Contrastive Learning for Enhancing Robust Scene Transfer in Vision-based Agile Flight

arXiv:2309.09865v3 Announce Type: replace-cross
Abstract: Scene transfer for vision-based mobile robotics applications is a highly relevant and challenging problem. The utility of a robot greatly depends on its ability to perform a task in the real world, outside of a well-controlled lab environment. Existing scene transfer end-to-end policy learning approaches often suffer from poor sample efficiency or limited generalization capabilities, making them unsuitable for mobile robotics applications. This work proposes an adaptive multi-pair contrastive learning strategy for visual representation learning …

abstract agile applications arxiv cs.cv cs.ro environment lab mobile policy robot robotics robust transfer type utility vision world