# Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads

arXiv: https://arxiv.org/abs/2004.11345 · HTML: https://mlsanity.com/paper/2004.11345/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 23 Apr 2020.

## Built on by (most cited first, 30 of 30)

- [Neural-Fly Enables Rapid Learning for Agile Flight in Strong Winds](https://mlsanity.com/paper/2205.06908/) (2022)
- [A Survey on Model-based Reinforcement Learning](https://mlsanity.com/paper/2206.09328/) (2022)
- [Adaptive-Control-Oriented Meta-Learning for Nonlinear Systems](https://mlsanity.com/paper/2103.04490/) (2021)
- [OmniDrones: An Efficient and Flexible Platform for Reinforcement Learning in Drone Control](https://mlsanity.com/paper/2309.12825/) (2023)
- [Learning to Fly in Seconds](https://mlsanity.com/paper/2311.13081/) (2023)
- [Context is Everything: Implicit Identification for Dynamics Adaptation](https://mlsanity.com/paper/2203.05549/) (2022)
- [A Machine Learning-oriented Survey on Tiny Machine Learning](https://mlsanity.com/paper/2309.11932/) (2023)
- [Learning Dynamics Models for Model Predictive Agents](https://mlsanity.com/paper/2109.14311/) (2021)
- [Learning a Single Near-hover Position Controller for Vastly Different Quadcopters](https://mlsanity.com/paper/2209.09232/) (2022)
- [Control-oriented meta-learning](https://mlsanity.com/paper/2204.06716/) (2022)
- [Active Learning of Discrete-Time Dynamics for Uncertainty-Aware Model Predictive Control](https://mlsanity.com/paper/2210.12583/) (2022)
- [Reinforcement Learning for Safe Robot Control using Control Lyapunov Barrier Functions](https://mlsanity.com/paper/2305.09793/) (2023)
- [Equivariant Reinforcement Learning for Quadrotor UAV](https://mlsanity.com/paper/2206.01233/) (2022)
- [Neural Moving Horizon Estimation for Robust Flight Control](https://mlsanity.com/paper/2206.10397/) (2022)
- [Improved Robustness and Safety for Pre-Adaptation of Meta Reinforcement Learning with Prior Regularization](https://mlsanity.com/paper/2108.08448/) (2021)
- [A Comprehensive Review and a Taxonomy of Edge Machine Learning: Requirements, Paradigms, and Techniques](https://mlsanity.com/paper/2302.08571/) (2023)
- [ProxFly: Robust Control for Close Proximity Quadcopter Flight via Residual Reinforcement Learning](https://mlsanity.com/paper/2409.13193/) (2024)
- [RoVerFly: Robust and Versatile Implicit Hybrid Control of Quadrotor-Payload Systems](https://mlsanity.com/paper/2509.11149/) (2025)
- [Meta-Learning Online Dynamics Model Adaptation in Off-Road Autonomous Driving](https://mlsanity.com/paper/2504.16923/) (2025)
- [Adaptive Asynchronous Control Using Meta-learned Neural Ordinary Differential Equations](https://mlsanity.com/paper/2207.12062/) (2022)
- [Meta-Reinforcement Learning with Universal Policy Adaptation: Provable Near-Optimality under All-task Optimum Comparator](https://mlsanity.com/paper/2410.09728/) (2024)
- [On Task-Relevant Loss Functions in Meta-Reinforcement Learning and Online LQR](https://mlsanity.com/paper/2312.05465/) (2023)
- [FLARE: Agile Flights for Quadrotor Cable-Suspended Payload System via Reinforcement Learning](https://mlsanity.com/paper/2508.09797/) (2025)
- [Error-Aware Policy Learning: Zero-Shot Generalization in Partially Observable Dynamic Environments](https://mlsanity.com/paper/2103.07732/) (2021)
- [Online No-regret Model-Based Meta RL for Personalized Navigation](https://mlsanity.com/paper/2204.01925/) (2022)
- [Designing a Robust Low-Level Agnostic Controller for a Quadrotor with Actor-Critic Reinforcement Learning](https://mlsanity.com/paper/2210.02964/) (2022)
- [Intercepting Unauthorized Aerial Robots in Controlled Airspace Using Reinforcement Learning](https://mlsanity.com/paper/2407.06909/) (2024)
- [AD-NODE: Adaptive Dynamics Learning with Neural ODEs for Mobile Robots Control](https://mlsanity.com/paper/2510.05443/) (2025)
- [Context-Aware Model-Based Reinforcement Learning for Autonomous Racing](https://mlsanity.com/paper/2510.11501/) (2025)
- [MAVEN: A Meta-Reinforcement Learning Framework for Varying-Dynamics Expertise in Agile Quadrotor Maneuvers](https://mlsanity.com/paper/2603.10714/) (2026)

## Abstract (arXiv)

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