{"ID":5443853,"CreatedAt":"2026-07-01T02:07:11.383974684Z","UpdatedAt":"2026-07-03T16:19:48.203452405Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.31919","arxiv_id":"2606.31919","title":"MVP-Nav: Multi-layer Value Map Planner Navigator","abstract":"Zero-shot Object Goal Navigation (ZSON) with RGB-only perception poses a fundamental challenge for embodied agents, as the absence of explicit depth information introduces severe physical uncertainty and semantic-physical misalignment. Existing approaches either rely on high-level semantic reasoning without geometric grounding or learn end-to-end policies that lack explicit physical constraints, often resulting in semantically plausible but physically unsafe behaviors. In this paper, we propose MVP-Nav, a physical-aware RGB-only navigation framework that aligns perception, planning, and control with the real 3D world. MVP-Nav reconstructs explicit physical occupancy from monocular observations by leveraging 3D foundation models to project 2D semantic instances into 3D oriented bounding boxes, forming a global spatial semantic representation. To unify high-level semantic reasoning and low-level physical constraints, we introduce a Multi-layer Value Map (MVM) that integrates semantic priorities and reconstructed geometry into a shared cost space, enabling physically grounded geometric planning. Extensive experiments on zero-shot object navigation benchmarks demonstrate that MVP-Nav significantly outperforms existing depth-free methods, achieving state-of-the-art performance and validating that structured physical priors can effectively compensate for the absence of active depth sensors.","short_abstract":"Zero-shot Object Goal Navigation (ZSON) with RGB-only perception poses a fundamental challenge for embodied agents, as the absence of explicit depth information introduces severe physical uncertainty and semantic-physical misalignment. Existing approaches either rely on high-level semantic reasoning without geometric g...","url_abs":"https://arxiv.org/abs/2606.31919","url_pdf":"https://arxiv.org/pdf/2606.31919v1","authors":"[\"Wenyuan Xie\",\"Shaokai Wu\",\"Yijin Zhou\",\"Yanbiao Ji\",\"Guodong Zhang\",\"Bayram Bayramli\",\"Qiuchang Li\",\"Xunchu Zhou\",\"Yue Ding\",\"Hongtao Lu\"]","published":"2026-06-30T16:25:47Z","proceeding":"cs.RO","tasks":"[\"cs.RO\",\"cs.AI\",\"cs.CV\"]","methods":"[]","has_code":false}