mlsanityData to 14 Oct 2026

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

arXiv 2504.07694 ↗First posted 10 Apr 2025cs.ROcited by 0

On the map: Deep reinforcement learning methods → RL for robot navigation and UAV control

Abstract (arXiv)

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.

Key facts. No citing papers in the map yet (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 12 of its 30 references that are in the map. First posted on arXiv 10 Apr 2025.

Builds on

12 of 30 references.

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Same direction, newest first

  1. 2024Reinforcement learning16 May

Other references · 11

  1. 2017Proximal Policy Optimization Algorithmscited by 15,215
  2. 2018On the Continuity of Rotation Representations in Neural Networkscited by 1,214
  3. 2021Isaac Gym: High Performance GPU-Based Physics Simulation For Robot Learningcited by 998
  4. 2019Learning agile and dynamic motor skills for legged robotscited by 504
  5. 2021Learning High-Speed Flight in the Wildcited by 128
  6. 2023Reaching the Limit in Autonomous Racing: Optimal Control versus Reinforcement Learningcited by 111
  7. 2021Learning to Fly -- a Gym Environment with PyBullet Physics for Reinforcement Learning of Multi-agent Quadcopter Controlcited by 77
  8. 2019Deep Drone Racing: From Simulation to Reality with Domain Randomizationcited by 76
  9. 2022Aggressive Quadrotor Flight Using Curiosity-Driven Reinforcement Learningcited by 8
  10. 2019Flying through a narrow gap using neural network: an end-to-end planning and control approachcited by 4
  11. 2017Towards Long-endurance Flight: Design and Implementation of a Variable-pitch Gasoline-engine Quadrotorcited by 1

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  1. 2024Deep Reinforcement Learning for Sim-to-Real Policy Transfer of VTOL-UAVs Offshore Docking Operations
  2. 2024The Power of Input: Benchmarking Zero-Shot Sim-To-Real Transfer of Reinforcement Learning Control Policies for Quadrotor Control
  3. 2025Reactive Aerobatic Flight via Reinforcement Learning
  4. 2026Multi-Task Reinforcement Learning of Drone Aerobatics by Exploiting Geometric Symmetries
  5. 2025Leveling the Playing Field: Carefully Comparing Classical and Learned Controllers for Quadrotor Trajectory Tracking
  6. 2024Multi-Fidelity Reinforcement Learning for Time-Optimal Quadrotor Re-planning
  7. 2025Non-Equilibrium MAV-Capture-MAV via Time-Optimal Planning and Reinforcement Learning
  8. 2024A Learning-based Quadcopter Controller with Extreme Adaptation