标题： 分解与构建：基于技能的视觉-语言导航智能体混合 显示英文标题

标题： Breaking Down and Building Up: Mixture of Skill-Based Vision-and-Language Navigation Agents

摘要： 视觉与语言导航（VLN）在使代理理解自然语言指令并导航复杂三维环境方面提出了重大挑战。 尽管最近的进展是由大规模预训练和数据增强推动的，但当前方法在推广到未见过的场景时仍然存在困难，尤其是在需要复杂的空间和时间推理时。 在本工作中，我们提出了SkillNav，一种模块化框架，该框架将结构化的基于技能的推理引入基于Transformer的VLN代理。 我们的方法将导航分解为一组可解释的基本技能（例如，垂直移动、区域和区域识别、停止和暂停），每个技能由一个专用代理处理。 然后，我们引入了一个新的零样本视觉-语言模型（VLM）路由器，该路由器通过将子目标与视觉观察和历史动作对齐，在每个时间步动态选择最合适的代理。 SkillNav在R2R基准上实现了新的最先进性能，并展示了对包含新指令风格和未见过环境的GSA-R2R基准的强大泛化能力。 显示英文摘要

摘要： Vision-and-Language Navigation (VLN) poses significant challenges in enabling agents to interpret natural language instructions and navigate complex 3D environments. While recent progress has been driven by large-scale pre-training and data augmentation, current methods still struggle to generalize to unseen scenarios, particularly when complex spatial and temporal reasoning is required. In this work, we propose SkillNav, a modular framework that introduces structured, skill-based reasoning into Transformer-based VLN agents. Our method decomposes navigation into a set of interpretable atomic skills (e.g., Vertical Movement, Area and Region Identification, Stop and Pause), each handled by a specialized agent. We then introduce a novel zero-shot Vision-Language Model (VLM)-based router, which dynamically selects the most suitable agent at each time step by aligning sub-goals with visual observations and historical actions. SkillNav achieves a new state-of-the-art performance on the R2R benchmark and demonstrates strong generalization to the GSA-R2R benchmark that includes novel instruction styles and unseen environments.