AcroRL: Learning Aggressive Quadrotor Inversion using Bidirectional Thrust
This paper proposes a reinforcement learning framework that modulates a constant reference trajectory to perform compact, position-constrained quadrotor inversions while remaining compatible with traditional trajectory generation and tracking. In simulation, the method reduces position RMSE by 32% and settling time by 57% relative to the strongest optimization-based baseline. Hardware experiments demonstrate successful inversion across multiple yaw configurations with position RMSE below 0.35m.
Article intelligence
Key points
- Bidirectional thrust enables inverted flight, perching, and sensing for quadrotors.
- Prior methods struggle with actuator saturation and motor reversal delay.
- Proposed RL framework reduces position RMSE by 32% and settling time by 57%.
- Hardware experiments validate the method's effectiveness and compatibility.
Why it matters
This matters because bidirectional thrust enables inverted flight, perching, and sensing for quadrotors.
Technical impact
May affect research directions, evaluation methods, open-source reproduction, and productization paths.
[2605.24301] AcroRL: Learning Aggressive Quadrotor Inversion using Bidirectional Thrust
[Submitted on 23 May 2026]
Title:AcroRL: Learning Aggressive Quadrotor Inversion using Bidirectional Thrust
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Abstract:Bidirectional thrust grants quadrotors a second equilibrium condition and increased control authority, expanding the envelope of possible aggressive maneuvers and enabling inverted flight, perching, and sensing. Prior geometric control approaches extend differential flatness through Hopf fibration-based attitude representations to support bidirectional thrust, but struggle with actuator saturation and motor reversal delay during inversions, requiring heuristic thrust posture scheduling and waypoint tuning. We propose a learning-based framework that modulates a constant reference trajectory to perform compact, position-constrained quadrotor inversions while remaining compatible with traditional trajectory generation and tracking across flight regimes. Separate policies are trained via reinforcement learning for nominal-to-inverted and inverted-to-nominal transitions. In JAX-based simulation, the proposed method achieves the lowest position deviation and settling time across all evaluated baselines, reducing position root mean square error (RMSE) by 32% and settling time by 57% relative to the strongest optimization-based baseline. Hardware experiments demonstrate successful inversion across multiple yaw configurations with position RMSE below 0.35m, and compatibility with downstream trajectory generation and control through circular flight in both regimes. Additionally, we provide an open-source implementation of the proposed framework.
Comments: 17 pages, 8 figures
Subjects:
Robotics (cs.RO)
Cite as: arXiv:2605.24301 [cs.RO]
(or arXiv:2605.24301v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.24301
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Gabriel Rodriguez [view email] [v1] Sat, 23 May 2026 00:02:22 UTC (8,408 KB)
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