力の注入に遅すぎることはない：反応的力注入によるVLAポストトレーニングの加速
Never Too Late for Force: Accelerating VLA Post-Training with Reactive Force Injection
事前学習済みの視覚言語行動（VLA）ポリシーに、接触反応性を追加するフレームワークLIFTを提案。力覚メモリとゼロ初期化クロスアテンションで反応的アクション専門家を移植し、オンラインDAggerループで分布シフトに対処。タオル折り、本挿入、ハノイの輪配置で性能向上を実証。
詳しい要約
1. どんなもの？
2. 先行研究と比べてどこがすごい？
3. 技術・手法の肝は？
4. どうやって有効だと検証した？
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著者: Yi Wang, Wendi Chen, Zimo Wen, Han Xue, Xueqi Li, Wenye Yu, Zhijie Chen, Hao Yang, Jun Lv, Chuan Wen, Cewu Lu
分類: cs.RO, cs.AI, cs.LG
原文アブストラクト
Pretrained vision-language-action (VLA) policies provide strong language-conditioned manipulation knowledge, but they remain largely vision-driven and can struggle once manipulation enters contact states where the scene is occluded, depth is ambiguous, or small force errors push execution off the offline demonstration distribution. We present LIFT (Late Reactive Injection of Force for VLA Post-Training), a force-aware post-training framework that adds contact reactivity to a pretrained VLA policy while preserving its general manipulation knowledge. LIFT grafts a reactive action expert beside the original action expert, initializes it from pretrained action weights, and injects recent 6D end-effector force through causal force memory and zero-initialized cross attention, enabling actions to be refreshed during execution. To address the policy-dependent distribution shift of contact feedback, LIFT further couples reactive force injection with an online DAgger loop that trains on a mixture of offline task-alignment data and human-corrected online rollouts. Across towel folding, book insertion, and Hanoi ring placement, LIFT learns faster and reaches higher performance than vision-only post-training, while ablations show that reactive force memory and online corrective data are both important for robust contact-rich manipulation. Our code and data will be publicly available.
