  A typical assumption in state-of-the-art self-localization models is that an
annotated training dataset is available for the target workspace. However, this
is not necessarily true when a robot travels around the general open world.
This work introduces a novel training scheme for open-world distributed robot
systems. In our scheme, a robot (``student") can ask the other robots it meets
at unfamiliar places (``teachers") for guidance. Specifically, a
pseudo-training dataset is reconstructed from the teacher model and then used
for continual learning of the student model under domain, class, and vocabulary
incremental setup. Unlike typical knowledge transfer schemes, our scheme
introduces only minimal assumptions on the teacher model, so that it can handle
various types of open-set teachers, including those uncooperative, untrainable
(e.g., image retrieval engines), or black-box teachers (i.e., data privacy). In
this paper, we investigate a ranking function as an instance of such generic
models, using a challenging data-free recursive distillation scenario, where a
student once trained can recursively join the next-generation open teacher set.
