Recursive Distillation for Open-Set Distributed Robot Localization: A Data-Free Approach to Knowledge Transfer Between Diverse Teacher Models

This work introduces a novel data-free recursive distillation scheme for open-world distributed robot systems, where a student robot can ask various types of teacher robots, including uncooperative, untrainable, or black-box models, for guidance in unfamiliar workspaces.