MVP-Nav: Multi-layer Value Map Planner Navigator

Source

• Source name: arXiv
• Source kind: paper
• Authors / organization: Wenyuan Xie, Shaokai Wu, Yijin Zhou, Yanbiao Ji, Guodong Zhang, Bayram Bayramli, Qiuchang Li, Xunchu Zhou, Yue Ding, Hongtao Lu
• Venue: arXiv: cs.RO
• Published: 2026-06-30
• Collected: 2026-07-01
• URL: http://arxiv.org/abs/2606.31919v1
• PDF: https://arxiv.org/pdf/2606.31919v1
• DOI: UNKNOWN

Plain-Language Brief

MVP-Nav is a new framework proposed for RGB-only navigation, which addresses the challenges posed by zero-shot object goal navigation. It uses 3D foundation models to create a global spatial semantic representation and introduces a Multi-layer Value Map (MVM) to unify high-level semantic reasoning and low-level physical constraints. The framework outperforms existing depth-free methods on zero-shot object navigation benchmarks.

What the Document Says

• The paper presents MVP-Nav, a physical-aware RGB-only navigation framework.
• MVP-Nav reconstructs explicit physical occupancy from monocular observations using 3D foundation models.
• It creates a global spatial semantic representation by projecting 2D semantic instances into 3D oriented bounding boxes.
• A Multi-layer Value Map (MVM) is introduced to integrate semantic priorities and reconstructed geometry into a shared cost space.
• MVP-Nav outperforms existing depth-free methods on zero-shot object navigation benchmarks.
• The framework achieves state-of-the-art performance.

Key Concepts Mentioned

• Zero-shot Object Goal Navigation
• RGB-only perception
• 3D foundation models
• Multi-layer Value Map
• Physical occupancy
• Semantic priorities
• Global spatial semantic representation

Claims Made by the Document

• MVP-Nav addresses challenges posed by zero-shot object goal navigation.
• MVP-Nav outperforms existing depth-free methods on zero-shot object navigation benchmarks.
• MVP-Nav achieves state-of-the-art performance.

Evidence Used

• The paper describes the proposed MVP-Nav framework and its results on zero-shot object navigation benchmarks.

Methods / Approach

• Using 3D foundation models to project 2D semantic instances into 3D oriented bounding boxes.
• Introducing a Multi-layer Value Map (MVM) for integrating semantic priorities and reconstructed geometry

Limitations Stated by the Authors

• The paper does not discuss potential limitations of the MVP-Nav framework beyond what is necessary to present the research.

Limitations Noted by the Agent

Agent interpretation — not from the source.

• The agent may need to consider that the performance of MVP-Nav could vary in real-world scenarios with different lighting conditions, object types, and environments.
• The agent should also be aware that the quality of the 3D foundation models used could impact the accuracy of the physical occupancy reconstruction.

Open Questions

• How does MVP-Nav handle dynamic objects or changing environments during navigation?
• What are the computational costs associated with using the Multi-layer Value Map (MVM) in real-time navigation?

Notes for Future Q&A

May include interpretation. Labeled where it does.

• The paper focuses on zero-shot object navigation, but it does not discuss how the framework would handle dynamic objects or changing environments.

Raw Abstract / Excerpt

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.