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.

翻译：面向视觉移动机器人应用的场景迁移是一个高度相关且充满挑战的问题。机器人的实用性在很大程度上取决于其在良好控制的实验室环境之外，在真实世界中执行任务的能力。现有的端到端场景迁移策略学习方法常面临样本效率低下或泛化能力有限的缺陷，使其不适合移动机器人应用。本文提出一种自适应多对对比学习策略用于视觉表征学习，实现了零样本场景迁移与真实世界部署。依赖于嵌入表示的策略控制器能够在未见环境中运行，无需在部署环境中进行微调。我们以基于视觉的敏捷四旋翼飞行任务验证了所提方法的性能。大量仿真与真实世界实验表明，该方法成功实现了对训练域以外的泛化，且性能全面超越所有基线方案。