AI RESEARCH
Uni-LaViRA: Language-Vision-Robot Actions Translation for Unified Embodied Navigation
arXiv CS.CV
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ArXi:2605.27582v1 Announce Type: cross 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.