ATLAS Navigator: Active Task-driven LAnguage-embedded Gaussian Splatting

Abstract:We address the challenge of task-oriented navigation in unstructured and unknown environments, where robots must incrementally build and reason on informative 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 on a ground robot equipped with only vision-based sensing. The experiments are conducted in both cluttered indoor and kilometer-scale outdoor environments, covering more than 2 km and 47,000 square meters of area. We achieve a competitive ratio of about 59% relative to the shortest possible path, demonstrating efficient exploration and semantic navigation in challenging real-world environments. Experiment videos and more details can be found on our project page: this https URL.

Robotics (cs.RO)

arXiv:2502.20386 [cs.RO]

(or arXiv:2502.20386v2 [cs.RO] for this version)

https://doi.org/10.48550/arXiv.2502.20386 Focus to learn more arXiv-issued DOI via DataCite

https://doi.org/10.1109/TFR.2026.3732100 Focus to learn more DOI(s) linking to related resources

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