{"paper":{"title":"PathPainter: Transferring the Generalization Ability of Image Generation Models to Embodied Navigation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Image generation models interpret natural language to create traversability masks on bird's-eye-view images for guiding robot navigation.","cross_cats":[],"primary_cat":"cs.RO","authors_text":"Fei Gao, Mo Zhu, Weiqi Gai, Xijie Huang, Xin Zhou, Yijin Wang, Yuru Tian, Yuze Wu","submitted_at":"2026-05-08T09:33:19Z","abstract_excerpt":"Bird's-eye-view (BEV) images have been widely demonstrated to provide valuable prior information for navigation. Given the global information provided by such views, two key challenges remain: how to fully exploit this information and how to reliably use it during execution. In this paper, we propose a navigation system that uses BEV images as global priors and is designed for ground and near-ground robotic platforms. The system employs an image generation model to interpret human intent from natural language, identify the target destination, and generate traversability masks. During execution"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"This work demonstrates how the world-understanding capabilities of foundation models can be transferred to embodied navigation, enabling robots to benefit from the strong generalization ability of existing image generation models.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"The image generation model can reliably interpret natural language intent and produce accurate traversability masks from BEV images that a conventional local motion planner can use successfully for long-range tasks.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"PathPainter transfers image generation models to embodied navigation by generating traversability masks from BEV images and language instructions while using cross-view localization to reduce odometry drift.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Image generation models interpret natural language to create traversability masks on bird's-eye-view images for guiding robot navigation.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"b5ec935e2e8cf87c960f57cdd7063ec788e6324bb29acded58b06cd408ac5dfa"},"source":{"id":"2605.07496","kind":"arxiv","version":2},"verdict":{"id":"e7c28f6f-7909-4c7d-88cc-5bd55a3fc53d","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-11T02:02:21.175119Z","strongest_claim":"This work demonstrates how the world-understanding capabilities of foundation models can be transferred to embodied navigation, enabling robots to benefit from the strong generalization ability of existing image generation models.","one_line_summary":"PathPainter transfers image generation models to embodied navigation by generating traversability masks from BEV images and language instructions while using cross-view localization to reduce odometry drift.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"The image generation model can reliably interpret natural language intent and produce accurate traversability masks from BEV images that a conventional local motion planner can use successfully for long-range tasks.","pith_extraction_headline":"Image generation models interpret natural language to create traversability masks on bird's-eye-view images for guiding robot navigation."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.07496/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"claim_evidence","ran_at":"2026-05-20T10:42:02.905510Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-20T05:42:43.105033Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T16:31:18.816844Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T11:44:05.509795Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"efab0d344dce0fcdf46dbb46df2f95116612ed889ca375f08eda37c155943e42"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":2,"snapshot_sha256":"d9a98c4e1dec6648fd98f4bd3548d4af202fcaaf84cdfbae7d3d8b5da48356c1"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"}