The problem of integrating high-level task planning in the execution loop of a real-world robot architecture remains challenging, as the planning times of traditional symbolic planners explode combinatorially with the number of symbols to plan upon. In this paper, we present Teriyaki, a framework for training Large Language Models (LLMs), and in particular the now well-known GPT-3 model, into neurosymbolic planners compatible with the Planning Domain Definition Language (PDDL). Unlike symbolic approaches, LLMs require a training process. However, their response time scales with the combined length of the input and the output. Hence, LLM-based planners can potentially provide significant performance gains on complex planning problems as the technology matures and becomes more accessible. In this preliminary work, which to our knowledge is the first using LLMs for planning in robotics, we (i) outline a methodology for training LLMs as PDDL solvers, (ii) generate PDDL-compliant planners for two challenging PDDL domains, and (iii) test the planning times and the plan quality associated with the obtained planners, while also comparing them to a state-of-the-art PDDL planner, namely Probe. Results confirm the viability of the approach, with Teriyaki-based planners being able to solve 95.5% of problems in a test data set of 1000 samples, and even generating plans up to 13.5% shorter on average than the employed traditional planner, depending on the domain.

翻译：将高层任务规划集成到真实机器人架构执行环路中的问题仍具挑战性，因为传统符号规划器的规划时间会随待规划符号数量呈组合式增长。本文提出Teriyaki框架，用于将大型语言模型（LLMs），特别是广为人知的GPT-3模型，训练为与规划域定义语言（PDDL）兼容的神经符号规划器。与符号方法不同，LLMs需要训练过程。然而，其响应时间随输入和输出组合长度变化。因此，随着技术成熟与普及，基于LLM的规划器有望在复杂规划问题上实现显著性能提升。在这项初步工作中（据我们所知，这是首次将LLMs用于机器人规划领域），我们（i）概述了训练LLMs作为PDDL求解器的方法论，（ii）为两个具有挑战性的PDDL域生成了PDDL兼容规划器，（iii）测试了所得规划器的规划时间与规划质量，同时将其与最先进的PDDL规划器Probe进行对比。结果验证了该方法的可行性：基于Teriyaki的规划器能够解决包含1000个样本测试数据集中95.5%的问题，甚至根据域的不同，平均生成比传统规划器短13.5%的规划方案。