Self-Refining Vision Language Model for Robotic Failure Detection and Reasoning
ARMOR is a model that detects and reasons about robotic failures by iteratively refining its predictions. It treats detection and reasoning as a self-refinement process where the model predicts outcomes and natural language explanations based on previous steps. Trained on mixed data, it achieves state-of-the-art performance by handling both sparse failure labels and expensive human reasoning annotations.