Symbolic task planning is a widely used approach to enforce robot autonomy due to its ease of understanding and deployment. However, symbolic task planning is difficult to scale in real-world when frequent re-planning is needed, for example, due to human-robot interactions or unforeseen events. Plan length and planning time can hinder the robot's efficiency and negatively affect the overall human-robot interaction's fluency. We present a framework, Teriyaki, designed to bridge the gap between symbolic task planning and machine learning approaches, by training Large Language Models (LLMs), namely GPT-3, into neurosymbolic task planners compatible with the Planning Domain Definition Language (PDDL). Potential benefits include: (i) better scalability in so far as the planning domain complexity increases, since LLMs' response time linearly scales with the combined length of the input and the output, instead of super-linearly as in the case of symbolic task planners, and (ii) the ability to synthesize a plan action-by-action instead of end-to-end, and to make each action available for execution as soon as it is generated, which in turn enables concurrent planning and execution. In the past year, significant efforts have been devoted by the research community to evaluate the overall cognitive abilities of LLMs, with alternate successes. Instead, with Teriyaki we aim to providing an overall planning performance comparable to traditional planners in specific planning domains, while leveraging LLMs capabilities in other metrics which are used to build a look-ahead predictive planning model. Preliminary results in selected domains show that our method can: (i) solve 95.5% of problems in a test data set of 1000 samples; (ii) produce plans up to 13.5% shorter than a traditional symbolic planner; (iii) reduce average overall waiting times for a plan availability by up to 61.4%.

翻译：符号任务规划因其易于理解和部署而成为提升机器人自主性的常用方法。然而，在实际应用场景中（例如因人类-机器人交互或突发事件需要频繁重新规划时），符号任务规划难以扩展。规划长度与规划时间可能限制机器人效率，并降低整体人机交互的流畅性。我们提出名为Teriyaki的框架，旨在通过将大语言模型（LLM，即GPT-3）训练为兼容规划领域定义语言（PDDL）的神经符号任务规划器，弥合符号任务规划与机器学习方法之间的鸿沟。潜在优势包括：（i）随着规划领域复杂度增加，可扩展性显著提升——LLM的响应时间随输入与输出总长度线性增长，而符号任务规划器呈超线性增长；（ii）能够逐动作而非端到端地合成规划，并在生成每个动作后即可执行，从而实现规划与执行的并行化。过去一年中，研究社区投入大量精力评估LLM的整体认知能力，结果喜忧参半。与之不同，Teriyaki旨在特定规划领域提供与传统规划器相当的整体规划性能，同时利用LLM在其他指标上的能力构建前瞻性预测规划模型。在选定领域的初步结果表明，我们的方法能够：（i）在包含1000个样本的测试数据集中解决95.5%的问题；（ii）生成比传统符号规划器短13.5%的规划；（iii）将规划可用性的平均等待时间降低61.4%。