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:将高级别任务规划纳入现实世界机器人结构执行环路执行过程中的问题依然具有挑战性,因为传统象征性规划者的规划时间随着需要规划的符号数目的增多而变化。在本文件中,我们向Teriyaki介绍了一个培训大语言模型的框架,特别是现在众所周知的GPT-3模型,将其纳入与规划DDL 语言(PDDL)兼容的神经同步规划器。与象征性的方法不同,LLMS需要有一个培训过程。然而,他们的反应时间尺度与投入和产出的长度相结合。因此,LLLM的规划者有可能随着技术的成熟和更易于获取,在复杂的规划问题上带来重大的业绩收益。在这个初步工作中,我们的知识是首先使用LMS来进行机器人规划,我们(一) 概述了培训LMS作为PDDL解答器的方法。 (二) 为两个挑战性更短的PDDL领域创建符合PDL的PDL规划员, 并且(三) 测试与获得的规划者相关的规划时间和计划质量,同时将它们与95 PDDDR计划的平均域域域域域比标准。</s>