Model-Based Meta-Reinforcement Learning for Flight With Suspended Payloads

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...

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Vydáno v:IEEE robotics and automation letters Ročník 6; číslo 2; s. 1471 - 1478
Hlavní autoři: Belkhale, Suneel, Li, Rachel, Kahn, Gregory, McAllister, Rowan, Calandra, Roberto, Levine, Sergey
Médium: Journal Article
Jazyk:angličtina
Vydáno: IEEE 01.04.2021
Témata:
Adaptation models
Data models
Heuristic algorithms
Machine learning for robot control
Mathematical model
Payloads
probabilistic inference
reinforcement learning
Robots
Task analysis
ISSN:2377-3766, 2377-3766
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Popis
Shrnutí: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
ISSN:2377-3766
2377-3766
DOI:10.1109/LRA.2021.3057046