Task planning can require defining myriad domain knowledge about the world in which a robot needs to act. To ameliorate that effort, large language models (LLMs) can be used to score potential next actions during task planning, and even generate action sequences directly, given an instruction in natural language with no additional domain information. However, such methods either require enumerating all possible next steps for scoring, or generate free-form text that may contain actions not possible on a given robot in its current context. We present a programmatic LLM prompt structure that enables plan generation functional across situated environments, robot capabilities, and tasks. Our key insight is to prompt the LLM with program-like specifications of the available actions and objects in an environment, as well as with example programs that can be executed. We make concrete recommendations about prompt structure and generation constraints through ablation experiments, demonstrate state of the art success rates in VirtualHome household tasks, and deploy our method on a physical robot arm for tabletop tasks. Website and code at progprompt.github.io

任务规划可能需要定义有关机器人需要行动的世界的无数领域知识。为了改善这一工作，可以使用大型语言模型 (LLM) 在任务规划期间对潜在的下一步动作进行评分，甚至在没有额外领域信息的自然语言指令的情况下直接生成动作序列。然而，此类方法要么需要枚举所有可能的后续步骤进行评分，要么生成自由格式的文本，其中可能包含给定机器人在当前上下文中不可能执行的操作。我们提出了一种程序化的 LLM 提示结构，可以跨情境环境、机器人功能和任务生成计划。我们的主要见解是通过环境中可用操作和对象的类似程序的规范以及示例来提示法学硕士可以执行的程序。我们通过消融实验对提示结构和生成约束提出了具体建议，展示了 VirtualHome 家庭任务中最先进的成功率，并将我们的方法部署在用于桌面任务的物理机器人手臂上。网站和代码位于 progprompt.github.io