# RoVerFly: Robust and Versatile Implicit Hybrid Control of Quadrotor-Payload Systems

arXiv: https://arxiv.org/abs/2509.11149 · HTML: https://mlsanity.com/paper/2509.11149/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 18 of its 31 references that are in the map.
- First posted on arXiv 14 Sep 2025.

## Builds on (most cited first, 18 of 18)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Domain Randomization for Transferring Deep Neural Networks from Simulation to the Real World](https://mlsanity.com/paper/1703.06907/) (2017)
- [Sim-to-Real Transfer of Robotic Control with Dynamics Randomization](https://mlsanity.com/paper/1710.06537/) (2017)
- [Reinforcement Learning for Versatile, Dynamic, and Robust Bipedal Locomotion Control](https://mlsanity.com/paper/2401.16889/) (2024)
- [Control of a Quadrotor with Reinforcement Learning](https://mlsanity.com/paper/1707.05110/) (2017)
- [Deep Drone Acrobatics](https://mlsanity.com/paper/2006.05768/) (2020)
- [A Benchmark Comparison of Learned Control Policies for Agile Quadrotor Flight](https://mlsanity.com/paper/2202.10796/) (2022)
- [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)
- [Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads](https://mlsanity.com/paper/2004.11345/) (2020)
- [Learning a Single Near-hover Position Controller for Vastly Different Quadcopters](https://mlsanity.com/paper/2209.09232/) (2022)
- [Learning Finite-State Controllers for Partially Observable Environments](https://mlsanity.com/paper/1301.6721/) (2013)
- [DATT: Deep Adaptive Trajectory Tracking for Quadrotor Control](https://mlsanity.com/paper/2310.09053/) (2023)
- [Learning-based Trajectory Tracking for Bird-inspired Flapping-Wing Robots](https://mlsanity.com/paper/2411.15130/) (2024)
- [Robotics meets Fluid Dynamics: A Characterization of the Induced Airflow below a Quadrotor as a Turbulent Jet](https://mlsanity.com/paper/2403.13321/) (2024)
- [ProxFly: Robust Control for Close Proximity Quadcopter Flight via Residual Reinforcement Learning](https://mlsanity.com/paper/2409.13193/) (2024)
- [Estimation of Aerodynamics Forces in Dynamic Morphing Wing Flight](https://mlsanity.com/paper/2508.02984/) (2025)
- [Differential-Flatness and Control of Quadrotor(s) with a Payload Suspended through Flexible Cable(s)](https://mlsanity.com/paper/1711.04895/) (2017)

## Built on by (most cited first, 4 of 4)

- [Finite Memory Belief Approximation for Optimal Control in Partially Observable Markov Decision Processes](https://mlsanity.com/paper/2601.03132/) (2026)
- [WOMBET: World Model-Based Experience Transfer for Robust and Sample-efficient Reinforcement Learning](https://mlsanity.com/paper/2604.08958/) (2026)
- [Robust Adversarial Policy Optimization Under Dynamics Uncertainty](https://mlsanity.com/paper/2604.10974/) (2026)
- [In-Distribution Imagination for Model-Based Offline Reinforcement Learning](https://mlsanity.com/paper/2609.38673/) (2026)

## Abstract (arXiv)

Designing robust controllers for precise trajectory tracking with quadrotors is challenging due to nonlinear dynamics and underactuation, and becomes harder with flexible cable-suspended payloads that add degrees of freedom and hybrid dynamics. Classical model-based methods offer stability guarantees but require extensive tuning and often fail to adapt when the configuration changes-when a payload is added or removed, or when its mass or cable length varies. We present RoVerFly, a unified learning-based control framework where a single reinforcement learning (RL) policy functions as an implicit hybrid controller, managing complex dynamics without explicit mode detection or controller switching. Trained with task and domain randomization, the controller is resilient to disturbances and varying dynamics. It achieves strong zero-shot generalization across payload settings-including no payload as well as varying mass and cable length-without re-tuning, while retaining the interpretability and structure of a feedback tracking controller. Code and supplementary materials are available at https://github.com/mintaeshkim/roverfly.