Large Language Models for Task Planning in Embodied AI: A Survey
TL;DR
A structured taxonomy is presented that organizes existing work into three complementary paradigms that represent dominant architectural tendencies in current LLM-based embodied task planning research, and compares these paradigms along dimension of accuracy, robustness, scalability, efficiency, and sim-to-real transfer.
Abstract
Large language models (LLMs) have recently emerged as promising components for task planning in embodied artificial intelligence (AI), where agents must decompose high-level natural language instructions into executable action sequences under dynamic environments and physical constraints. Unlike purely text-based planning, embodied task planning requires grounding in object affordances, partial observability, and the gap between symbolic reasoning and low-level control execution. Classical planning methods, such as STRIPS, PDDL, and HTN, provide formal and interpretable frameworks, yet they struggle with unstructured real-world settings and open-ended instructions. This paper surveys LLM-based approaches to embodied task planning. We present a structured taxonomy that organizes existing work into three complementary paradigms: (1) hierarchical planning, where LLMs serve as high-level planners that decompose goals into subgoals; (2) closed-loop planning, where execution feedback and environmental state monitoring support replanning; and (3) end-to-end embodied planning frameworks, where multimodal LLMs and vision-language-action models integrate perception, language understanding, and action prediction within learned policies. vcThese categories are not strictly mutually exclusive, but rather represent dominant architectural tendencies in current LLM-based embodied task planning research.. We compare these paradigms along dimension of accuracy, robustness, scalability, efficiency, and sim-to-real transfer. The comparison suggests that while LLMs are effective for commonsense-driven task decomposition and feedback-based replanning, they remain limited in physical reasoning, real-time efficiency, and reliable low-level execution. Open challenges are further discussed, including granularity mismatch, physical commonsense deficits, safe replanning, and benchmark standardization, and outline future directions toward more reliable and physically grounded embodied planning.