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-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：分层架构已在机器人系统中广泛应用。大多数分层架构将规划与执行功能分别部署在不同层级中。然而，如何将规划层中的高层任务平滑过渡至执行层的底层电机指令仍缺乏直接有效的解决方案。为应对这一挑战，我们提出了一种创新方法，通过大语言模型（LLMs）将机械臂基元任务落地为机器人底层动作。基于任务框架形式化方法，我们设计了程序化提示模板，从而能够使大语言模型生成用于混合控制的位置/力设定点。本文还提供了对多种前沿大语言模型的评估结果。