶ飛行ロボットのためのモデルベースメタ強化学習
Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads
吊り下げ荷物の未知の物理特性に飛行中数秒で適応するため、変化したダイナミクスのモデルをメタ学習する手法を提案し、非適応手法より優れた運搬性能を示した。
著者: Suneel Belkhale, Rachel Li, Gregory Kahn, Rowan McAllister, Roberto Calandra, Sergey Levine
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
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
