視覚ベースの俊敏飛行におけるロバストなシーン転移を実現する対照学習
Contrastive Learning for Enhancing Robust Scene Transfer in Vision-based Agile Flight
適応的なマルチペア対照学習で視覚表現を学習し、未見環境でもファインチューニングなしにドローンの俊敏飛行を可能にした研究。
著者: Jiaxu Xing, Leonard Bauersfeld, Yunlong Song, Chunwei Xing, Davide Scaramuzza
分類: cs.RO, cs.CV
原文アブストラクト
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.
