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

TL;DR

This survey reviews recent advancements in Vision-and-Language Navigation (VLN) for robotics, highlighting challenges and proposing future directions to improve multi-robot collaboration and human-robot interaction.

Contribution

It provides an extensive review of nearly 200 articles on VLN in robotics and outlines promising research directions for enhancing multi-robot coordination and communication.

Findings

Current models struggle with ambiguity resolution and bidirectional communication.

Decentralized decision-making and dynamic role assignment are crucial for scalable collaboration.

Future systems should support proactive clarification and real-time feedback.

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.