Robotic manipulation is a fundamental component of automation. However, traditional perception-planning pipelines often fall short in open-ended tasks due to limited flexibility, while the architecture of a single end-to-end Vision-Language-Action (VLA) offers promising capabilities but lacks crucial mechanisms for anticipating and recovering from failure. To address these challenges, we propose FPC-VLA, a dual-model framework that integrates VLA with a supervisor for failure prediction and correction. The supervisor evaluates action viability through vision-language queries and generates corrective strategies when risks arise, trained efficiently without manual labeling. A dual-stream fusion module further refines actions by leveraging past predictions. Evaluation results on multiple simulation platforms (SIMPLER and LIBERO) and robot embodiments (WidowX, Google Robot, Franka) show that FPC-VLA outperforms state-of-the-art models in both zero-shot and fine-tuned settings. Successful real-world deployments on diverse, long-horizon tasks confirm FPC-VLA's strong generalization and practical utility for building more reliable autonomous systems.

翻译：机器人操作是自动化的基础组成部分。然而，传统的感知-规划流程在开放任务中常因灵活性不足而受限，而单一的端到端视觉-语言-动作（VLA）架构虽展现出潜力，却缺乏预测故障及从中恢复的关键机制。为应对这些挑战，我们提出FPC-VLA，这是一种双模型框架，将VLA与用于故障预测和纠正的监督器相结合。该监督器通过视觉-语言查询评估动作可行性，并在风险出现时生成纠正策略，其训练过程无需人工标注即可高效完成。一个双流融合模块通过利用历史预测进一步优化动作。在多个仿真平台（SIMPLER和LIBERO）及机器人实体（WidowX、Google Robot、Franka）上的评估结果表明，FPC-VLA在零样本和微调设置下均优于现有最先进模型。在多样化、长周期任务中的成功实际部署，证实了FPC-VLA在构建更可靠自主系统方面具有强大的泛化能力和实用价值。