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

TL;DR

This paper introduces VLN-PE, a realistic robotic platform for vision-and-language navigation, revealing significant challenges in deploying current models physically and providing a foundation for more robust, adaptable VLN systems.

Contribution

We present VLN-PE, a comprehensive, physically realistic VLN platform that enables systematic evaluation of navigation models in real robot settings, highlighting key physical and environmental challenges.

Findings

Performance drops due to limited observation space and lighting variations.

Physical challenges like collisions and falls significantly impact navigation success.

Legged robots face locomotion constraints in complex environments.

Abstract

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/.