SpeedAug
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SpeedAug: Policy Acceleration via Tempo-Enriched Policy and RL Fine-Tuning
Announce Type: replace Abstract: Robotic policy learning for complex real-world manipulation tasks has seen rapid recent progress, enabled in large part by the ability to collect demonstrations through human operation. However, policies trained from such demonstrations often execute tasks far more slowly than the robot's physical capabilities, as demonstration data is collected under practical constraints that favor conservative, success-oriented trajectories over execution speed. Existing...