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arXiv · 2504.07694

Sim-to-Real Transfer in Reinforcement Learning for Maneuver Control of a Variable-Pitch MAV

Zhikun Wang·Shiyu Zhao
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Abstract

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.

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Zhikun Wang, Shiyu Zhao. 2025-04-10. Sim-to-Real Transfer in Reinforcement Learning for Maneuver Control of a Variable-Pitch MAV. https://arxiv.org/abs/2504.07694

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