RoboFAC: A Comprehensive Framework for Robotic Failure Analysis and Correction
RoboFAC addresses a lack of structured supervision for robotic failure diagnosis in Vision-Language-Action (VLA) models. It creates a large, diverse dataset of 9,440 erroneous trajectories and annotates failures hierarchically. A specialized multimodal model is then trained on this data to systematically diagnose failures—identifying the type, location, and cause—and provide both high-level planning steps and precise low-level commands for recovery.