フロー逆転ステアリングによるロボット汎用ポリシーの性能向上
Improving Robotic Generalist Policies via Flow Reversal Steering
フローマッチングに基づく汎用ロボットポリシーに対し、不完全な行動を逆フローで潜在ノイズに変換し、近傍の高品質な行動モードへ写像する手法を提案。シミュレーションと実機でゼロショット制御や強化学習の改善に効果を示した。
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著者: Andy Tang, William Chen, Andrew Wagenmaker, Chelsea Finn, Sergey Levine
分類: cs.RO
原文アブストラクト
Generalist policies can learn a wide range of skills from diverse robot datasets. In order to solve or improve on challenging new tasks, we need a way to infer and invoke the appropriate actions from the policy's rich behavioral prior, especially when directly commanding the policy fails. We focus on flow matching generalists and propose Flow Reversal Steering (FRS): a method that takes suboptimal but ``reasonable'' actions, finds their latent noises by passing them through the flow policy in reverse, and maps them to nearby generalist action modes. We evaluate FRS across many simulated and real-world manipulation settings. First, FRS can turn coarse semantic guidance from humans or vision-language models (VLMs) into corresponding good robot actions, improving zero-shot control. These gains can be distilled with behavioral cloning by training an auxiliary policy to output noises that the generalist maps to good actions -- showing up to 95% absolute task success rate boosts in under a minute of training. Finally, FRS enables policy improvement by bootstrapping reinforcement learning with semantic knowledge, improving on several tasks that standard RL fails to improve on.
