FPC-VLA: A Vision-Language-Action Framework with a Supervisor for Failure Prediction and Correction
Year: 2025
Type: preprint
Abstract: 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.
Source: arXiv (Cornell University)
Authors: Yang, Yifan, Duan, Zhixiang, Xie, Tianshi, Cao, Fuyu, Shen, Pinxi, Song, Peili, Jin, Piaopiao, Sun, Guokang, Xu, Shaoqing, You, Yangwei, Liu, Jingtai
Language: English
Subfield: Control and Systems Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W7080182035
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