Sim-to-Real Transfer in Reinforcement Learning for Maneuver Control of a Variable-Pitch MAV
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arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2025
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| Acceso en línea: | |
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| _version_ | 1866916683655938048 |
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| author | Wang, Zhikun Zhao, Shiyu |
| author_facet | Wang, Zhikun Zhao, Shiyu |
| contents | Reinforcement learning (RL) algorithms can enable high-maneuverability in unmanned aerial vehicles (MAVs), but transferring them from simulation to real-world use is challenging. Variable-pitch propeller (VPP) MAVs offer greater agility, yet their complex dynamics complicate the sim-to-real transfer. This paper introduces a novel RL framework to overcome these challenges, enabling VPP MAVs to perform advanced aerial maneuvers in real-world settings. Our approach includes real-to-sim transfer techniques-such as system identification, domain randomization, and curriculum learning to create robust training simulations and a sim-to-real transfer strategy combining a cascade control system with a fast-response low-level controller for reliable deployment. Results demonstrate the effectiveness of this framework in achieving zero-shot deployment, enabling MAVs to perform complex maneuvers such as flips and wall-backtracking. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2504_07694 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sim-to-Real Transfer in Reinforcement Learning for Maneuver Control of a Variable-Pitch MAV Wang, Zhikun Zhao, Shiyu Robotics Reinforcement learning (RL) algorithms can enable high-maneuverability in unmanned aerial vehicles (MAVs), but transferring them from simulation to real-world use is challenging. Variable-pitch propeller (VPP) MAVs offer greater agility, yet their complex dynamics complicate the sim-to-real transfer. This paper introduces a novel RL framework to overcome these challenges, enabling VPP MAVs to perform advanced aerial maneuvers in real-world settings. Our approach includes real-to-sim transfer techniques-such as system identification, domain randomization, and curriculum learning to create robust training simulations and a sim-to-real transfer strategy combining a cascade control system with a fast-response low-level controller for reliable deployment. Results demonstrate the effectiveness of this framework in achieving zero-shot deployment, enabling MAVs to perform complex maneuvers such as flips and wall-backtracking. |
| title | Sim-to-Real Transfer in Reinforcement Learning for Maneuver Control of a Variable-Pitch MAV |
| topic | Robotics |
| url | https://arxiv.org/abs/2504.07694 |