通过视觉-语言导航提升人机协作的综述
A Survey on Improving Human Robot Collaboration through Vision-and-Language Navigation
AI总结：
本文综述了视觉-语言导航在提升人机协作中的最新进展，探讨了多机器人协作中的关键挑战，并提出通过先进NLU技术实现主动澄清和动态角色分配的未来方向。
摘要：
视觉-语言导航（VLN）是一种多模态、协作任务，要求智能体解读人类指令、在3D环境中导航，并在模糊性下有效沟通。本文综述了机器人领域最近的VLN进展，并概述了改进多机器人协作的有前景方向。尽管取得进展，当前模型在双向沟通、模糊性解决和多智能体系统中的协同决策方面仍存在困难。我们回顾了大约200篇相关文章，以深入了解当前的状况。通过此次综述，我们旨在提供一个详尽的资源，激发进一步研究在VLN和机器人学的交汇处。我们倡导未来的VLN系统应通过先进的自然语言理解（NLU）技术支持主动澄清、实时反馈和上下文推理。此外，去中心化决策框架与动态角色分配对于可扩展、高效的多机器人协作至关重要。这些创新可以显著提升人机交互（HRI）并使在医疗、物流和灾害响应等领域的实际部署成为可能。
英文摘要：
Vision-and-Language Navigation (VLN) is a multi-modal, cooperative task requiring agents to interpret human instructions, navigate 3D environments, and communicate effectively under ambiguity. This paper presents a comprehensive review of recent VLN advancements in robotics and outlines promising directions to improve multi-robot coordination. Despite progress, current models struggle with bidirectional communication, ambiguity resolution, and collaborative decision-making in the multi-agent systems. We review approximately 200 relevant articles to provide an in-depth understanding of the current landscape. Through this survey, we aim to provide a thorough resource that inspires further research at the intersection of VLN and robotics. We advocate that the future VLN systems should support proactive clarification, real-time feedback, and contextual reasoning through advanced natural language understanding (NLU) techniques. Additionally, decentralized decision-making frameworks with dynamic role assignment are essential for scalable, efficient multi-robot collaboration. These innovations can significantly enhance human-robot interaction (HRI) and enable real-world deployment in domains such as healthcare, logistics, and disaster response.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
