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.

提出了一种适用于开放式分布式机器人系统的新型训练方案，通过在未知地点遇到的其他机器人的协助下，构建伪训练数据集并用于学生模型的持续学习，在处理各种类型的开放式教师时只引入了最少的假设，包括不合作、无法训练（例如图像检索引擎）或黑盒教师（即数据隐私）。通过使用具有挑战性的无数据递归蒸馏场景中的排名函数作为实例，研究了这种通用模型，其中经过训练的学生可以递归地加入下一代开放教师集合。