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

arXiv: https://arxiv.org/abs/2502.20386 · HTML: https://mlsanity.com/paper/2502.20386/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Feb 2025.

## Built on by (most cited first, 4 of 4)

- [Segment then Splat: Unified 3D Open-Vocabulary Segmentation via Gaussian Splatting](https://mlsanity.com/paper/2503.22204/) (2025)
- [Heterogeneous Robot Collaboration in Unstructured Environments with Grounded Generative Intelligence](https://mlsanity.com/paper/2510.26915/) (2025)
- [Actively Resolving Contextual Uncertainty for Underspecified Tasks in Natural Language](https://mlsanity.com/paper/2609.30428/) (2026)
- [Splatblox: Traversability-Aware Gaussian Splatting for Outdoor Robot Navigation](https://mlsanity.com/paper/2511.18525/) (2025)

## Abstract (arXiv)

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: https://ongdexter.github.io/atlasnav.