Optimal Transport Q-Learning Method Accelerates Vision-Language Robot Policies最优传输Q学习加速视觉语言机器人策略学习
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Researchers introduce Optimal Transport Q-Learning (OTQL), a new RL post-training method for fine-tuning and accelerating flow-based policies that accurately capture multimodal robot trajectory distributions in VLA models.
OTQL achieves policy optimization with an interaction budget of only 50-60 episodes while avoiding computationally expensive distillation in both simulation and real-world robotic tasks.
The method uses advantage-weighted conditional optimal transport flow matching to address performance gaps caused by suboptimal demonstrations and distribution shifts.