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.

翻译：现有的导航方法主要针对特定机器人具身形态设计，限制了其在多样化机器人平台间的泛化能力。本文提出X-Nav，一种新颖的端到端跨具身导航框架，通过单一统一策略即可部署于轮式与四足机器人的多种具身形态。X-Nav包含两个学习阶段：1）利用深度强化学习，在大量随机生成的机器人具身形态上，基于特权观测训练多个专家策略；2）通过基于Transformer的导航动作分块（Nav-ACT）技术，从专家策略中蒸馏出单一通用策略。该通用策略直接将视觉与本体感知观测映射为底层控制指令，从而实现对新型机器人具身形态的泛化。仿真实验表明，X-Nav在未见过的具身形态和逼真环境中均实现了零样本迁移。可扩展性研究显示，随着训练所用随机生成具身形态数量的增加，X-Nav的性能持续提升。消融实验验证了X-Nav的设计选择。此外，通过真实环境实验进一步证实了X-Nav在现实场景中的泛化能力。