The Procedural Planning Abilities of Cloud-Based LLMS in Embodied Environments: A Meta-Analysis

Recent advancements in large language models (LLMs) have transformed natural language processing and extended their application to the challenging domain of procedural planning in embodied environments. In these settings, high-level goals must be decomposed into precise, sequential actions that can be executed by robots or simulated agents, a task complicated by dynamic real-world constraints and inherent ambiguity. We present a systematic meta-analysis of procedural planning methodologies, categorizing existing studies into four approaches. By leveraging the VirtualHome ActivityPrograms dataset, our analysis shows that while foundational methods remain competitive, novel techniques that incorporate feedback, symbolic reasoning, and external tools significantly enhance the executability and robustness of plans. We identify challenges in reasoning granularity, plan-world misalignment, and scalability, and propose directions toward unified, self-reflective planning frameworks.