Layered architectures have been widely used in robot systems. The majority of them implement planning and execution functions in separate layers. However, there still lacks a straightforward way to transit high-level tasks in the planning layer to the low-level motor commands in the execution layer. In order to tackle this challenge, we propose a novel approach to ground the manipulator primitive tasks to robot low-level actions using large language models (LLMs). We designed a program-function-like prompt based on the task frame formalism. In this way, we enable LLMs to generate position/force set-points for hybrid control. Evaluations over several state-of-the-art LLMs are provided.

翻译：分层架构在机器人系统中得到了广泛应用。大多数架构将规划与执行功能分别部署在不同层级中，然而，从规划层的高层级任务过渡到执行层的低层级电机指令仍缺乏直接有效的途径。为应对这一挑战，我们提出了一种基于大型语言模型（LLMs）的新型方法，旨在将机械臂基本任务转化为机器人低层级动作。基于任务框架形式化方法，我们设计了类似程序函数的提示机制，从而能够引导LLMs生成用于混合控制的位置/力设定值。文中还提供了对多个先进LLMs的评估结果。