Training Self-localization Models for Unseen Unfamiliar Places via Teacher-to-Student Data-Free Knowledge Transfer

arXiv:2403.10552v1 Announce Type: new
Abstract: 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 …

abstract art arxiv cs.ai cs.cv cs.lg cs.ro data dataset distributed free general however knowledge localization novel open-world robot state study training transfer travels type via workspace world