CommitFlow: 長期的ロボットマニピュレーションVLA実行のための意味的コミットメント検証と局所修正
CommitFlow: Semantic Commitment Verification and Local Correction for Long-Horizon Robot Manipulation VLA Execution
VLAポリシーの長期実行において、各段階で必要な物理的状態が満たされる前に次の段階へ進んでしまう問題に対し、意味的コミットメントの監視と局所修正を組み合わせた閉ループ実行フレームワークを提案し、RoboTwin 2.0でベースポリシーを22.7%上回る成功率を達成した。
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？
5. 議論はある？
6. 次に読むべき論文は？
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著者: Zixiang Zhao, Yansong Feng, Yang Yang, Chaoyu Wang, Haoran Xiao, Hui Zhang, Chuang Cheng, Jianjun Ma
分類: cs.RO
原文アブストラクト
Although vision-language-action (VLA) policies have advanced rapidly, long-horizon execution may still progress to the next task stage before the required physical effect has been established. We call this a mismatch between semantic commitments, physical conditions that a stage must establish or maintain, and the actual physical state. Because an action command alone cannot confirm such a condition, local deviations can propagate and cause task failure. To address this problem, we present CommitFlow, a closed-loop execution framework that combines commitment monitoring with local correction while keeping the base policy frozen. CommitFlow integrates three components. A Semantic Commitment Monitor (SCM) compares stage requirements against current state evidence and holds back dependent actions when a required condition is unmet or violated. BoundaryFlow then generates a local correction conditioned on the current state and base action, and Relation and Gain Calibration (RGC) selects the smallest correction strength that satisfies the relevant constraints. Across the ten common RoboTwin 2.0 benchmark tasks, CommitFlow achieves a mean success rate of 75.9 percent, improving on the base policy pi0.5 by 22.7 percent. Cross-policy experiments show consistent gains, pointing toward reliable long-horizon robot execution.