Reinforcement learning policies for quadrotor inversion transitions with bidirectional thrust outperform optimization baselines by 32% in position RMSE and 57% in settling time in simulation, with successful hardware validation.
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cs.RO 2years
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Flow control steers VLA models with real-time user inputs to achieve higher success rates and faster task completion while maintaining action quality.
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AcroRL: Learning Aggressive Quadrotor Inversion using Bidirectional Thrust
Reinforcement learning policies for quadrotor inversion transitions with bidirectional thrust outperform optimization baselines by 32% in position RMSE and 57% in settling time in simulation, with successful hardware validation.
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Flow Control: Steering Vision-Language-Action Models with Simple Real-Time Inputs
Flow control steers VLA models with real-time user inputs to achieve higher success rates and faster task completion while maintaining action quality.