最適輸送Q学習によるフローポリシーの操縦と高速化
Optimal Transport Q-Learning for Flow Policy Steering and Acceleration
ロボットの経験を用いたRL後訓練により、準最適なフローベースポリシーを微調整・高速化する最適輸送Q学習（OTQL）を提案した。
詳しい要約
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著者: Andreas Sochopoulos, Esmeralda S. Whitammer, Nikolaos Tsagkas, João Moura, Michael Gienger, Sethu Vijayakumar
分類: cs.RO
原文アブストラクト
Diffusion and flow policies have recently demonstrated remarkable performance in robotic applications by accurately capturing multimodal robot trajectory distributions, especially in the context of vision language action (VLA) models. However, high quality policy performance also requires fast inference and high quality demonstrations, which are often hard to get. Lack of these leads to suboptimal policy behaviors and failure under distribution shifts. In this work we address the problem of fine-tuning and accelerating suboptimal flow-based policies using the robot's experience through RL post-training. We introduce Optimal Transport Q-Learning (OTQL), a new method for finetuning flow policies using advantage weighted conditional optimal transport flow matching. OTQL can finetune and accelerate flows with an interaction budget of 50-60 episodes while avoiding computationally expensive distillation in simulation and real-world robot tasks. Our results show that OTQL post-trains flow policies using the robot's own experience, increasing average success percentage of single-task policies from 36% to 86% and of a pre-trained VLA from 38% to 76% while reducing the number of inference steps per action generation by 70%.
