# FLARE: Agile Flights for Quadrotor Cable-Suspended Payload System via Reinforcement Learning

arXiv: https://arxiv.org/abs/2508.09797 · HTML: https://mlsanity.com/paper/2508.09797/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Placed in RL for robot navigation and UAV control, one of 15 directions in Deep reinforcement learning methods.
- Builds on 7 of its 23 references that are in the map.
- First posted on arXiv 13 Aug 2025.

## Placed in

- Deep reinforcement learning methods / [RL for robot navigation and UAV control](https://mlsanity.com/deeprl/rl-for-robot-navigation-and-uav-control/)

## Builds on (most cited first, 7 of 7)

- [Proximal Policy Optimization Algorithms](https://mlsanity.com/paper/1707.06347/) (2017)
- [Autonomous Drone Racing with Deep Reinforcement Learning](https://mlsanity.com/paper/2103.08624/) (2021)
- [Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads](https://mlsanity.com/paper/2004.11345/) (2020)
- [Dashing for the Golden Snitch: Multi-Drone Time-Optimal Motion Planning with Multi-Agent Reinforcement Learning](https://mlsanity.com/paper/2409.16720/) (2024)
- [AutoTrans: A Complete Planning and Control Framework for Autonomous UAV Payload Transportation](https://mlsanity.com/paper/2310.15050/) (2023)
- [PCMPC: Perception-Constrained Model Predictive Control for Quadrotors with Suspended Loads using a Single Camera and IMU](https://mlsanity.com/paper/2107.10888/) (2021)
- [HPA-MPC: Hybrid Perception-Aware Nonlinear Model Predictive Control for Quadrotors with Suspended Loads](https://mlsanity.com/paper/2411.11982/) (2024)

## Built on by (most cited first, 1 of 1)

- [ASTER: Attitude-aware Suspended-payload Quadrotor Traversal via Efficient Reinforcement Learning](https://mlsanity.com/paper/2603.10715/) (2026)

## Abstract (arXiv)

Agile flight for the quadrotor cable-suspended payload system is a formidable challenge due to its underactuated, highly nonlinear, and hybrid dynamics. Traditional optimization-based methods often struggle with high computational costs and the complexities of cable mode transitions, limiting their real-time applicability and maneuverability exploitation. In this letter, we present FLARE, a reinforcement learning (RL) framework that directly learns agile navigation policy from high-fidelity simulation. Our method is validated across three designed challenging scenarios, notably outperforming a state-of-the-art optimization-based approach by a 3x speedup during gate traversal maneuvers. Furthermore, the learned policies achieve successful zero-shot sim-to-real transfer, demonstrating remarkable agility and safety in real-world experiments, running in real time on an onboard computer.