机器人导航中的语言与规划：对前沿模型的多语言评估
Language and Planning in Robotic Navigation: A Multilingual Evaluation of State-of-the-Art Models
AI总结：
本研究首次将阿拉伯语引入机器人视觉-语言导航领域，采用NavGPT框架结合R2R数据集，评估多款前沿多语言及阿拉伯语大模型的导航规划能力，发现部分模型阿拉伯语推理存在局限，为相关模型优化指明方向。
摘要：
GPT-4等大语言模型（LLM）在跨越多个领域的海量数据集上训练，在各类任务中展现出出色的推理、理解与规划能力。本研究是首个将阿拉伯语融入机器人视觉-语言导航（VLN）领域的工作，该方向在现有研究中明显未被充分探索。我们对包括GPT-4o mini、Llama 3 8B、Phi-3 medium 14B在内的前沿多语言小语言模型（SLM），以及以阿拉伯语为核心的LLM Jais开展了综合评估。我们的方法采用NavGPT框架——一种纯基于LLM的指令跟随导航智能体，使用R2R数据集通过零样本序列动作预测来评估语言对导航推理的影响。通过综合实验我们证明，当提供英语和阿拉伯语指令时，我们的框架能够完成导航任务的高层规划。但部分模型由于自身能力局限、性能欠佳以及解析问题，在阿拉伯语的推理与规划上表现不佳。这些发现凸显了增强语言模型的规划与推理能力对实现有效导航的重要性，强调这是未来发展的关键方向，同时也释放了阿拉伯语模型在实际应用中产生价值的潜力。
英文摘要：
Large Language Models (LLMs) such as GPT-4, trained on huge amount of datasets spanning multiple domains, exhibit significant reasoning, understanding, and planning capabilities across various tasks. This study presents the first-ever work in Arabic language integration within the Vision-and-Language Navigation (VLN) domain in robotics, an area that has been notably underexplored in existing research. We perform a comprehensive evaluation of state-of-the-art multi-lingual Small Language Models (SLMs), including GPT-4o mini, Llama 3 8B, and Phi-3 medium 14B, alongside the Arabic-centric LLM, Jais. Our approach utilizes the NavGPT framework, a pure LLM-based instruction-following navigation agent, to assess the impact of language on navigation reasoning through zero-shot sequential action prediction using the R2R dataset. Through comprehensive experiments, we demonstrate that our framework is capable of high-level planning for navigation tasks when provided with instructions in both English and Arabic. However, certain models struggled with reasoning and planning in the Arabic language due to inherent limitations in their capabilities, sub-optimal performance, and parsing issues. These findings highlight the importance of enhancing planning and reasoning capabilities in language models for effective navigation, emphasizing this as a key area for further development while also unlocking the potential of Arabic-language models for impactful real-world applications.
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。
