[2505.12224] RoboFAC: A Comprehensive Framework for Robotic Failure Analysis and Correction

Computer Science > Robotics arXiv:2505.12224 (cs) [Submitted on 18 May 2025 (v1), last revised 22 Mar 2026 (this version, v4)] Title:RoboFAC: A Comprehensive Framework for Robotic Failure Analysis and Correction Authors:Zewei Ye, Weifeng Lu, Minghao Ye, Tao Lin, Shuo Yang, Junchi Yan, Bo Zhao View a PDF of the paper titled RoboFAC: A Comprehensive Framework for Robotic Failure Analysis and Correction, by Zewei Ye and 6 other authors View PDF HTML (experimental) Abstract:Vision-Language-Action (VLA) models have recently advanced robotic manipulation by translating natural-language instructions and visual observations into control actions. However, existing VLAs are primarily trained on successful expert demonstrations and lack structured supervision for failure diagnosis and recovery, limiting robustness in open-world scenarios. To address this limitation, we propose the Robotic Failure Analysis and Correction (RoboFAC) framework. We construct a large-scale failure-centric dataset comprising 9,440 erroneous manipulation trajectories and 78,623 QA pairs across 53 scenes in both simulation and real-world environments, with systematically categorized failure types. Leveraging this dataset, we develop a lightweight multimodal model specialized for task understanding, failure analysis, and failure correction, enabling efficient local deployment while remaining competitive with large proprietary models. Experimental results demonstrate that RoboFAC achieves a 34.1% higher failure...