# VISTA: Open-Vocabulary, Task-Relevant Robot Exploration with Online Semantic Gaussian Splatting

arXiv: https://arxiv.org/abs/2507.01125 · HTML: https://mlsanity.com/paper/2507.01125/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Jul 2025.

## Built on by (most cited first, 7 of 7)

- [π, But Make It Fly: Physics-Guided Transfer of VLA Models to Aerial Manipulation](https://mlsanity.com/paper/2603.25038/) (2026)
- [SINGER: An Onboard Generalist Vision-Language Navigation Policy for Drones](https://mlsanity.com/paper/2509.18610/) (2025)
- [Coverage Optimization for Camera View Selection](https://mlsanity.com/paper/2604.05259/) (2026)
- [GaussLite: Online Task-Conditioned 3D Gaussian Splatting for Real-Time Robotic Mapping](https://mlsanity.com/paper/2606.30809/) (2026)
- [Semantic-Aware Guided Drone Exploration for Language-Conditioned 3D Indoor Mapping](https://mlsanity.com/paper/2605.23160/) (2026)
- [CognitiveReality: Robot-Agnostic Semantic Gaussian Mapping with an LLM Agent for Immersive Collaborative VR Teleoperation](https://mlsanity.com/paper/2609.31418/) (2026)
- [AGILE-GS: Anchor-Guided Fast Next-Best-View Selection for Active 3D Gaussian Splatting](https://mlsanity.com/paper/2609.34176/) (2026)

## Abstract (arXiv)

We present VISTA (Viewpoint-based Image selection with Semantic Task Awareness), an active exploration method for robots to plan informative trajectories that improve 3D map quality in areas most relevant for task completion. Given an open-vocabulary search instruction (e.g., "find a person"), VISTA enables a robot to explore its environment to search for the object of interest, while simultaneously building a real-time semantic 3D Gaussian Splatting reconstruction of the scene. The robot navigates its environment by planning receding-horizon trajectories that prioritize semantic similarity to the query and exploration of unseen regions of the environment. To evaluate trajectories, VISTA introduces a novel, efficient viewpoint-semantic coverage metric that quantifies both the geometric view diversity and task relevance in the 3D scene. On static datasets, our coverage metric outperforms state-of-the-art baselines, FisherRF and Bayes' Rays, in computation speed and reconstruction quality. In quadrotor hardware experiments, VISTA achieves 6x higher success rates in challenging maps, compared to baseline methods, while matching baseline performance in less challenging maps. Lastly, we show that VISTA is platform-agnostic by deploying it on a quadrotor drone and a Spot quadruped robot. Open-source code will be released upon acceptance of the paper.