RoVerFly: Robust and Versatile Learning-based Control of Quadrotor Across Payload Configurations

2025-09-14

AI Summary

Quadrotors carrying suspended payloads face significant challenges—including strong nonlinearity, underactuation, hybrid dynamics, and poor generalization—especially when payload mass and cable length vary. Method: This paper proposes a unified robust controller based on reinforcement learning, integrating task randomization and domain randomization for end-to-end training in simulation. The approach eliminates the need for controller switching or manual retuning across different payload configurations. Its architecture synergistically combines the adaptability of learned policies with the interpretability and structural guarantees of classical feedback control. Contribution/Results: Experimental results demonstrate centimeter-level trajectory tracking accuracy across diverse real-world payload configurations (no load, variable mass, variable cable length), strong disturbance rejection, and zero-shot cross-configuration transfer capability—outperforming existing switched or dedicated controllers by a significant margin.

Technology Category

• Intelligent Robots: Behavior Learning & Control
• Machine Learning: Adversarial Learning & Robustness
• Planning, Routing, and Scheduling: Learning for Planning and Scheduling

Application Category

• Responsible Web: Machine-in-the-loop, human agency and autonomy
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work

Abstract

Designing robust controllers for precise, arbitrary trajectory tracking with quadrotors is challenging due to nonlinear dynamics and underactuation, and becomes harder with flexible cable-suspended payloads that introduce extra degrees of freedom and hybridness. Classical model-based methods offer stability guarantees but require extensive tuning and often do not adapt when the configuration changes, such as when a payload is added or removed, or when the payload mass or cable length varies. We present RoVerFly, a unified learning-based control framework in which a reinforcement learning (RL) policy serves as a robust and versatile tracking controller for standard quadrotors and for cable-suspended payload systems across a range of configurations. 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 controller switching or 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

Problem

• Robust quadrotor control for precise trajectory tracking
• Adapting to varying payload configurations without retuning
• Handling cable-suspended payloads with hybrid dynamics

Innovation

• Reinforcement learning policy for quadrotor control
• Robust tracking across payload configurations
• Zero-shot generalization without retuning