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Source note3 min read

MVP-Nav: Multi-layer Value Map Planner Navigator

Source

arXiv

arxiv.org/abs/2606.31919v1

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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

The paper presents a new navigation framework called MVP-Nav which addresses the challenge of zero-shot object goal navigation using only RGB perception. The framework aligns perception, planning, and control with the real 3D world by reconstructing explicit physical occupancy from monocular observations and introducing a Multi-layer Value Map (MVM) to unify high-level semantic reasoning and low-level physical constraints.

What the Document Says

  • The paper discusses the challenge of zero-shot object goal navigation with RGB-only perception, due to the absence of explicit depth information leading to severe physical uncertainty and semantic-physical misalignment.
  • MVP-Nav is proposed as a solution, aligning perception, planning, and control with the real 3D world by reconstructing physical occupancy from monocular observations using 3D foundation models.
  • The framework forms a global spatial semantic representation by projecting 2D semantic instances into 3D oriented bounding boxes.
  • To unify high-level semantic reasoning and low-level physical constraints, a Multi-layer Value Map (MVM) is introduced to integrate semantic priorities and reconstructed geometry into a shared cost space.
  • Extensive experiments on zero-shot object navigation benchmarks show that MVP-Nav outperforms existing depth-free methods, achieving state-of-the-art performance.

Key Concepts Mentioned

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

Claims Made by the Document

  • Existing approaches either rely on high-level semantic reasoning without geometric grounding or lack explicit physical constraints.
  • MVP-Nav significantly outperforms existing depth-free methods and achieves state-of-the-art performance.

Evidence Used

  • The paper describes the challenges faced by existing approaches and presents MVP-Nav as a solution.
  • Experimental results on zero-shot object navigation benchmarks are presented to demonstrate the superiority of MVP-Nav over existing depth-free methods.

Methods / Approach

  • Reconstructing physical occupancy from monocular observations
  • Projecting 2D semantic instances into 3D oriented bounding boxes
  • Forming a global spatial semantic representation
  • Introducing a Multi-layer Value Map (MVM) to integrate semantic priorities and reconstructed geometry

Limitations Stated by the Authors

The paper does not discuss potential limitations of MVP-Nav in terms of computational complexity or robustness in various environments.

Limitations Noted by the Agent

Agent interpretation — not from the source.

The agent should be aware that the performance of MVP-Nav might vary depending on factors such as the complexity of the environment, availability of 3D foundation models, and the quality of monocular observations.

Open Questions

  • What is the computational complexity of MVP-Nav?
  • How robust is MVP-Nav in various environments?
  • What are potential improvements or modifications to MVP-Nav?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The paper does not provide information about the computational complexity or robustness of MVP-Nav. These aspects could be investigated further.

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.