论文标题
PROG PROMPT：使用大语言模型生成位置的机器人任务计划
ProgPrompt: Generating Situated Robot Task Plans using Large Language Models
论文作者
论文摘要
任务计划可能需要定义有关机器人需要采取行动的世界的无数领域知识。为了改善这项工作，可以使用大型语言模型（LLMS）在任务计划期间为潜在的下一个动作评分，甚至直接生成动作序列，鉴于没有其他域信息的自然语言指令，也可以直接生成动作序列。但是，这种方法要么需要枚举所有可能的下一步评分，要么生成可能包含在当前上下文中给定机器人上不可能操作的自由形式文本。我们提出了一个程序化的LLM提示结构，该结构可以使计划生成在位置环境，机器人功能和任务之间的功能。我们的关键见解是提示LLM具有环境中可用操作和对象的类似程序的规格，以及可以执行的示例程序。我们通过消融实验提出了有关迅速结构和生成约束的具体建议，证明了虚拟屋家庭任务中最先进的成功率，并将我们的方法部署在桌面任务的物理机器人臂上。网站at progprompt.github.io
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 at progprompt.github.io
