CommitFlow：面向长时程机器人操作VLA执行的语义承诺验证与局部修正
CommitFlow: Semantic Commitment Verification and Local Correction for Long-Horizon Robot Manipulation VLA Execution
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中文总结 AI 辅助
CommitFlow提出闭环执行框架，通过语义承诺监控与局部修正解决VLA长时程任务中物理效果未达成即推进的问题，在RoboTwin 2.0上平均成功率提升22.7%。
中文摘要 AI 辅助
尽管视觉-语言-动作（VLA）策略发展迅速，但在长时程执行中，系统可能在所需物理效果尚未建立之前就推进到下一个任务阶段。我们将此称为语义承诺（即某个阶段必须建立或维持的物理条件）与实际物理状态之间的不匹配。由于仅凭动作命令无法确认此类条件，局部偏差可能会传播并导致任务失败。为解决这一问题，我们提出了CommitFlow，一个闭环执行框架，它在保持基础策略冻结的同时，将承诺监控与局部修正相结合。CommitFlow集成了三个组件。语义承诺监控器（SCM）将阶段要求与当前状态证据进行比较，并在所需条件未满足或遭到违反时阻止依赖动作的执行。随后，BoundaryFlow根据当前状态和基础动作生成局部修正，而关系与增益校准（RGC）则选择满足相关约束的最小修正强度。在RoboTwin 2.0基准的十个常见任务中，CommitFlow实现了75.9%的平均成功率，比基础策略pi0.5提高了22.7%。跨策略实验显示出一致的增益，指向可靠的长时程机器人执行。
英文摘要
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.
发表机构
- National University of Defense Technology（国防科技大学）
机构由 AI 辅助整理，请以论文原文为准。
