思考、行动与提问：开放世界交互式个性化机器人导航
Think, Act, and Ask: Open-World Interactive Personalized Robot Navigation
AI总结：
针对零样本目标导航忽略自然语言交互与个性化识别的问题，提出ORION框架，利用大语言模型进行序列决策，协同感知、导航与通信模块，实现开放世界交互式个性化机器人导航，并验证了用户反馈对性能的提升作用。
摘要：
零样本目标导航（ZSON）使智能体能够在未知环境中导航至开放词汇表描述的目标物体。现有的ZSON研究主要侧重于遵循单一指令以寻找通用物体类别，忽略了自然语言交互的利用以及识别用户特定物体的复杂性。为了解决这些局限性，我们引入了零样本交互式个性化目标导航（ZIPON），在该任务中，机器人需要在与用户进行对话的同时导航至个性化的目标物体。为了解决ZIPON问题，我们提出了一种名为Open-woRld Interactive persOnalized Navigation（ORION）的新框架，该框架利用大语言模型（LLMs）进行序列决策，以操控用于感知、导航和通信的不同模块。实验结果表明，能够利用用户反馈的交互式智能体表现出显著的性能提升。然而，对于所有方法而言，在任务完成度与导航及交互效率之间取得良好平衡仍然是一个挑战。我们进一步提供了关于多样化用户反馈形式对智能体性能影响的更多发现。代码可在 https://github.com/sled-group/navchat 获取。
英文摘要：
Zero-Shot Object Navigation (ZSON) enables agents to navigate towards open-vocabulary objects in unknown environments. The existing works of ZSON mainly focus on following individual instructions to find generic object classes, neglecting the utilization of natural language interaction and the complexities of identifying user-specific objects. To address these limitations, we introduce Zero-shot Interactive Personalized Object Navigation (ZIPON), where robots need to navigate to personalized goal objects while engaging in conversations with users. To solve ZIPON, we propose a new framework termed Open-woRld Interactive persOnalized Navigation (ORION), which uses Large Language Models (LLMs) to make sequential decisions to manipulate different modules for perception, navigation and communication. Experimental results show that the performance of interactive agents that can leverage user feedback exhibits significant improvement. However, obtaining a good balance between task completion and the efficiency of navigation and interaction remains challenging for all methods. We further provide more findings on the impact of diverse user feedback forms on the agents' performance. Code is available at https://github.com/sled-group/navchat.
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。
