Large Language Models (LLMs) are predominantly trained on imperative programming languages, and existing work on LLM-based robot programming reflects this: it focuses on goal-directed tasks expressed as plans. However, many robotic applications require not tasks but behaviors--open-ended, continuous patterns of sensory-motor engagement with no termination condition (e.g., greeting visitors, patrolling an area). Such behaviors are better served by declarative and reactive programming, on which LLMs have virtually no training. We investigate whether this gap can be bridged. Using Playful, a reactive scripting language, we show that an LLM-based coding agent can generate correct declarative and reactive programs for robotic behaviors. These programs are produced from under-specified natural language descriptions, given suitable context and a set of reusable programming guidelines. The guidelines encode design principles that redirect the agent from imperative defaults toward proper reactive patterns. Developed once by an expert, they generalize across robots and behaviors. Starting from only the generic robot operating system API, the agent jointly designs both the imperative component (Python modules for sensing, acting, and evaluating) and the declarative component (the reactive script that composes them into behavior logic). The declarative nature of reactive programming supports both incremental refinement through conversational turns and one-shot generation of complete behaviors.

翻译：暂无翻译