{"ID":2921132,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-04T06:21:04.369492701Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.01788","arxiv_id":"2606.01788","title":"PlatonicNav: Unveiling Semantic Correspondence in Navigation with Platonic Topological Maps","abstract":"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 unify vision-and-language navigation (VLN) and object 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 explicit cross-modal supervision such as CLIP or large vision-language models, leaving open whether such grounding is possible from a purely vision-built map. To address these challenges, we extend the Platonic Representation Hypothesis to embodied navigation and recast vision-only ObjNav, cross-modal ObjNav, and VLN as three different interfaces to the same object-centric semantic manifold. We further introduce PlatonicNav, a training-free framework whose Platonic Topological Map fuses geometric and semantic node distances from a self-supervised visual encoder, and grounds language goals via blind matching without 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.","short_abstract":"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 unify vision-and-language navigat...","url_abs":"https://arxiv.org/abs/2606.01788","url_pdf":"https://arxiv.org/pdf/2606.01788v1","authors":"[\"Junlin Long\",\"Zeyu Zhang\",\"Xu Deng\",\"Yiran Wang\",\"Yue Yang\",\"Luke Borgnolo\",\"Maxwell Twelftree\",\"Yang Zhao\"]","published":"2026-06-01T07:08:50Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[\"Language Model\",\"LoRA\"]","has_code":false,"code_links":[{"ID":612565,"CreatedAt":"2026-06-02T02:42:49.606572591Z","UpdatedAt":"2026-06-02T02:42:49.606572591Z","DeletedAt":null,"paper_id":2921132,"paper_url":"https://arxiv.org/abs/2606.01788","paper_title":"PlatonicNav: Unveiling Semantic Correspondence in Navigation with Platonic Topological Maps","repo_url":"https://github.com/AIGeeksGroup/PlatonicNav","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}