Uncertainty Quantification for Flow-Based Generalist Robot Policies
Vision-language-action models lack confidence measures for real-world use. This work proposes SAVE, a framework using velocity field disagreement (VFD) to guide active fine-tuning. VFD quantifies uncertainty in flow models, prioritizing tasks and initial states for expert demonstration collection. This method yields better uncertainty estimates and reduces required expert demonstrations by at least 22%.