Uni-LaViRA: Language-Vision-Robot Actions Translation for Unified Embodied Navigation
Uni-LaViRA is a unified agentic architecture for embodied navigation that reduces navigation decision to a single Language-Vision-Robot Actions Translation. It leverages pretrained MLLMs in a zero-shot manner across four task families and four real robots, using TODO List Memory and Second Chance Backtrack mechanisms to achieve self-correcting navigation without training.
Article intelligence
Key points
- Generality in navigation can be obtained structurally, not only through data scale.
- Uni-LaViRA decomposes navigation into a language action (semantic direction) and a vision action (pixel target), both within the output manifold of MLLMs.
- Unifies four task families (VLN-CE, ObjectNav, EQA, Aerial-VLN) and four heterogeneous robots zero-shot.
- Achieves competitive results without training, including 60.7% SR on VLN-CE R2R.
Why it matters
This matters because generality in navigation can be obtained structurally, not only through data scale.
Technical impact
May affect model selection, inference cost, product capability, and evaluation benchmarks.
[2605.27582] Uni-LaViRA: Language-Vision-Robot Actions Translation for Unified Embodied Navigation
[Submitted on 26 May 2026]
Title:Uni-LaViRA: Language-Vision-Robot Actions Translation for Unified Embodied Navigation
View a PDF of the paper titled Uni-LaViRA: Language-Vision-Robot Actions Translation for Unified Embodied Navigation, by Hongyu Ding and 15 other authors
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Abstract:Embodied navigation requires an agent to map language and visual observations to a stream of spatial actions that drive a real robot through environments it has never seen. The dominant approach has been to scale vision-language-action (VLA) foundation models on ever-larger collections of robot trajectories. This paper argues that, for navigation specifically, generality can be obtained structurally, not only through data scale. The underlying decision structure of navigation reduces to a single Language-Vision-Robot Actions Translation. The language action emits semantic-level directional command and the vision action emits a pixel-level visual target. Both outputs lie inside the natural output manifold of pretrained multimodal large language models (MLLMs), so the task can be reasoned about by an agent rather than learned from robot data. Therefore, we present Uni-LaViRA, a unified agentic architecture that extends the same insight to four task families (VLN-CE, ObjectNav, EQA, and Aerial-VLN) and to four heterogeneous real robots (Wheeled, Quadruped, Humanoid robot, and a self-built UAV) in a zero-shot manner. Two agent-loop mechanisms make this unification practical. TODO List Memory (TDM) rewrites a structured checklist of pending sub-goals at every step, reciting the unfinished items back into the agent's most recent attention window. Second Chance Backtrack (SCB) rolls the robot back to the pre-error state and conditions the agent's next plan on the failed sub-trajectory, turning single-pass navigation into a self-correcting process. With zero training effort, Uni-LaViRA reaches 60.7% SR on VLN-CE R2R, 51.3% on VLN-CE RxR, 77.7% on HM3D-v2, 60.0% on HM3D-OVON, 54.7% on MP3D-EQA, and 40.0% on OpenUAV, matching or even surpassing recent training navigation foundation models that consume millions of samples and thousands of GPU-hours.
Comments: Project page: this https URL
Subjects:
Robotics (cs.RO); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2605.27582 [cs.RO]
(or arXiv:2605.27582v1 [cs.RO] for this version)
https://doi.org/10.48550/arXiv.2605.27582
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Hongyu Ding [view email] [v1] Tue, 26 May 2026 18:52:04 UTC (7,856 KB)
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