  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.
