# X-Nav: Learning End-to-End Cross-Embodiment Navigation for Mobile Robots

arXiv: https://arxiv.org/abs/2507.14731 · HTML: https://mlsanity.com/paper/2507.14731/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Jul 2025.

## Built on by (most cited first, 5 of 5)

- [MM-Nav: Multi-View VLA Model for Robust Visual Navigation via Multi-Expert Learning](https://mlsanity.com/paper/2510.03142/) (2025)
- [AsyncShield: A Plug-and-Play Edge Adapter for Asynchronous Cloud-based VLA Navigation](https://mlsanity.com/paper/2604.24086/) (2026)
- [Learning Sidewalk Autopilot from Multi-Scale Imitation with Corrective Behavior Expansion](https://mlsanity.com/paper/2603.22527/) (2026)
- [CrossTracer: Cross-Embodiment Navigation via VLA Model Reasoning and Trace Residuals Adapting](https://mlsanity.com/paper/2608.06688/) (2026)
- [GLST: Global-Local Spatio-Temporal Memory Decomposition Framework for Online Chained-Goal Navigation](https://mlsanity.com/paper/2610.17927/) (2026)

## Abstract (arXiv)

Existing navigation methods are primarily designed for specific robot embodiments, limiting their generalizability across diverse robot platforms. In this paper, we introduce X-Nav, a novel framework for end-to-end cross-embodiment navigation where a single unified policy can be deployed across various embodiments for both wheeled and quadrupedal robots. X-Nav consists of two learning stages: 1) multiple expert policies are trained using deep reinforcement learning with privileged observations on a wide range of randomly generated robot embodiments; and 2) a single general policy is distilled from the expert policies via navigation action chunking with transformer (Nav-ACT). The general policy directly maps visual and proprioceptive observations to low-level control commands, enabling generalization to novel robot embodiments. Simulated experiments demonstrated that X-Nav achieved zero-shot transfer to both unseen embodiments and photorealistic environments. A scalability study showed that the performance of X-Nav improves when trained with an increasing number of randomly generated embodiments. An ablation study confirmed the design choices of X-Nav. Furthermore, real-world experiments were conducted to validate the generalizability of X-Nav in real-world environments.