PlatonicNav: Unveiling Semantic Correspondence in Navigation with Platonic Topological Maps
Summary
PlatonicNav introduces a training-free framework for embodied navigation that uses vision-only semantic maps and blind matching to ground language goals, achieving generalization across tasks and embodiments without explicit cross-modal training.
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Paper page - PlatonicNav: Unveiling Semantic Correspondence in Navigation with Platonic Topological Maps
Source: https://huggingface.co/papers/2606.01788
Abstract
A training-free framework for embodied navigation that uses a vision-only approach to create semantic maps and ground language goals through blind matching without paired vision-language data.
Embodied visual navigation, where an agent perceives a complex environment and acts to reach a goal from raw sensory input, underpins a wide range of applications such as household service robotics, assistive robotics, and large-scale autonomous exploration. However, recent attempts to unifyvision-and-language navigation(VLN) andobject goal navigation(ObjNav) remain at the level of architectural fusion, mixed-task training, and large vision-language pretraining, without examining whether independently trained vision and language encoders may already share a common semantic structure. Moreover, even object-centric topological maps still ground language goals through explicitcross-modal supervisionsuch asCLIPorlarge vision-language models, leaving open whether such grounding is possible from a purely vision-built map. To address these challenges, we extend thePlatonic Representation Hypothesisto embodied navigation and recastvision-only ObjNav,cross-modal ObjNav, andVLNas three different interfaces to the same object-centricsemantic manifold. We further introduce PlatonicNav, a training-free framework whosePlatonic Topological Mapfuses geometric and semantic node distances from aself-supervised visual encoder, and grounds language goals viablind matchingwithout any paired vision-language data. Extensive experiments on simulation benchmarks including HM3D-IIN, OVON, and R2R-CE on MP3D, together with deployment on Unitree Go2, demonstrate that PlatonicNav generalizes across tasks, modalities, and embodiments without explicit cross-modal training. Code: https://github.com/AIGeeksGroup/PlatonicNav. Website: https://aigeeksgroup.github.io/PlatonicNav.
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