FLARE: Agile Flights for Quadrotor Cable-Suspended Payload System via Reinforcement Learning
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arXiv
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| Auteurs principaux: | , , , |
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| Format: | Preprint |
| Publié: |
2025
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| _version_ | 1866910004714405888 |
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| author | Cao, Dongcheng Zhou, Jin Wang, Xian Li, Shuo |
| author_facet | Cao, Dongcheng Zhou, Jin Wang, Xian Li, Shuo |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_09797 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | FLARE: Agile Flights for Quadrotor Cable-Suspended Payload System via Reinforcement Learning Cao, Dongcheng Zhou, Jin Wang, Xian Li, Shuo Robotics 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. |
| title | FLARE: Agile Flights for Quadrotor Cable-Suspended Payload System via Reinforcement Learning |
| topic | Robotics |
| url | https://arxiv.org/abs/2508.09797 |