  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.
