Training Self-localization Models for Unseen Unfamiliar Places via
  Teacher-to-Student Data-Free Knowledge Transfer
Kenta Tsukahara Kanji Tanaka Daiki Iwata
A typical assumption in state-of-the-art self-localization models is that an
annotated training dataset is available in the target workspace. However, this
does not always hold when a robot travels in a general open-world. This study
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 thereafter used for continual learning
of the student model. Unlike typical knowledge transfer schemes, our scheme
introduces only minimal assumptions on the teacher model, such that it can
handle various types of open-set teachers, including uncooperative, untrainable
(e.g., image retrieval engines), and blackbox teachers (i.e., data privacy).
Rather than relying on the availability of private data of teachers as in
existing methods, we propose to exploit an assumption that holds universally in
self-localization tasks: "The teacher model is a self-localization system" and
to reuse the self-localization system of a teacher as a sole accessible
communication channel. We particularly focus on designing an excellent
student/questioner whose interactions with teachers can yield effective
question-and-answer sequences that can be used as pseudo-training datasets for
the student self-localization model. When applied to a generic recursive
knowledge distillation scenario, our approach exhibited stable and consistent
performance improvement.