# LaViRA: Language-Vision-Robot Actions Translation for Zero-Shot Vision Language Navigation in Continuous Environments

arXiv: https://arxiv.org/abs/2510.19655 · HTML: https://mlsanity.com/paper/2510.19655/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Oct 2025.

## Built on by (most cited first, 9 of 9)

- [UrbanNav: Learning Language-Guided Urban Navigation from Web-Scale Human Trajectories](https://mlsanity.com/paper/2512.09607/) (2025)
- [AgenticNav: Zero-Shot Vision-and-Language Navigation as a Tool-Calling Harness](https://mlsanity.com/paper/2606.10577/) (2026)
- [NavWM: A Unified Navigation World Model for Foresight-Driven Planning](https://mlsanity.com/paper/2606.24101/) (2026)
- [Uni-LaViRA: Language-Vision-Robot Actions Translation for Unified Embodied Navigation](https://mlsanity.com/paper/2605.27582/) (2026)
- [HarnessVLN: Unifying Training-Free Embodied Navigation through an Agent Harness](https://mlsanity.com/paper/2609.15195/) (2026)
- [Goal2Pixel: Grounding Goals to Pixels for Vision-Language Navigation](https://mlsanity.com/paper/2606.01621/) (2026)
- [HAM-VLN: Harnessing Hierarchical Agentic Memory for Zero-Shot Vision-and-Language Navigation](https://mlsanity.com/paper/2607.29600/) (2026)
- [ManiLong-Shot: Interaction-Aware One-Shot Imitation Learning for Long-Horizon Manipulation](https://mlsanity.com/paper/2512.16302/) (2025)
- [One MLLM, One Call: Efficient Zero-Shot Vision-and-Language Navigation via Spatial-Aware Waypoints](https://mlsanity.com/paper/2609.06476/) (2026)

## Abstract (arXiv)

LaViRA: Zero-shot Vision-and-Language Navigation in Continuous Environments (VLN-CE) requires an agent to navigate unseen environments based on natural language instructions without any prior training. Current methods face a critical trade-off: either rely on environment-specific waypoint predictors that limit scene generalization, or underutilize the reasoning capabilities of large models during navigation. We introduce LaViRA, a simple yet effective zero-shot framework that addresses this dilemma by decomposing action into a coarse-to-fine hierarchy: Language Action for high-level planning, Vision Action for middle-level perceptual grounding, and Robot Action for low-level control. This modular decomposition allows us to leverage the distinct strengths of different scales of Multimodal Large Language Models (MLLMs) at each stage, creating a system that is powerful in its reasoning, grounding and practical control. LaViRA significantly outperforms existing state-of-the-art methods on the VLN-CE benchmark, demonstrating superior generalization capabilities in unseen environments, while maintaining transparency and efficiency for real-world deployment. Project page: https://robo-lavira.github.io/lavira-zs-vln/