  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 that enables zero-shot scene transfer and real-world
deployment. Control policies relying on the embedding are able to operate in
unseen environments without the need for finetuning in the deployment
environment. We demonstrate the performance of our approach on the task of
agile, vision-based quadrotor flight. Extensive simulation and real-world
experiments demonstrate that our approach successfully generalizes beyond the
training domain and outperforms all baselines.
