ATLAS Navigator: 言語埋め込みガウシアンスプラッティングによる能動的タスク駆動ナビゲーション
ATLAS Navigator: Active Task-driven LAnguage-embedded Gaussian Splatting
言語埋め込みガウシアンスプラッティングを用いた階層表現で、未知環境での自然言語タスクに基づくナビゲーションを実現し、屋内・屋外の実機実験で有効性を示した。
著者: Dexter Ong, Yuezhan Tao, Varun Murali, Igor Spasojevic, Vijay Kumar, Pratik Chaudhari
分類: cs.RO
原文アブストラクト
We address the challenge of task-oriented navigation in unstructured and unknown environments, where robots must incrementally build and reason on rich, metric-semantic maps in real time. Since tasks may require clarification or re-specification, it is necessary for the information in the map to be rich enough to enable generalization across a wide range of tasks. To effectively execute tasks specified in natural language, we propose a hierarchical representation built on language-embedded Gaussian splatting that enables both sparse semantic planning that lends itself to online operation and dense geometric representation for collision-free navigation. We validate the effectiveness of our method through real-world robot experiments conducted in both cluttered indoor and kilometer-scale outdoor environments, with a competitive ratio of about 60% against privileged baselines. Experiment videos and more details can be found on our project page: https://atlasnav.github.io
