AcroRL: Learning Aggressive Quadrotor Inversion using Bidirectional Thrust
Overview
This paper introduces AcroRL, a learning-based framework that enables quadrotors with bidirectional thrust to perform aggressive maneuvers such as inverted flight and perching. By training separate reinforcement learning policies for transitions between nominal and inverted states, the method achieves lower position RMSE (32% reduction) and settling time (57% reduction) compared to optimization-based baselines in simulation. Hardware experiments confirm successful inversion across multiple yaw configurations with position RMSE below 0.35m, while maintaining compatibility with traditional trajectory generation. An open-source implementation is provided.
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Environment: simulation
“ed 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”
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- arXiv Robotics — research abstractsSignalAcroRL: Learning Aggressive Quadrotor Inversion using Bidirectional Thrust
This paper introduces AcroRL, a learning-based framework that enables quadrotors with bidirectional thrust to perform aggressive maneuvers such as inverted flight and perching. By training separate reinforcement learning policies for transitions between nominal and inverted states, the method achieves lower position RMSE (32% reduction) and settling time (57% reduction) compared to optimization-based baselines in simulation. Hardware experiments confirm successful inversion across multiple yaw configurations with position RMSE below 0.35m, while maintaining compatibility with traditional trajectory generation. An open-source implementation is provided.
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