CARTIER：面向机器人指令执行的制图语言推理
CARTIER: Cartographic lAnguage Reasoning Targeted at Instruction Execution for Robots
AI总结：
针对机器人导航中自然对话式隐式指令解析难题，提出CARTIER方法，借助LLM结合场景物体上下文解读交互，在AI2Thor家居场景中查询解析可靠性较现有方法最高提升42%。
摘要：
本研究探索了大语言模型（LLM）解决导航领域中空间规划与自然语言界面交叉问题的能力。我们聚焦于遵循复杂指令的任务，这类指令更接近自然对话，而非机器人领域常见的传统显式程序指令。与多数以往研究采用简单祈使式导航指令（如“去冰箱那里”）不同，我们研究通过对话交互获取的隐式指令。我们利用3D模拟器AI2Thor大规模构建家居查询场景，并针对40种物体类型补充了复杂语言查询以扩展场景。实验证明，采用我们提出的CARTIER（面向机器人指令执行的制图语言推理）方法的机器人，通过发挥LLM结合场景中物体上下文解读用户交互的能力，其解析描述性语言查询的可靠性比现有基于LLM的方法最高高出42%。
英文摘要：
This work explores the capacity of large language models (LLMs) to address problems at the intersection of spatial planning and natural language interfaces for navigation. We focus on following complex instructions that are more akin to natural conversation than traditional explicit procedural directives typically seen in robotics. Unlike most prior work where navigation directives are provided as simple imperative commands (e.g., "go to the fridge"), we examine implicit directives obtained through conversational interactions.We leverage the 3D simulator AI2Thor to create household query scenarios at scale, and augment it by adding complex language queries for 40 object types. We demonstrate that a robot using our method CARTIER (Cartographic lAnguage Reasoning Targeted at Instruction Execution for Robots) can parse descriptive language queries up to 42% more reliably than existing LLM-enabled methods by exploiting the ability of LLMs to interpret the user interaction in the context of the objects in the scenario.
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。
