Jiatong Li, Jisheng Dang, Qiangqiang Ma, Bimei Wang, Jingwen Zhao, Wenjun Ma, Juncheng Li, Hong Peng · 2026 · arXiv
DOI: 10.48550/arXiv.2610.28109 · open-access PDF
视觉语言行动(VLA)模型在标准机器人操作基准上表现强劲,但是,当任务条件偏离名义设置时,它们的可靠性可能会显著下降。对象位置和任务指令的干扰可能会导致完全失败。现有的VLA策略通常缺乏明确的运行时间机制来区分这些失败模式并选择适当的恢复策略。 为了系统地减轻这个问题,我们建议FIT-MAC,一个意识失败的、封闭循环的多代理控制框架,以改善 t
Machine translation (Meta M2M100); the English original follows.
Vision-language-action (VLA) models have achieved strong performance on standard robotic manipulation benchmarks. However, their reliability can degrade substantially when task conditions deviate from the nominal setting. Perturbations in object positions and task instructions may lead to complete task failure. Existing VLA policies generally lack an explicit runtime mechanism for distinguishing these failure modes and selecting an appropriate recovery strategy. To systematically mitigate this issue, we propose FIT-MAC, a failure-aware, closed-loop multi-agent control framework for improving t
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