MonoDream: Monocular Vision-Language Navigation with Panoramic Dreaming

Understand

Vision-Language Navigation (VLN) tasks often leverage panoramic RGB and depth inputs to provide rich spatial cues for action planning, but these sensors can be costly or less accessible in real-world deployments.

• Recent approaches based on Vision-Language Action (VLA) models achieve strong results with monocular input, yet they still lag behind methods using panoramic RGB-D information.
• We present MonoDream, a lightweight VLA framework that enables monocular agents to learn a Unified Navigation Representation (UNR).
• This shared feature representation jointly aligns navigation-relevant visual semantics (e.g., global layout, depth, and future cues) and language-grounded action intent, enabling more reliable action prediction.