# Rethinking the Embodied Gap in Vision-and-Language Navigation: A Holistic Study of Physical and Visual Disparities

arXiv: https://arxiv.org/abs/2507.13019 · HTML: https://mlsanity.com/paper/2507.13019/

## Key facts

- Cited by 25 papers in the map (data through 14 Oct 2026).
- Placed in Spatial and embodied reasoning, one of 17 directions in LLM reasoning.
- Builds on 58 of its 65 references that are in the map.
- First posted on arXiv 17 Jul 2025.

## Placed in

- LLM reasoning / [Spatial and embodied reasoning](https://mlsanity.com/lr/spatial-and-embodied-reasoning/)

## Builds on (most cited first, 30 of 58)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [Denoising Diffusion Probabilistic Models](https://mlsanity.com/paper/2006.11239/) (2020)
- [3D Gaussian Splatting for Real-Time Radiance Field Rendering](https://mlsanity.com/paper/2308.04079/) (2023)
- [OpenVLA: An Open-Source Vision-Language-Action Model](https://mlsanity.com/paper/2406.09246/) (2024)
- [Diffusion Policy: Visuomotor Policy Learning via Action Diffusion](https://mlsanity.com/paper/2303.04137/) (2023)
- [Matterport3D: Learning from RGB-D Data in Indoor Environments](https://mlsanity.com/paper/1709.06158/) (2017)
- [Habitat: A Platform for Embodied AI Research](https://mlsanity.com/paper/1904.01201/) (2019)
- [Vision-and-Language Navigation: Interpreting visually-grounded navigation instructions in real environments](https://mlsanity.com/paper/1711.07280/) (2017)
- [EVA-CLIP: Improved Training Techniques for CLIP at Scale](https://mlsanity.com/paper/2303.15389/) (2023)
- [ALFRED: A Benchmark for Interpreting Grounded Instructions for Everyday Tasks](https://mlsanity.com/paper/1912.01734/) (2019)
- [Scaffold-GS: Structured 3D Gaussians for View-Adaptive Rendering](https://mlsanity.com/paper/2312.00109/) (2023)
- [Habitat-Matterport 3D Dataset (HM3D): 1000 Large-scale 3D Environments for Embodied AI](https://mlsanity.com/paper/2109.08238/) (2021)
- [LLaMA-VID: An Image is Worth 2 Tokens in Large Language Models](https://mlsanity.com/paper/2311.17043/) (2023)
- [Beyond the Nav-Graph: Vision-and-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2004.02857/) (2020)
- [Room-Across-Room: Multilingual Vision-and-Language Navigation with Dense Spatiotemporal Grounding](https://mlsanity.com/paper/2010.07954/) (2020)
- [ProcTHOR: Large-Scale Embodied AI Using Procedural Generation](https://mlsanity.com/paper/2206.06994/) (2022)
- [Visual Language Maps for Robot Navigation](https://mlsanity.com/paper/2210.05714/) (2022)
- [REVERIE: Remote Embodied Visual Referring Expression in Real Indoor Environments](https://mlsanity.com/paper/1904.10151/) (2019)
- [Long-CLIP: Unlocking the Long-Text Capability of CLIP](https://mlsanity.com/paper/2403.15378/) (2024)
- [NavGPT: Explicit Reasoning in Vision-and-Language Navigation with Large Language Models](https://mlsanity.com/paper/2305.16986/) (2023)
- [Navigation World Models](https://mlsanity.com/paper/2412.03572/) (2024)
- [Vision-and-Dialog Navigation](https://mlsanity.com/paper/1907.04957/) (2019)
- [NoMaD: Goal Masked Diffusion Policies for Navigation and Exploration](https://mlsanity.com/paper/2310.07896/) (2023)
- [Learning to Navigate Unseen Environments: Back Translation with Environmental Dropout](https://mlsanity.com/paper/1904.04195/) (2019)
- [NaVid: Video-based VLM Plans the Next Step for Vision-and-Language Navigation](https://mlsanity.com/paper/2402.15852/) (2024)
- [NaVILA: Legged Robot Vision-Language-Action Model for Navigation](https://mlsanity.com/paper/2412.04453/) (2024)
- [VLFM: Vision-Language Frontier Maps for Zero-Shot Semantic Navigation](https://mlsanity.com/paper/2312.03275/) (2023)
- [ETPNav: Evolving Topological Planning for Vision-Language Navigation in Continuous Environments](https://mlsanity.com/paper/2304.03047/) (2023)

## Built on by (most cited first, 25 of 25)

- [VLNVerse: A Benchmark for Vision-Language Navigation with Versatile, Embodied, Realistic Simulation and Evaluation](https://mlsanity.com/paper/2512.19021/) (2025)
- [Qwen-RobotNav Technical Report: A Scalable Navigation Model Designed for an Agentic Navigation System](https://mlsanity.com/paper/2606.18112/) (2026)
- [TIC-VLA: A Think-in-Control Vision-Language-Action Model for Robot Navigation in Dynamic Environments](https://mlsanity.com/paper/2602.02459/) (2026)
- [CapNav: Benchmarking Vision Language Models on Capability-conditioned Indoor Navigation](https://mlsanity.com/paper/2602.18424/) (2026)
- [textscNaVIDA: Vision-Language Navigation with Inverse Dynamics Augmentation](https://mlsanity.com/paper/2601.18188/) (2026)
- [One Agent to Guide Them All: Empowering MLLMs for Vision-and-Language Navigation via Explicit World Representation](https://mlsanity.com/paper/2602.15400/) (2026)
- [MM-Nav: Multi-View VLA Model for Robust Visual Navigation via Multi-Expert Learning](https://mlsanity.com/paper/2510.03142/) (2025)
- [Ask When It Pays: Cost-Aware Open-Ended Interaction for Instance Goal Navigation](https://mlsanity.com/paper/2606.03175/) (2026)
- [All-day Multi-scenes Lifelong Vision-and-Language Navigation with Tucker Adaptation](https://mlsanity.com/paper/2603.14276/) (2026)
- [From Failure to Feedback: Group Revision Unlocks Hard Cases in Object-Level Grounding](https://mlsanity.com/paper/2605.15951/) (2026)
- [P2DNav: Panorama-to-Downview Reasoning for Zero-shot Vision-and-Language Navigation](https://mlsanity.com/paper/2605.19634/) (2026)
- [OmniEcho: Audio-Visual Spatial Understanding for Omni-Modal Embodied Agents](https://mlsanity.com/paper/2609.23407/) (2026)
- [User-Feedback-Driven Adaptation for Vision-and-Language Navigation](https://mlsanity.com/paper/2512.10322/) (2025)
- [What Limits Vision-and-Language Navigation ?](https://mlsanity.com/paper/2605.13328/) (2026)
- [Image2Sim: Scaling Embodied Navigation via Generative Neural Simulator](https://mlsanity.com/paper/2607.05765/) (2026)
- [A Comprehensive Survey and Systematic Real-World Evaluation of Embodied Vision-and-Language Navigation](https://mlsanity.com/paper/2607.09792/) (2026)
- [TANGO: Humanoid Navigation in Cluttered Environments with a Whole-Body Vision-Language-Action Model](https://mlsanity.com/paper/2609.09158/) (2026)
- [LatentPilot: Scene-Aware Vision-and-Language Navigation by Dreaming Ahead with Latent Visual Reasoning](https://mlsanity.com/paper/2603.29165/) (2026)
- [GIST: Multimodal Knowledge Extraction and Spatial Grounding via Intelligent Semantic Topology](https://mlsanity.com/paper/2604.15495/) (2026)
- [CoFL-S: Spatially Queryable Sector Flow Fields for Local Language-Conditioned Navigation](https://mlsanity.com/paper/2607.02222/) (2026)
- [NavVerse: Benchmarking Indoor-to-Outdoor Embodied Navigation in Continuous Robot Simulation](https://mlsanity.com/paper/2607.19695/) (2026)
- [EA-Nav: Learning Safe Visual Navigation Policies with Embodiment Awareness](https://mlsanity.com/paper/2607.19880/) (2026)
- [RTNav: Towards Real-Time Zero-Shot Object Navigation](https://mlsanity.com/paper/2608.26496/) (2026)
- [Systematic Multi-Agent Vision-and-Language Navigation: Formulation, Benchmark, and Method](https://mlsanity.com/paper/2609.35965/) (2026)
- [FireNav: Decision-Oriented Reconstruction Evidence Routing for Vision-Language Navigation in Indoor Fire Environments](https://mlsanity.com/paper/2610.15595/) (2026)

## Abstract (arXiv)

Recent Vision-and-Language Navigation (VLN) advancements are promising, but their idealized assumptions about robot movement and control fail to reflect physically embodied deployment challenges. To bridge this gap, we introduce VLN-PE, a physically realistic VLN platform supporting humanoid, quadruped, and wheeled robots. For the first time, we systematically evaluate several ego-centric VLN methods in physical robotic settings across different technical pipelines, including classification models for single-step discrete action prediction, a diffusion model for dense waypoint prediction, and a train-free, map-based large language model (LLM) integrated with path planning. Our results reveal significant performance degradation due to limited robot observation space, environmental lighting variations, and physical challenges like collisions and falls. This also exposes locomotion constraints for legged robots in complex environments. VLN-PE is highly extensible, allowing seamless integration of new scenes beyond MP3D, thereby enabling more comprehensive VLN evaluation. Despite the weak generalization of current models in physical deployment, VLN-PE provides a new pathway for improving cross-embodiment's overall adaptability. We hope our findings and tools inspire the community to rethink VLN limitations and advance robust, practical VLN models. The code is available at https://crystalsixone.github.io/vln_pe.github.io/.