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FIT-MAC: A Closed-Loop Multi-Agent Framework for Robust Vision-Language-Action Models

Jiatong Li, Jisheng Dang, Qiangqiang Ma, Bimei Wang, Jingwen Zhao, Wenjun Ma, Juncheng Li, Hong Peng, Bin Hu · 2026 · arXiv (Cornell University)

DOI: 10.48550/arxiv.2610.28109 · cited by 0 works (OpenAlex) · open-access PDF

Spanish

Los modelos de visión-lenguaje-acción (VLA) han logrado un fuerte rendimiento en los benchmarks de manipulación robótica estándar. Sin embargo, su fiabilidad puede debilitarse sustancialmente cuando las condiciones de tarea se desvían de la configuración nominal. Las perturbaciones en las posiciones de objeto y las instrucciones de tarea pueden conducir a un fracaso de tarea completo. Las políticas de VLA existentes carecen generalmente de un mecanismo de tiempo de ejecución explícito para distinguir estos modos de fracaso y seleccionar una estrategia de recuperación adecuada. Para mitigar sistemáticamente este problema, proponemos el FIT-MAC, un marco de control multiagente cerrado, consciente del fracaso, para mejorar la robustez de las políticas de VLA a la inferencia. El F

Machine translation (Meta M2M100); the English original follows.

Abstract

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 the robustness of VLA policies at inference time. FIT-MAC augments a VLA policy with an inference-time control layer. First, the Planner Agent parses the language instruction into a structured task specification that captures the manipulation goal and spatial constraints, and the Grounder Agent resolves each instruction-level reference to a visual target. Next, the Verifier Agent tracks phase-specific execution progress, distinguishes target-binding errors from local execution failures, and determines whether intervention is warranted. The Correction Agent then routes permitted interventions to target rebinding or bounded grasping or placement recovery, depending on the failure type. When no intervention is required, the pretrained VLA remains the default action generator. Extensive experiments on LIBERO-PRO demonstrate that FIT-MAC improves robustness across multiple VLA backbones, consistently increasing success rates and achieving state-of-the-art performance under diverse perturbations.

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Topics: Computer science, Robustness (evolution), Planner, Inference, Task (project management), Artificial intelligence

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