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

Jiaxu Xing, Leonard Bauersfeld, Yunlong Song, Chunwei Xing, Davide Scaramuzza

Keywords: Imitation learning, Contrastive Learning, Agile Drone Flight

TL;DR: Learning scene agnostic but task relevant image encoding for visuomotor policy learning.

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 multipair 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.

Submission Number: 18