Abstract
This paper presents a framework that can interpret humans' navigation commands containing temporal elements and directly translate their natural language instructions into robot motion planning. Central to our framework is utilizing Large Language Models (LLMs). To enhance the reliability of LLMs in the framework and improve user experience, we propose methods to resolve the ambiguity in natural language instructions and capture user preferences. The process begins with an ambiguity classifier, identifying potential uncertainties in the instructions. Ambiguous statements trigger a GPT-4-based mechanism that generates clarifying questions, incorporating user responses for disambiguation. Also, the framework assesses and records user preferences for non-ambiguous instructions, enhancing future interactions. The last part of this process is the translation of disambiguated instructions into a robot motion plan using Linear Temporal Logic. This paper details the development of this framework and the evaluation of its performance in various test scenarios.
Abstract (translated)
本文提出了一种框架,可以解释人类包含时间元素的导航指令,并直接将自然语言指令翻译成机器人运动规划。该框架的核心是利用大型语言模型(LLMs)。为了提高LLMs在框架中的可靠性并改善用户体验,我们提出了方法来解决自然语言指令中的歧义,并捕获用户偏好。过程从歧义分类器开始,该分类器确定指令中的潜在不确定性。含糊不清的声明会触发基于GPT-4的机制,该机制生成澄清的问题,包括用户的回答用于消除歧义。此外,该框架评估并记录了非歧义指令的用户偏好,提高了未来交互。该过程的最后一部分是将解歧后的指令翻译成机器人运动规划,使用线性时间逻辑。本文详细介绍了该框架的开发以及在不同测试场景中的性能评估。
URL
https://arxiv.org/abs/2404.14547
