Recursive Distillation for Open-Set Distributed Robot Localization
This addresses the challenge of distributed robot localization in real-world, open-set scenarios, though it appears incremental as it builds on knowledge transfer schemes.
The paper tackles the problem of robot self-localization in open-world environments where annotated training data is unavailable, by introducing a training scheme where robots can learn from other robots they encounter. The result is a system that handles various types of teacher models, including uncooperative or black-box ones, with minimal assumptions.
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.