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.

翻译：当前最先进的自定位模型通常假设目标工作空间存在标注训练数据集。然而，当机器人在通用开放世界中移动时，这一假设未必成立。本文提出了一种针对开放世界分布式机器人系统的新型训练方案。在该方案中，机器人（"学生"）可以向其在陌生地点遇到的其它机器人（"教师"）请求指导。具体而言，从教师模型重建伪训练数据集，随后在域、类别和词汇增量设置下用于学生模型的持续学习。与典型的知识迁移方案不同，本方案对教师模型仅施加极少的假设条件，从而能够处理各类开放集教师，包括不合作的、不可训练的（如图像检索引擎）或黑盒教师（即数据隐私保护）。本文以排序函数作为此类通用模型的实例，在具有挑战性的无数据递归蒸馏场景中展开研究——经过一次训练的学生模型可递归地加入下一代开放教师集合。