A Survey on Improving Human Robot Collaboration through Vision-and-Language Navigation

November 06, 2025 · The Cartographer · 🏛 arXiv.org

"No code URL or promise found in abstract"
"Title-pattern auto-detect: A Survey on Improving Human Robot Collaboration through Vision-and-Language Navigation"

Evidence collected by the PWNC Scanner

Authors Nivedan Yakolli, Avinash Gautam, Abhijit Das, Yuankai Qi, Virendra Singh Shekhawat arXiv ID 2512.00027 Category cs.RO: Robotics Cross-listed cs.AI, cs.CV, cs.HC Citations 0 Venue arXiv.org Last Checked 5 days ago

Abstract

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.