Motivated by the substantial achievements observed in Large Language Models (LLMs) in the field of natural language processing, recent research has commenced investigations into the application of LLMs for complex, long-horizon sequential task planning challenges in robotics. LLMs are advantageous in offering the potential to enhance the generalizability as task-agnostic planners and facilitate flexible interaction between human instructors and planning systems. However, task plans generated by LLMs often lack feasibility and correctness. To address this challenge, we introduce ISR-LLM, a novel framework that improves LLM-based planning through an iterative self-refinement process. The framework operates through three sequential steps: preprocessing, planning, and iterative self-refinement. During preprocessing, an LLM translator is employed to convert natural language input into a Planning Domain Definition Language (PDDL) formulation. In the planning phase, an LLM planner formulates an initial plan, which is then assessed and refined in the iterative self-refinement step by using a validator. We examine the performance of ISR-LLM across three distinct planning domains. The results show that ISR-LLM is able to achieve markedly higher success rates in task accomplishments compared to state-of-the-art LLM-based planners. Moreover, it also preserves the broad applicability and generalizability of working with natural language instructions.

翻译：受大语言模型（LLM）在自然语言处理领域显著成就的启发，近期研究开始探索将LLM应用于机器人领域复杂长时域顺序任务规划挑战。LLM的优势在于，其作为任务无关规划器可提升泛化能力，并促进人类指导者与规划系统间的灵活交互。然而，LLM生成的任务规划往往缺乏可行性与正确性。为解决这一挑战，我们提出ISR-LLM——一种通过迭代自优化流程增强基于LLM规划能力的新型框架。该框架包含三个连续步骤：预处理、规划与迭代自优化。在预处理阶段，采用LLM翻译器将自然语言输入转换为规划领域定义语言（PDDL）表述；在规划阶段，LLM规划器生成初始方案；随后在迭代自优化步骤中，通过验证器对方案进行评估与优化。我们针对三个不同规划领域检验了ISR-LLM的性能。结果表明，与当前最先进的基于LLM的规划器相比，ISR-LLM在任务达成率上显著提升，同时保持了处理自然语言指令的广泛适用性与泛化能力。